{
  "meta": {
    "benchmark": "STRIDE",
    "split": "waymo",
    "source_dataset": "Waymo Open Dataset (validation)",
    "n_questions": 1200,
    "n_templates": 24,
    "n_scenes": 335,
    "counts_by_template": {
      "SC-1": 50,
      "SC-2": 50,
      "SC-3": 50,
      "SC-4": 50,
      "SP-2": 50,
      "SP-3": 50,
      "SP-3a": 50,
      "TE-1": 50,
      "TE-2": 50,
      "TE-4": 50,
      "TE-5": 50,
      "TM-2": 50,
      "TM-3": 50,
      "TM-5": 50,
      "TRJ-1": 50,
      "TRJ-10": 50,
      "TRJ-2": 50,
      "TRJ-3": 50,
      "TRJ-4": 50,
      "TRJ-5": 50,
      "TRJ-6": 50,
      "TRJ-7": 50,
      "TRJ-8": 50,
      "TRJ-9": 50
    },
    "counts_by_task_family": {
      "scenario-corner-case": 200,
      "space-perception": 150,
      "time-extrapolation": 200,
      "time-memory": 150,
      "trajectory-prediction": 500
    },
    "counts_by_format": {
      "MCQ": 900,
      "OEQ": 300
    },
    "image_paths_are_relative_to": "STRIDE_WAYMO_IMAGE_ROOT",
    "notes": [
      "Ground-truth answers included for open evaluation.",
      "image_paths are relative to Waymo val/images/.",
      "bbox_xyxy (when present) is the precomputed target box; eval overlays a red rectangle — users do not need to run SAM.",
      "candidate_trajectories (when present) are used to overlay TRJ ranking options on the query frame.",
      "Each question uses 5 chronological CAM_FRONT frames; query frame is last.",
      "candidate_trajectories_image (when present) are preprojected pixel polylines for TRJ ranking overlays — no Waymo calibration needed at eval."
    ],
    "packaged_at": "2026-09-29T01:02:52.158450+00:00"
  },
  "tasks": [
    {
      "group_id": "group_014",
      "frame_index": 90,
      "history_image_paths": [
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        "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-075_FRONT.jpg",
        "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-080_FRONT.jpg",
        "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-085_FRONT.jpg",
        "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-090_FRONT.jpg"
      ],
      "question": "Which option best describes the current scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "0426ef72af3ce42152d3559ee3bf2b9b:group_014_SC-1",
      "id": "SC-1",
      "ground_truth": "B",
      "bundle_frame_names": [
        "0426ef72af3ce42152d3559ee3bf2b9b-070",
        "0426ef72af3ce42152d3559ee3bf2b9b-075",
        "0426ef72af3ce42152d3559ee3bf2b9b-080",
        "0426ef72af3ce42152d3559ee3bf2b9b-085",
        "0426ef72af3ce42152d3559ee3bf2b9b-090"
      ],
      "choices": {
        "A": "highway",
        "B": "traffic intersection",
        "C": "construction zone",
        "D": "right turn"
      },
      "ground_truth_text": "traffic intersection",
      "frame_name": "0426ef72af3ce42152d3559ee3bf2b9b-090",
      "anchor_image_path": "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-090_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "0426ef72af3ce42152d3559ee3bf2b9b",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "0426ef72af3ce42152d3559ee3bf2b9b:group_014",
      "image_paths": [
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      ],
      "image_path": "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-090_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_014",
      "frame_index": 175,
      "history_image_paths": [
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      ],
      "question": "Which option best describes the current scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636:group_014_SC-1",
      "id": "SC-1",
      "ground_truth": "C",
      "bundle_frame_names": [
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        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-160",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-165",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-170",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-175"
      ],
      "choices": {
        "A": "construction zone",
        "B": "right turn",
        "C": "traffic intersection",
        "D": "highway"
      },
      "ground_truth_text": "Traffic intersection",
      "frame_name": "053cb7c6b8b71cc8ed1d9f3b2b5b8636-175",
      "anchor_image_path": "053cb7c6b8b71cc8ed1d9f3b2b5b8636/CAM_FRONT/053cb7c6b8b71cc8ed1d9f3b2b5b8636-175_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636:group_014",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 214,
      "history_image_paths": [
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      ],
      "question": "Which option best describes the current scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "05e1f9ef75f7e47d67f096e05d167dcb:group_013_SC-1",
      "id": "SC-1",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "05e1f9ef75f7e47d67f096e05d167dcb-199",
        "05e1f9ef75f7e47d67f096e05d167dcb-204",
        "05e1f9ef75f7e47d67f096e05d167dcb-209",
        "05e1f9ef75f7e47d67f096e05d167dcb-214"
      ],
      "choices": {
        "A": "pedestrian crossing",
        "B": "right turn",
        "C": "t-intersection",
        "D": "highway merge"
      },
      "ground_truth_text": "Pedestrian crossing",
      "frame_name": "05e1f9ef75f7e47d67f096e05d167dcb-214",
      "anchor_image_path": "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-214_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "05e1f9ef75f7e47d67f096e05d167dcb",
      "scenario_cluster": "Cyclist",
      "bundle_id": "05e1f9ef75f7e47d67f096e05d167dcb:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_014",
      "frame_index": 230,
      "history_image_paths": [
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      ],
      "question": "Which option best describes the current scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "0cdf4bedea5f52bfb54edea6cbdbb15e:group_014_SC-1",
      "id": "SC-1",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "0cdf4bedea5f52bfb54edea6cbdbb15e-215",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-220",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-225",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-230"
      ],
      "choices": {
        "A": "construction zone",
        "B": "traffic intersection",
        "C": "right turn",
        "D": "urban road"
      },
      "ground_truth_text": "Night road",
      "frame_name": "0cdf4bedea5f52bfb54edea6cbdbb15e-230",
      "anchor_image_path": "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-230_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "0cdf4bedea5f52bfb54edea6cbdbb15e",
      "scenario_cluster": "Intersection",
      "bundle_id": "0cdf4bedea5f52bfb54edea6cbdbb15e:group_014",
      "image_paths": [
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      "scoring_type": "mcq"
    },
    {
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      ],
      "question": "Which option best describes the current scene?",
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      "question_format": "MCQ",
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      "id": "SC-1",
      "ground_truth": "A",
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      ],
      "choices": {
        "A": "t-intersection",
        "B": "urban road",
        "C": "right turn",
        "D": "parking lot"
      },
      "ground_truth_text": "single lane",
      "frame_name": "0f71456c67c9eeff7b8cea4e979e0393-198",
      "anchor_image_path": "0f71456c67c9eeff7b8cea4e979e0393/CAM_FRONT/0f71456c67c9eeff7b8cea4e979e0393-198_FRONT.jpg",
      "task": "scenario-corner-case",
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    {
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      ],
      "question": "Which option best describes the current scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "0fff5ea6ebf1edce430191415bb02953:group_012_SC-1",
      "id": "SC-1",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "0fff5ea6ebf1edce430191415bb02953-193"
      ],
      "choices": {
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        "B": "highway merge",
        "C": "right turn",
        "D": "t-intersection"
      },
      "ground_truth_text": "Merging scenario",
      "frame_name": "0fff5ea6ebf1edce430191415bb02953-193",
      "anchor_image_path": "0fff5ea6ebf1edce430191415bb02953/CAM_FRONT/0fff5ea6ebf1edce430191415bb02953-193_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Cyclist",
      "bundle_id": "0fff5ea6ebf1edce430191415bb02953:group_012",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_014",
      "frame_index": 175,
      "history_image_paths": [
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "question_format": "MCQ",
      "question_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636:group_014_SC-2",
      "id": "SC-2",
      "ground_truth": "A",
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      "choices": {
        "A": "Crossing pedestrian or cyclist",
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        "C": "Road debris",
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        "E": "No clear immediate risk"
      },
      "ground_truth_text": "A car on the right side of the road is about to cut in front of the ego vehicle, which could lead to a collision if the ego vehicle does not react in time.",
      "frame_name": "053cb7c6b8b71cc8ed1d9f3b2b5b8636-175",
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      "task": "scenario-corner-case",
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "choices": {
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      },
      "ground_truth_text": "The cyclist is in the same lane as the ego vehicle's path of travel. A potential risk exists if the ego vehicle needs to change lanes or if the cyclist makes an unexpected maneuver, such as swerving or stopping.",
      "frame_name": "05e1f9ef75f7e47d67f096e05d167dcb-214",
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      "task": "scenario-corner-case",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_012",
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "id": "SC-2",
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      "choices": {
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        "E": "No clear immediate risk"
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      "ground_truth_text": "The primary risk is the potential for collision with the pedestrian crossing the street and the white van in the adjacent lane, especially as the red sedan executes its turn.",
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      "task": "scenario-corner-case",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_013",
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "ground_truth": "A",
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        "D": "Unclear lane boundary",
        "E": "No clear immediate risk"
      },
      "ground_truth_text": "A red SUV cutting into the ego vehicle's lane from the right, which could lead to a collision if the ego vehicle does not react in time.",
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      "anchor_image_path": "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-174_FRONT.jpg",
      "task": "scenario-corner-case",
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    {
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      "question": "What is the main risk source in this scene?",
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      "ground_truth_text": "The main risk source is the potential for a cut-in maneuver by a vehicle in the adjacent lane, which could lead to a collision or require a sudden deceleration. This is especially relevant at intersections where vehicles may attempt to turn or merge into the ego's path.",
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    {
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      "question": "What is the main risk source in this scene?",
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      "choices": {
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        "E": "No clear immediate risk"
      },
      "ground_truth_text": "A pedestrian walking in the middle of the road, which is an unexpected and dangerous situation for a vehicle to encounter.",
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      "task": "scenario-corner-case",
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      "scenario_cluster": "Pedestrian",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_012",
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "question_format": "MCQ",
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      "id": "SC-2",
      "ground_truth": "B",
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      "choices": {
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      },
      "ground_truth_text": "The cyclist is a vulnerable road user. The main risk is a potential collision if the ego vehicle does not maintain a safe distance or if the cyclist makes an unexpected maneuver, such as swerving or stopping.",
      "frame_name": "1c03d26f31f89c4e75d406bd2de496f8-133",
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      "task": "scenario-corner-case",
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    {
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      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "ground_truth_text": "The main risk is the poor visibility due to rain and the resulting reflections on the wet road, which can obscure other vehicles and road markings. The risk of hydroplaning or reduced traction is also present.",
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      "task": "scenario-corner-case",
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    {
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      ],
      "question": "What is the main risk source in this scene?",
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      "ground_truth_text": "The primary risk is the presence of the cyclist in the bike lane. While the cyclist is in a designated area, there is a potential for conflict if the cyclist were to swerve into the traffic lane or if the ego vehicle were to drift into the bike lane. Additionally, the cyclist's position near the intersection could pose a risk if they are not visible to other drivers.",
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    {
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      "ground_truth_text": "A large moving truck with an open back is stopped in the lane ahead, which could pose a risk if the vehicle is not properly aware of the cargo or if the truck moves unexpectedly. The presence of a stop sign and crosswalk requires the vehicle to be vigilant for pedestrians and other vehicles.",
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    {
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    {
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      "question": "What is the main risk source in this scene?",
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      "choices": {
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    {
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "38561ca659f7ba32383f3f1cad122546:group_012_SC-2",
      "id": "SC-2",
      "ground_truth": "E",
      "bundle_frame_names": [
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        "38561ca659f7ba32383f3f1cad122546-231",
        "38561ca659f7ba32383f3f1cad122546-236"
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      "choices": {
        "A": "Road debris",
        "B": "Cut-in vehicle",
        "C": "Crossing pedestrian or cyclist",
        "D": "Unclear lane boundary",
        "E": "No clear immediate risk"
      },
      "ground_truth_text": "The main risk is the potential for a collision with the parked car on the right at the intersection, as the ego vehicle is on a narrow road with limited visibility. Additionally, the presence of the stop sign indicates that the vehicle must yield to any cross traffic, which could be a source of conflict.",
      "frame_name": "38561ca659f7ba32383f3f1cad122546-236",
      "anchor_image_path": "38561ca659f7ba32383f3f1cad122546/CAM_FRONT/38561ca659f7ba32383f3f1cad122546-236_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "38561ca659f7ba32383f3f1cad122546",
      "scenario_cluster": "Intersection",
      "bundle_id": "38561ca659f7ba32383f3f1cad122546:group_012",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_014",
      "frame_index": 60,
      "history_image_paths": [
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "44ba4fa0579084ba64f738b927fcd280:group_014_SC-2",
      "id": "SC-2",
      "ground_truth": "E",
      "bundle_frame_names": [
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      "choices": {
        "A": "No clear immediate risk",
        "B": "Cut-in vehicle",
        "C": "Crossing pedestrian or cyclist",
        "D": "Unclear lane boundary",
        "E": "Road debris"
      },
      "ground_truth_text": "The primary risk is the potential for a collision with other vehicles, particularly when changing lanes or merging. The presence of construction on the side of the road also poses a risk if the vehicle were to drift into the work zone.",
      "frame_name": "44ba4fa0579084ba64f738b927fcd280-060",
      "anchor_image_path": "44ba4fa0579084ba64f738b927fcd280/CAM_FRONT/44ba4fa0579084ba64f738b927fcd280-060_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "44ba4fa0579084ba64f738b927fcd280",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "44ba4fa0579084ba64f738b927fcd280:group_014",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_014",
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      "history_image_paths": [
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "id": "SC-2",
      "ground_truth": "D",
      "bundle_frame_names": [
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        "E": "No clear immediate risk"
      },
      "ground_truth_text": "The cyclist is a vulnerable road user. The primary risk is a potential collision if the ego vehicle or other vehicles do not detect and yield to the cyclist, especially given the low-light conditions and the cyclist's position at the edge of the roadway.",
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      "task": "scenario-corner-case",
      "scene_id": "46933f5db465b1ed13dc48343129c8b5",
      "scenario_cluster": "Cyclist",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_013",
      "frame_index": 214,
      "history_image_paths": [
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      ],
      "question": "What is the main risk source in this scene?",
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      "question_id": "46eaaa7a82f4c3a80a134f672f905b7e:group_013_SC-2",
      "id": "SC-2",
      "ground_truth": "C",
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      "choices": {
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      "ground_truth_text": "The motorcyclist is a vulnerable road user. The risk is primarily from a potential collision if the ego vehicle fails to maintain a safe following distance or if the motorcyclist makes an unexpected maneuver.",
      "frame_name": "46eaaa7a82f4c3a80a134f672f905b7e-214",
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      "task": "scenario-corner-case",
      "scene_id": "46eaaa7a82f4c3a80a134f672f905b7e",
      "scenario_cluster": "Cyclist",
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    {
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      "question": "What is the main risk source in this scene?",
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      "ground_truth": "B",
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      "choices": {
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        "C": "Cut-in vehicle",
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      "ground_truth_text": "The primary risk is the potential for a collision with other vehicles at the intersection, especially if the traffic lights change or if other drivers do not obey the signals.",
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      "anchor_image_path": "4714de6823f14ec054f617bdcfcb83c1/CAM_FRONT/4714de6823f14ec054f617bdcfcb83c1-178_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Intersection",
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      "scoring_type": "mcq"
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    {
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      "question": "What is the main risk source in this scene?",
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      "id": "SC-2",
      "ground_truth": "E",
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      "choices": {
        "A": "Road debris",
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      "ground_truth_text": "Cut-ins from vehicles in adjacent lanes, especially the black SUV on the left and the white van on the right, which could suddenly change lanes and enter the ego vehicle's path.",
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      "anchor_image_path": "474a369cbdce69432665f10ee171ac55/CAM_FRONT/474a369cbdce69432665f10ee171ac55-043_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Cut-ins",
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    {
      "group_id": "group_032",
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      "question": "What is the main risk source in this scene?",
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      "choices": {
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        "E": "No clear immediate risk"
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      "ground_truth_text": "The intersection presents a risk of conflicting traffic, and the roadwork on the right introduces potential hazards from construction equipment and lane shifts.",
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      "task": "scenario-corner-case",
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      "scoring_type": "mcq"
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    {
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      "question": "What is the main risk source in this scene?",
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    {
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      "question": "What is the main risk source in this scene?",
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      "task": "scenario-corner-case",
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      "scoring_type": "mcq"
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    {
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      "question": "What is the main risk source in this scene?",
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      "ground_truth_text": "The low visibility at night, combined with the complex and potentially confusing layout of the industrial area, increases the risk of misjudging the path of travel or failing to see other vehicles or obstacles.",
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      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
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      "question_format": "MCQ",
      "question_id": "5f00a03431807e0808773563fe4b344c:group_012_SC-2",
      "id": "SC-2",
      "ground_truth": "A",
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      "choices": {
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        "C": "Road debris",
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        "E": "No clear immediate risk"
      },
      "ground_truth_text": "The cyclist on the sidewalk poses a potential risk, as they are in the path of the ego vehicle's potential right turn or lane change. Their presence requires the driver to be vigilant for any sudden movements into the traffic lane.",
      "frame_name": "5f00a03431807e0808773563fe4b344c-236",
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      "task": "scenario-corner-case",
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      "scenario_cluster": "Cyclist",
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      "scoring_type": "mcq"
    },
    {
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      ],
      "question": "What is the main risk source in this scene?",
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      },
      "ground_truth_text": "The primary risk is the sun glare, which significantly impairs the driver's vision, particularly on the right side of the road. This reduced visibility increases the risk of not detecting foreign objects, debris, or other vehicles in the blind spots created by the glare.",
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      "task": "scenario-corner-case",
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      "scoring_type": "mcq"
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    {
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      ],
      "question": "What is the main risk source in this scene?",
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      "ground_truth": "E",
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      },
      "ground_truth_text": "The primary risk is the potential for a collision with other vehicles at the intersection, especially if the traffic light changes unexpectedly or if other drivers do not obey the signal. The risk is higher at night due to reduced visibility.",
      "frame_name": "6321452e4fe9b1bf7883fbba3c521ba7-098",
      "anchor_image_path": "6321452e4fe9b1bf7883fbba3c521ba7/CAM_FRONT/6321452e4fe9b1bf7883fbba3c521ba7-098_FRONT.jpg",
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    {
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      ],
      "question": "What is the main risk source in this scene?",
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      },
      "ground_truth_text": "The main risk is the high density of traffic, which includes a mix of vehicles, including larger special vehicles. The presence of parked cars on the side of the road and the potential for pedestrians to cross the street create a complex environment that requires constant vigilance.",
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      "task": "scenario-corner-case",
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    {
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      },
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    {
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      "question": "What is the main risk source in this scene?",
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      "choices": {
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      "ground_truth_text": "The primary risk is the potential for a rear-end collision if the vehicle does not stop in time for the red light, or if the following vehicle does not react appropriately to the deceleration.",
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    {
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      "question": "What is the main risk source in this scene?",
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      "choices": {
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      "ground_truth_text": "The foreign object debris poses a potential risk to the vehicle's safety, as it could fall and cause damage or create a hazard on the road.",
      "frame_name": "6da6d10808633154702c238b489579f2-105",
      "anchor_image_path": "6da6d10808633154702c238b489579f2/CAM_FRONT/6da6d10808633154702c238b489579f2-105_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "6da6d10808633154702c238b489579f2",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "6da6d10808633154702c238b489579f2:group_034",
      "image_paths": [
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_012",
      "frame_index": 193,
      "history_image_paths": [
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        "70a5a1a0881dc89625434d15b7919660/CAM_FRONT/70a5a1a0881dc89625434d15b7919660-193_FRONT.jpg"
      ],
      "question": "What is the main risk source in this scene?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "70a5a1a0881dc89625434d15b7919660:group_012_SC-2",
      "id": "SC-2",
      "ground_truth": "E",
      "bundle_frame_names": [
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        "70a5a1a0881dc89625434d15b7919660-178",
        "70a5a1a0881dc89625434d15b7919660-183",
        "70a5a1a0881dc89625434d15b7919660-188",
        "70a5a1a0881dc89625434d15b7919660-193"
      ],
      "choices": {
        "A": "Road debris",
        "B": "Cut-in vehicle",
        "C": "No clear immediate risk",
        "D": "Unclear lane boundary",
        "E": "Crossing pedestrian or cyclist"
      },
      "ground_truth_text": "Pedestrians crossing the street or walking on the sidewalk, which could lead to a collision if not properly detected and reacted to.",
      "frame_name": "70a5a1a0881dc89625434d15b7919660-193",
      "anchor_image_path": "70a5a1a0881dc89625434d15b7919660/CAM_FRONT/70a5a1a0881dc89625434d15b7919660-193_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "70a5a1a0881dc89625434d15b7919660",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "70a5a1a0881dc89625434d15b7919660:group_012",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_033",
      "frame_index": 194,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "0075c28f1f1a68c4c1c8dcf37958046b:group_033_SC-3",
      "id": "SC-3",
      "ground_truth": "B",
      "bundle_frame_names": [
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        "0075c28f1f1a68c4c1c8dcf37958046b-179",
        "0075c28f1f1a68c4c1c8dcf37958046b-184",
        "0075c28f1f1a68c4c1c8dcf37958046b-189",
        "0075c28f1f1a68c4c1c8dcf37958046b-194"
      ],
      "choices": {
        "A": "Slow down",
        "B": "Keep lane carefully",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "Proceed with caution, maintain lane position, and be prepared to stop if any debris is observed on the road surface.",
      "frame_name": "0075c28f1f1a68c4c1c8dcf37958046b-194",
      "anchor_image_path": "0075c28f1f1a68c4c1c8dcf37958046b/CAM_FRONT/0075c28f1f1a68c4c1c8dcf37958046b-194_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "0075c28f1f1a68c4c1c8dcf37958046b",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "0075c28f1f1a68c4c1c8dcf37958046b:group_033",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_014",
      "frame_index": 175,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636:group_014_SC-3",
      "id": "SC-3",
      "ground_truth": "E",
      "bundle_frame_names": [
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        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-160",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-165",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-170",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-175"
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      "choices": {
        "A": "Keep lane carefully",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Speed up",
        "E": "Stop and wait"
      },
      "ground_truth_text": "The ego vehicle should slow down and prepare to stop to avoid a potential collision with the car that is about to cut in.",
      "frame_name": "053cb7c6b8b71cc8ed1d9f3b2b5b8636-175",
      "anchor_image_path": "053cb7c6b8b71cc8ed1d9f3b2b5b8636/CAM_FRONT/053cb7c6b8b71cc8ed1d9f3b2b5b8636-175_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636:group_014",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 214,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "05e1f9ef75f7e47d67f096e05d167dcb:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "05e1f9ef75f7e47d67f096e05d167dcb-199",
        "05e1f9ef75f7e47d67f096e05d167dcb-204",
        "05e1f9ef75f7e47d67f096e05d167dcb-209",
        "05e1f9ef75f7e47d67f096e05d167dcb-214"
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      "choices": {
        "A": "Stop and wait",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Keep lane carefully",
        "E": "Speed up"
      },
      "ground_truth_text": "Maintain a safe following distance and be prepared to adjust speed or change lanes if necessary to ensure a safe clearance from the cyclist.",
      "frame_name": "05e1f9ef75f7e47d67f096e05d167dcb-214",
      "anchor_image_path": "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-214_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "05e1f9ef75f7e47d67f096e05d167dcb",
      "scenario_cluster": "Cyclist",
      "bundle_id": "05e1f9ef75f7e47d67f096e05d167dcb:group_013",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 204,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "06a6ff41e238ac25ca0ce315ab895329:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "B",
      "bundle_frame_names": [
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        "06a6ff41e238ac25ca0ce315ab895329-189",
        "06a6ff41e238ac25ca0ce315ab895329-194",
        "06a6ff41e238ac25ca0ce315ab895329-199",
        "06a6ff41e238ac25ca0ce315ab895329-204"
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      "choices": {
        "A": "Keep lane carefully",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "The vehicle should proceed with caution, maintaining a safe distance from the debris and being prepared to stop or swerve if necessary. The driver should also be aware of the potential for the debris to be unstable or to obstruct the view of other hazards.",
      "frame_name": "06a6ff41e238ac25ca0ce315ab895329-204",
      "anchor_image_path": "06a6ff41e238ac25ca0ce315ab895329/CAM_FRONT/06a6ff41e238ac25ca0ce315ab895329-204_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "06a6ff41e238ac25ca0ce315ab895329",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "06a6ff41e238ac25ca0ce315ab895329:group_013",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_032",
      "frame_index": 93,
      "history_image_paths": [
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      "question": "What is the preferred action for the ego vehicle?",
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      "question_id": "08c980dc4d5291e938e3aa785fced231:group_032_SC-3",
      "id": "SC-3",
      "ground_truth": "E",
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      "choices": {
        "A": "Keep lane carefully",
        "B": "Speed up",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Slow down"
      },
      "ground_truth_text": "Proceed with caution through the construction zone, maintaining a safe distance from the barriers and being vigilant for any unexpected debris or changes in the road surface.",
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      "anchor_image_path": "08c980dc4d5291e938e3aa785fced231/CAM_FRONT/08c980dc4d5291e938e3aa785fced231-093_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Foreign Object Debris",
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    },
    {
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      ],
      "question": "What is the preferred action for the ego vehicle?",
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      "task": "scenario-corner-case",
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    {
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        "1ed2ba95b036f4a0d22347e9f6cc987a-134"
      ],
      "choices": {
        "A": "Keep lane carefully",
        "B": "Change lane",
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        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should maintain a safe following distance and stay within its lane. The driver should be aware of the cyclist's presence and be prepared to react if the cyclist changes position or if other vehicles or pedestrians enter the scene. The vehicle should continue to drive cautiously and follow traffic rules.",
      "frame_name": "1ed2ba95b036f4a0d22347e9f6cc987a-134",
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      "task": "scenario-corner-case",
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      "scenario_cluster": "Cyclist",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
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      "question_format": "MCQ",
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      "choices": {
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      "task": "scenario-corner-case",
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    {
      "group_id": "group_013",
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      ],
      "question": "What is the preferred action for the ego vehicle?",
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    {
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      "question": "What is the preferred action for the ego vehicle?",
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      "ground_truth_text": "The vehicle should maintain a steady speed and be prepared to stop if necessary. The driver should be vigilant for pedestrians crossing the street and ensure that all turns are made safely, respecting the traffic signals and the right-of-way of other vehicles.",
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      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
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      "choices": {
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      "ground_truth_text": "The vehicle should continue to drive straight, maintaining its lane and reducing speed as it approaches the intersection. It must be prepared to stop at the stop sign and yield to any pedestrians or oncoming traffic.",
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    {
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      "question": "What is the preferred action for the ego vehicle?",
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      "ground_truth_text": "The preferred action is to maintain a safe following distance and ensure that the lane change is executed smoothly and safely, with proper signaling and checking of blind spots.",
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    {
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 13,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "2f7a008f2b7d7a2c897db23af3a0b0a7:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "D",
      "bundle_frame_names": [
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        "2f7a008f2b7d7a2c897db23af3a0b0a7-010",
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        "2f7a008f2b7d7a2c897db23af3a0b0a7-013"
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      "choices": {
        "A": "Keep lane carefully",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "Stop at the intersection and wait for the traffic signal to turn green before executing the left turn.",
      "frame_name": "2f7a008f2b7d7a2c897db23af3a0b0a7-013",
      "anchor_image_path": "2f7a008f2b7d7a2c897db23af3a0b0a7/CAM_FRONT/2f7a008f2b7d7a2c897db23af3a0b0a7-013_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "2f7a008f2b7d7a2c897db23af3a0b0a7",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "2f7a008f2b7d7a2c897db23af3a0b0a7:group_013",
      "image_path": "2f7a008f2b7d7a2c897db23af3a0b0a7/CAM_FRONT/2f7a008f2b7d7a2c897db23af3a0b0a7-013_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 64,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "36bcd46c350c6037c1b9f817636455db:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "E",
      "bundle_frame_names": [
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        "36bcd46c350c6037c1b9f817636455db-049",
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      "choices": {
        "A": "Speed up",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Keep lane carefully"
      },
      "ground_truth_text": "Proceed with caution through the intersection and into the alley, maintaining a safe speed and being prepared to stop if necessary.",
      "frame_name": "36bcd46c350c6037c1b9f817636455db-064",
      "anchor_image_path": "36bcd46c350c6037c1b9f817636455db/CAM_FRONT/36bcd46c350c6037c1b9f817636455db-064_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "36bcd46c350c6037c1b9f817636455db",
      "scenario_cluster": "Intersection",
      "bundle_id": "36bcd46c350c6037c1b9f817636455db:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_034",
      "frame_index": 200,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "398703cb3d52176faf9da2a53aac4b69:group_034_SC-3",
      "id": "SC-3",
      "ground_truth": "D",
      "bundle_frame_names": [
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        "398703cb3d52176faf9da2a53aac4b69-195",
        "398703cb3d52176faf9da2a53aac4b69-200"
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      "choices": {
        "A": "Stop and wait",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Keep lane carefully",
        "E": "Speed up"
      },
      "ground_truth_text": "Maintain speed and lane position, be prepared to brake or steer to avoid a collision if the cut-in vehicle does not yield.",
      "frame_name": "398703cb3d52176faf9da2a53aac4b69-200",
      "anchor_image_path": "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-200_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "398703cb3d52176faf9da2a53aac4b69",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "398703cb3d52176faf9da2a53aac4b69:group_034",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 149,
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "3c87e8251965c3ffbc95a320e49d20f6:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "C",
      "bundle_frame_names": [
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      "choices": {
        "A": "Change lane",
        "B": "Slow down",
        "C": "Keep lane carefully",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should maintain a safe following distance and a clear lane, ensuring it has a clear view of the cyclist and is prepared to react to any sudden movements. The vehicle should be cautious of the cyclist's position and potential for changing lanes or stopping.",
      "frame_name": "3c87e8251965c3ffbc95a320e49d20f6-149",
      "anchor_image_path": "3c87e8251965c3ffbc95a320e49d20f6/CAM_FRONT/3c87e8251965c3ffbc95a320e49d20f6-149_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "3c87e8251965c3ffbc95a320e49d20f6",
      "scenario_cluster": "Cyclist",
      "bundle_id": "3c87e8251965c3ffbc95a320e49d20f6:group_013",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 54,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
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      "question_format": "MCQ",
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      "id": "SC-3",
      "ground_truth": "D",
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      "choices": {
        "A": "Stop and wait",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Keep lane carefully",
        "E": "Speed up"
      },
      "ground_truth_text": "The vehicle should maintain a safe following distance, stay aware of the traffic in adjacent lanes, and be prepared to change lanes or adjust speed as needed. It should also be vigilant for pedestrians and other road users, especially near the school.",
      "frame_name": "4114fc22dfe5b28572e503ee4ed21636-054",
      "anchor_image_path": "4114fc22dfe5b28572e503ee4ed21636/CAM_FRONT/4114fc22dfe5b28572e503ee4ed21636-054_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Multi-Lane Maneuvers",
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    {
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      "question": "What is the preferred action for the ego vehicle?",
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      "id": "SC-3",
      "ground_truth": "B",
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        "B": "Stop and wait",
        "C": "Change lane",
        "D": "Slow down",
        "E": "Speed up"
      },
      "ground_truth_text": "Proceed with caution through the intersection, maintaining a safe speed and being vigilant for other vehicles and potential hazards related to the construction zone.",
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      "anchor_image_path": "4224f4dc217ac4500cd9d47a392658dc/CAM_FRONT/4224f4dc217ac4500cd9d47a392658dc-033_FRONT.jpg",
      "task": "scenario-corner-case",
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    {
      "group_id": "group_014",
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        "C": "Change lane",
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        "E": "Speed up"
      },
      "ground_truth_text": "The driver should reduce speed, increase following distance, and use windshield wipers to clear the glass. If visibility remains poor, the driver should consider pulling over to a safe location until conditions improve.",
      "frame_name": "425497055114d44b2506e57c5a06fd19-030",
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      "task": "scenario-corner-case",
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      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "425497055114d44b2506e57c5a06fd19:group_014",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_012",
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      "question": "What is the preferred action for the ego vehicle?",
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      "choices": {
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      "ground_truth_text": "Maintain a safe following distance and be prepared to brake or steer to avoid a collision if a vehicle cuts in unexpectedly.",
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    {
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      "question": "What is the preferred action for the ego vehicle?",
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      "choices": {
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      "ground_truth_text": "Proceed through the intersection with caution, then navigate the construction zone by maintaining a safe distance from the work area and following the lane markings.",
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      "task": "scenario-corner-case",
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    {
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      "question": "What is the preferred action for the ego vehicle?",
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      "ground_truth_text": "Proceed with caution, maintain a safe following distance, and be prepared to slow down or stop as required by the temporary traffic signals and road conditions.",
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    {
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    {
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    {
      "group_id": "group_014",
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "B",
      "bundle_frame_names": [
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        "5a71aee52b4764ceb8c0bef7f3e3a6c9-179",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-184",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-189",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-194"
      ],
      "choices": {
        "A": "Slow down",
        "B": "Keep lane carefully",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "The preferred action is to maintain a safe following distance and be prepared to adjust speed or change lanes if necessary. The vehicle should remain vigilant and monitor the movements of surrounding vehicles, especially those in the adjacent lanes, to ensure safe navigation through the traffic.",
      "frame_name": "5a71aee52b4764ceb8c0bef7f3e3a6c9-194",
      "anchor_image_path": "5a71aee52b4764ceb8c0bef7f3e3a6c9/CAM_FRONT/5a71aee52b4764ceb8c0bef7f3e3a6c9-194_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_032",
      "frame_index": 203,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "5e9e4deffd43523dedb6156f04a230b1:group_032_SC-3",
      "id": "SC-3",
      "ground_truth": "E",
      "bundle_frame_names": [
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      "choices": {
        "A": "Keep lane carefully",
        "B": "Speed up",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Slow down"
      },
      "ground_truth_text": "Proceed with caution, maintain a safe speed, and be prepared to stop or maneuver around the debris to avoid a potential hazard.",
      "frame_name": "5e9e4deffd43523dedb6156f04a230b1-203",
      "anchor_image_path": "5e9e4deffd43523dedb6156f04a230b1/CAM_FRONT/5e9e4deffd43523dedb6156f04a230b1-203_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "5e9e4deffd43523dedb6156f04a230b1:group_032",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_012",
      "frame_index": 236,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "5f00a03431807e0808773563fe4b344c:group_012_SC-3",
      "id": "SC-3",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "5f00a03431807e0808773563fe4b344c-221",
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      ],
      "choices": {
        "A": "Keep lane carefully",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should maintain a safe following distance, be prepared to slow down or stop if the cyclist moves into the road, and ensure a clear view of the sidewalk before any maneuver to the right.",
      "frame_name": "5f00a03431807e0808773563fe4b344c-236",
      "anchor_image_path": "5f00a03431807e0808773563fe4b344c/CAM_FRONT/5f00a03431807e0808773563fe4b344c-236_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "5f00a03431807e0808773563fe4b344c",
      "scenario_cluster": "Cyclist",
      "bundle_id": "5f00a03431807e0808773563fe4b344c:group_012",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_012",
      "frame_index": 138,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "605a964cc30b61fedf8a41bdf130f505:group_012_SC-3",
      "id": "SC-3",
      "ground_truth": "D",
      "bundle_frame_names": [
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      "choices": {
        "A": "Keep lane carefully",
        "B": "Stop and wait",
        "C": "Change lane",
        "D": "Slow down",
        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should slow down and be prepared to stop to allow the cyclist to safely cross the intersection.",
      "frame_name": "605a964cc30b61fedf8a41bdf130f505-138",
      "anchor_image_path": "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-138_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Cyclist",
      "bundle_id": "605a964cc30b61fedf8a41bdf130f505:group_012",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 114,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "606924060f4d8b8d78a4241f44af9d24:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "E",
      "bundle_frame_names": [
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      "choices": {
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        "B": "Slow down",
        "C": "Change lane",
        "D": "Speed up",
        "E": "Stop and wait"
      },
      "ground_truth_text": "The ego vehicle should maintain a safe following distance and be prepared to adjust its speed or lane position in response to the maneuvers of the black sedan and the white utility truck. It should also be vigilant for any sudden changes in the behavior of these vehicles, ensuring a safe and smooth passage through the intersection.",
      "frame_name": "606924060f4d8b8d78a4241f44af9d24-114",
      "anchor_image_path": "606924060f4d8b8d78a4241f44af9d24/CAM_FRONT/606924060f4d8b8d78a4241f44af9d24-114_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "606924060f4d8b8d78a4241f44af9d24",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "606924060f4d8b8d78a4241f44af9d24:group_013",
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      "scoring_type": "mcq"
    },
    {
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      "frame_index": 209,
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
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      "question_format": "MCQ",
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      "ground_truth": "A",
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      "choices": {
        "A": "Keep lane carefully",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should maintain a safe distance and be prepared to stop or yield if necessary, especially as the traffic light for the cross-traffic turns green. The driver should be vigilant for the turning minivan and other vehicles in the intersection.",
      "frame_name": "622e0fc649b4053ecfd9f35962a55107-209",
      "anchor_image_path": "622e0fc649b4053ecfd9f35962a55107/CAM_FRONT/622e0fc649b4053ecfd9f35962a55107-209_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "622e0fc649b4053ecfd9f35962a55107:group_013",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_012",
      "frame_index": 178,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
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      "question_format": "MCQ",
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      "id": "SC-3",
      "ground_truth": "E",
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      "choices": {
        "A": "Keep lane carefully",
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        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Slow down"
      },
      "ground_truth_text": "The ego vehicle should slow down and stop to yield to the pedestrian crossing the road.",
      "frame_name": "62502fb60c74f34c9edf8ad3c7461776-178",
      "anchor_image_path": "62502fb60c74f34c9edf8ad3c7461776/CAM_FRONT/62502fb60c74f34c9edf8ad3c7461776-178_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "62502fb60c74f34c9edf8ad3c7461776",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "62502fb60c74f34c9edf8ad3c7461776:group_012",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 204,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "62b67666791605074bbbbe60dad987f2:group_013_SC-3",
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      "choices": {
        "A": "Stop and wait",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Keep lane carefully",
        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should proceed with caution, maintaining a safe speed and being vigilant for any pedestrians or cyclists who may cross the street or enter the travel lane.",
      "frame_name": "62b67666791605074bbbbe60dad987f2-204",
      "anchor_image_path": "62b67666791605074bbbbe60dad987f2/CAM_FRONT/62b67666791605074bbbbe60dad987f2-204_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "62b67666791605074bbbbe60dad987f2",
      "scenario_cluster": "Cyclist",
      "bundle_id": "62b67666791605074bbbbe60dad987f2:group_013",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_012",
      "frame_index": 98,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "6321452e4fe9b1bf7883fbba3c521ba7:group_012_SC-3",
      "id": "SC-3",
      "ground_truth": "A",
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      "choices": {
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      "ground_truth_text": "The ego vehicle should stop at the red light and proceed only when the light turns green, ensuring that the intersection is clear of other vehicles before moving forward.",
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      "anchor_image_path": "6321452e4fe9b1bf7883fbba3c521ba7/CAM_FRONT/6321452e4fe9b1bf7883fbba3c521ba7-098_FRONT.jpg",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_012",
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      "question": "What is the preferred action for the ego vehicle?",
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      "ground_truth_text": "The ego vehicle should maintain a safe following distance from the vehicle ahead and be prepared to adjust speed or change lanes if necessary. The driver should be aware of the special vehicles and anticipate their movements, such as potential lane changes or stops.",
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      "scoring_type": "mcq"
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    {
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    {
      "group_id": "group_032",
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      ],
      "question": "What is the preferred action for the ego vehicle?",
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      "anchor_image_path": "64c3f8ce8c99d46f5ef849049cdc1141/CAM_FRONT/64c3f8ce8c99d46f5ef849049cdc1141-193_FRONT.jpg",
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      "question": "What is the preferred action for the ego vehicle?",
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        "6c96c45f414e3570f93d29c9c2117d80/CAM_FRONT/6c96c45f414e3570f93d29c9c2117d80-188_FRONT.jpg",
        "6c96c45f414e3570f93d29c9c2117d80/CAM_FRONT/6c96c45f414e3570f93d29c9c2117d80-193_FRONT.jpg",
        "6c96c45f414e3570f93d29c9c2117d80/CAM_FRONT/6c96c45f414e3570f93d29c9c2117d80-198_FRONT.jpg"
      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "6c96c45f414e3570f93d29c9c2117d80:group_032_SC-3",
      "id": "SC-3",
      "ground_truth": "C",
      "bundle_frame_names": [
        "6c96c45f414e3570f93d29c9c2117d80-178",
        "6c96c45f414e3570f93d29c9c2117d80-183",
        "6c96c45f414e3570f93d29c9c2117d80-188",
        "6c96c45f414e3570f93d29c9c2117d80-193",
        "6c96c45f414e3570f93d29c9c2117d80-198"
      ],
      "choices": {
        "A": "Keep lane carefully",
        "B": "Slow down",
        "C": "Stop and wait",
        "D": "Change lane",
        "E": "Speed up"
      },
      "ground_truth_text": "Proceed through the intersection with caution, maintaining a safe speed and being vigilant for any potential hazards, such as construction zones or pedestrians, that may be present but not immediately visible.",
      "frame_name": "6c96c45f414e3570f93d29c9c2117d80-198",
      "anchor_image_path": "6c96c45f414e3570f93d29c9c2117d80/CAM_FRONT/6c96c45f414e3570f93d29c9c2117d80-198_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "6c96c45f414e3570f93d29c9c2117d80",
      "scenario_cluster": "Construction",
      "bundle_id": "6c96c45f414e3570f93d29c9c2117d80:group_032",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_033",
      "frame_index": 194,
      "history_image_paths": [
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        "6d2556548b36274db8d073ef6c240ace/CAM_FRONT/6d2556548b36274db8d073ef6c240ace-179_FRONT.jpg",
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        "6d2556548b36274db8d073ef6c240ace/CAM_FRONT/6d2556548b36274db8d073ef6c240ace-194_FRONT.jpg"
      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "6d2556548b36274db8d073ef6c240ace:group_033_SC-3",
      "id": "SC-3",
      "ground_truth": "B",
      "bundle_frame_names": [
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        "6d2556548b36274db8d073ef6c240ace-179",
        "6d2556548b36274db8d073ef6c240ace-184",
        "6d2556548b36274db8d073ef6c240ace-189",
        "6d2556548b36274db8d073ef6c240ace-194"
      ],
      "choices": {
        "A": "Slow down",
        "B": "Keep lane carefully",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "The vehicle should proceed with caution, maintaining a steady speed and staying within the designated lane. It should be prepared to slow down or stop if necessary, and remain vigilant for any changes in the construction setup or the presence of workers.",
      "frame_name": "6d2556548b36274db8d073ef6c240ace-194",
      "anchor_image_path": "6d2556548b36274db8d073ef6c240ace/CAM_FRONT/6d2556548b36274db8d073ef6c240ace-194_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "6d2556548b36274db8d073ef6c240ace",
      "scenario_cluster": "Construction",
      "bundle_id": "6d2556548b36274db8d073ef6c240ace:group_033",
      "image_paths": [
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      ],
      "image_path": "6d2556548b36274db8d073ef6c240ace/CAM_FRONT/6d2556548b36274db8d073ef6c240ace-194_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_034",
      "frame_index": 160,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "6e73c2f589c607a008e4fd91932d36de:group_034_SC-3",
      "id": "SC-3",
      "ground_truth": "C",
      "bundle_frame_names": [
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        "6e73c2f589c607a008e4fd91932d36de-145",
        "6e73c2f589c607a008e4fd91932d36de-150",
        "6e73c2f589c607a008e4fd91932d36de-155",
        "6e73c2f589c607a008e4fd91932d36de-160"
      ],
      "choices": {
        "A": "Change lane",
        "B": "Slow down",
        "C": "Keep lane carefully",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "Proceed with caution, maintain a safe speed, and stay within the designated lane. Be prepared to slow down or stop if necessary due to the construction zone.",
      "frame_name": "6e73c2f589c607a008e4fd91932d36de-160",
      "anchor_image_path": "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-160_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "6e73c2f589c607a008e4fd91932d36de",
      "scenario_cluster": "Construction",
      "bundle_id": "6e73c2f589c607a008e4fd91932d36de:group_034",
      "image_paths": [
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      ],
      "image_path": "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-160_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_012",
      "frame_index": 223,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "6eea094dcdafb7b521a5b4f210e98556:group_012_SC-3",
      "id": "SC-3",
      "ground_truth": "D",
      "bundle_frame_names": [
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        "6eea094dcdafb7b521a5b4f210e98556-208",
        "6eea094dcdafb7b521a5b4f210e98556-213",
        "6eea094dcdafb7b521a5b4f210e98556-218",
        "6eea094dcdafb7b521a5b4f210e98556-223"
      ],
      "choices": {
        "A": "Stop and wait",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Keep lane carefully",
        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should slow down and proceed with extreme caution through the construction zone. It must be prepared to stop if necessary, maintain a safe following distance, and be vigilant for any signals or movements from the workers.",
      "frame_name": "6eea094dcdafb7b521a5b4f210e98556-223",
      "anchor_image_path": "6eea094dcdafb7b521a5b4f210e98556/CAM_FRONT/6eea094dcdafb7b521a5b4f210e98556-223_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "6eea094dcdafb7b521a5b4f210e98556",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "6eea094dcdafb7b521a5b4f210e98556:group_012",
      "image_paths": [
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      ],
      "image_path": "6eea094dcdafb7b521a5b4f210e98556/CAM_FRONT/6eea094dcdafb7b521a5b4f210e98556-223_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_012",
      "frame_index": 193,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "70a5a1a0881dc89625434d15b7919660:group_012_SC-3",
      "id": "SC-3",
      "ground_truth": "B",
      "bundle_frame_names": [
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        "70a5a1a0881dc89625434d15b7919660-178",
        "70a5a1a0881dc89625434d15b7919660-183",
        "70a5a1a0881dc89625434d15b7919660-188",
        "70a5a1a0881dc89625434d15b7919660-193"
      ],
      "choices": {
        "A": "Keep lane carefully",
        "B": "Slow down",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "Maintain a safe speed, be vigilant for pedestrians, and be prepared to stop if a pedestrian enters the crosswalk or the path of the vehicle.",
      "frame_name": "70a5a1a0881dc89625434d15b7919660-193",
      "anchor_image_path": "70a5a1a0881dc89625434d15b7919660/CAM_FRONT/70a5a1a0881dc89625434d15b7919660-193_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "70a5a1a0881dc89625434d15b7919660",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "70a5a1a0881dc89625434d15b7919660:group_012",
      "image_paths": [
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      ],
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_013",
      "frame_index": 234,
      "history_image_paths": [
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      ],
      "question": "What is the preferred action for the ego vehicle?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "710f3838dd1c3647ec4487d18e28fd05:group_013_SC-3",
      "id": "SC-3",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "710f3838dd1c3647ec4487d18e28fd05-219",
        "710f3838dd1c3647ec4487d18e28fd05-224",
        "710f3838dd1c3647ec4487d18e28fd05-229",
        "710f3838dd1c3647ec4487d18e28fd05-234"
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      "choices": {
        "A": "Slow down",
        "B": "Keep lane carefully",
        "C": "Change lane",
        "D": "Stop and wait",
        "E": "Speed up"
      },
      "ground_truth_text": "The ego vehicle should maintain a safe following distance and be prepared to slow down or stop if the cyclist changes direction or if other vehicles in the lane ahead slow down. The driver should also be aware of the cyclist's position and anticipate their movements.",
      "frame_name": "710f3838dd1c3647ec4487d18e28fd05-234",
      "anchor_image_path": "710f3838dd1c3647ec4487d18e28fd05/CAM_FRONT/710f3838dd1c3647ec4487d18e28fd05-234_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "710f3838dd1c3647ec4487d18e28fd05",
      "scenario_cluster": "Cyclist",
      "bundle_id": "710f3838dd1c3647ec4487d18e28fd05:group_013",
      "image_paths": [
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      ],
      "image_path": "710f3838dd1c3647ec4487d18e28fd05/CAM_FRONT/710f3838dd1c3647ec4487d18e28fd05-234_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_033",
      "frame_index": 194,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "0075c28f1f1a68c4c1c8dcf37958046b:group_033_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a multi-lane road with streetlights. The ego vehicle is driving straight, and the road is mostly clear. On the right side of the road, a white pickup truck is parked. The scene is dark, with the primary light sources being streetlights and the vehicle's headlights. The road markings are visible, including a crosswalk and lane dividers. The main risk is Foreign Object Debris The preferred action is to proceed with caution, maintain lane position, and be prepared to stop if any debris is observed on the road surface.",
      "bundle_frame_names": [
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        "0075c28f1f1a68c4c1c8dcf37958046b-179",
        "0075c28f1f1a68c4c1c8dcf37958046b-184",
        "0075c28f1f1a68c4c1c8dcf37958046b-189",
        "0075c28f1f1a68c4c1c8dcf37958046b-194"
      ],
      "ground_truth_text": "A nighttime driving scene on a multi-lane road with streetlights. The ego vehicle is driving straight, and the road is mostly clear. On the right side of the road, a white pickup truck is parked. The scene is dark, with the primary light sources being streetlights and the vehicle's headlights. The road markings are visible, including a crosswalk and lane dividers. The main risk is Foreign Object Debris The preferred action is to proceed with caution, maintain lane position, and be prepared to stop if any debris is observed on the road surface.",
      "frame_name": "0075c28f1f1a68c4c1c8dcf37958046b-194",
      "anchor_image_path": "0075c28f1f1a68c4c1c8dcf37958046b/CAM_FRONT/0075c28f1f1a68c4c1c8dcf37958046b-194_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "0075c28f1f1a68c4c1c8dcf37958046b",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "0075c28f1f1a68c4c1c8dcf37958046b:group_033",
      "image_paths": [
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      ],
      "image_path": "0075c28f1f1a68c4c1c8dcf37958046b/CAM_FRONT/0075c28f1f1a68c4c1c8dcf37958046b-194_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_014",
      "frame_index": 90,
      "history_image_paths": [
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        "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-075_FRONT.jpg",
        "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-080_FRONT.jpg",
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        "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-090_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "0426ef72af3ce42152d3559ee3bf2b9b:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "The scene is a continuous forward drive through a residential neighborhood on an overcast day. The road is wet, suggesting recent rain. The vehicle starts on a wider, multi-lane road and then turns onto a narrower, two-lane street lined with parked cars and houses. In the distance, a tall city building is visible. The annotation 'Special Vehicles' is relevant as the scene contains numerous parked cars, which are the primary objects of interest. The main risk is The primary risk is the presence of parked vehicles on both sides of the road, which reduces the available space for maneuvering and could obscure the view of other vehicles or pedestrians. The wet road surface also increases the risk of hydroplaning or reduced traction. The preferred action is to the vehicle should proceed with caution, maintaining a safe speed and a consistent distance from the vehicle ahead. The driver should be vigilant for any sudden movements from parked cars, such as doors opening, or for pedestrians crossing the street.",
      "bundle_frame_names": [
        "0426ef72af3ce42152d3559ee3bf2b9b-070",
        "0426ef72af3ce42152d3559ee3bf2b9b-075",
        "0426ef72af3ce42152d3559ee3bf2b9b-080",
        "0426ef72af3ce42152d3559ee3bf2b9b-085",
        "0426ef72af3ce42152d3559ee3bf2b9b-090"
      ],
      "ground_truth_text": "The scene is a continuous forward drive through a residential neighborhood on an overcast day. The road is wet, suggesting recent rain. The vehicle starts on a wider, multi-lane road and then turns onto a narrower, two-lane street lined with parked cars and houses. In the distance, a tall city building is visible. The annotation 'Special Vehicles' is relevant as the scene contains numerous parked cars, which are the primary objects of interest. The main risk is The primary risk is the presence of parked vehicles on both sides of the road, which reduces the available space for maneuvering and could obscure the view of other vehicles or pedestrians. The wet road surface also increases the risk of hydroplaning or reduced traction. The preferred action is to the vehicle should proceed with caution, maintaining a safe speed and a consistent distance from the vehicle ahead. The driver should be vigilant for any sudden movements from parked cars, such as doors opening, or for pedestrians crossing the street.",
      "frame_name": "0426ef72af3ce42152d3559ee3bf2b9b-090",
      "anchor_image_path": "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-090_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "0426ef72af3ce42152d3559ee3bf2b9b",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "0426ef72af3ce42152d3559ee3bf2b9b:group_014",
      "image_paths": [
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      ],
      "image_path": "0426ef72af3ce42152d3559ee3bf2b9b/CAM_FRONT/0426ef72af3ce42152d3559ee3bf2b9b-090_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_033",
      "frame_index": 89,
      "history_image_paths": [
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        "04fcff6d002a8cc76666a30f7bde20b2/CAM_FRONT/04fcff6d002a8cc76666a30f7bde20b2-089_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "04fcff6d002a8cc76666a30f7bde20b2:group_033_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a multi-lane road approaching an intersection. The ego vehicle is moving forward, with traffic lights ahead showing green. The intersection is marked by a street sign for 'Thomas St' and is flanked by construction barriers and orange traffic cones. A few other vehicles are visible in the distance, and the road is illuminated by streetlights. The main risk is The intersection presents a risk due to the presence of construction barriers and cones, which may obstruct the view or limit the available lane width. Additionally, the presence of other vehicles, including a car turning left, requires careful monitoring. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being prepared to yield to any vehicles or pedestrians that may be present.",
      "bundle_frame_names": [
        "04fcff6d002a8cc76666a30f7bde20b2-069",
        "04fcff6d002a8cc76666a30f7bde20b2-074",
        "04fcff6d002a8cc76666a30f7bde20b2-079",
        "04fcff6d002a8cc76666a30f7bde20b2-084",
        "04fcff6d002a8cc76666a30f7bde20b2-089"
      ],
      "ground_truth_text": "A nighttime driving scene on a multi-lane road approaching an intersection. The ego vehicle is moving forward, with traffic lights ahead showing green. The intersection is marked by a street sign for 'Thomas St' and is flanked by construction barriers and orange traffic cones. A few other vehicles are visible in the distance, and the road is illuminated by streetlights. The main risk is The intersection presents a risk due to the presence of construction barriers and cones, which may obstruct the view or limit the available lane width. Additionally, the presence of other vehicles, including a car turning left, requires careful monitoring. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being prepared to yield to any vehicles or pedestrians that may be present.",
      "frame_name": "04fcff6d002a8cc76666a30f7bde20b2-089",
      "anchor_image_path": "04fcff6d002a8cc76666a30f7bde20b2/CAM_FRONT/04fcff6d002a8cc76666a30f7bde20b2-089_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "04fcff6d002a8cc76666a30f7bde20b2",
      "scenario_cluster": "Intersection",
      "bundle_id": "04fcff6d002a8cc76666a30f7bde20b2:group_033",
      "image_paths": [
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      ],
      "image_path": "04fcff6d002a8cc76666a30f7bde20b2/CAM_FRONT/04fcff6d002a8cc76666a30f7bde20b2-089_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_014",
      "frame_index": 175,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "A forward-facing view of a city street with tall buildings on both sides. The street is relatively clear with a few parked cars and some traffic. A pedestrian is seen crossing the street in the foreground. The scene is captured during the day under bright, clear skies. The main risk is A car on the right side of the road is about to cut in front of the ego vehicle, which could lead to a collision if the ego vehicle does not react in time. The preferred action is to the ego vehicle should slow down and prepare to stop to avoid a potential collision with the car that is about to cut in.",
      "bundle_frame_names": [
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-155",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-160",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-165",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-170",
        "053cb7c6b8b71cc8ed1d9f3b2b5b8636-175"
      ],
      "ground_truth_text": "A forward-facing view of a city street with tall buildings on both sides. The street is relatively clear with a few parked cars and some traffic. A pedestrian is seen crossing the street in the foreground. The scene is captured during the day under bright, clear skies. The main risk is A car on the right side of the road is about to cut in front of the ego vehicle, which could lead to a collision if the ego vehicle does not react in time. The preferred action is to the ego vehicle should slow down and prepare to stop to avoid a potential collision with the car that is about to cut in.",
      "frame_name": "053cb7c6b8b71cc8ed1d9f3b2b5b8636-175",
      "anchor_image_path": "053cb7c6b8b71cc8ed1d9f3b2b5b8636/CAM_FRONT/053cb7c6b8b71cc8ed1d9f3b2b5b8636-175_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "053cb7c6b8b71cc8ed1d9f3b2b5b8636:group_014",
      "image_paths": [
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      ],
      "image_path": "053cb7c6b8b71cc8ed1d9f3b2b5b8636/CAM_FRONT/053cb7c6b8b71cc8ed1d9f3b2b5b8636-175_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 214,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "05e1f9ef75f7e47d67f096e05d167dcb:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A wide, urban street scene on a partly cloudy day, with a multi-lane road, a crosswalk, and a large building in the distance. The scene is active with pedestrians crossing the street, cars driving, and a public transit bus. The most prominent feature is a cyclist riding in the rightmost lane, moving away from the camera, which is the focus of the annotation. The main risk is The cyclist is in the same lane as the ego vehicle's path of travel. A potential risk exists if the ego vehicle needs to change lanes or if the cyclist makes an unexpected maneuver, such as swerving or stopping. The preferred action is to maintain a safe following distance and be prepared to adjust speed or change lanes if necessary to ensure a safe clearance from the cyclist.",
      "bundle_frame_names": [
        "05e1f9ef75f7e47d67f096e05d167dcb-194",
        "05e1f9ef75f7e47d67f096e05d167dcb-199",
        "05e1f9ef75f7e47d67f096e05d167dcb-204",
        "05e1f9ef75f7e47d67f096e05d167dcb-209",
        "05e1f9ef75f7e47d67f096e05d167dcb-214"
      ],
      "ground_truth_text": "A wide, urban street scene on a partly cloudy day, with a multi-lane road, a crosswalk, and a large building in the distance. The scene is active with pedestrians crossing the street, cars driving, and a public transit bus. The most prominent feature is a cyclist riding in the rightmost lane, moving away from the camera, which is the focus of the annotation. The main risk is The cyclist is in the same lane as the ego vehicle's path of travel. A potential risk exists if the ego vehicle needs to change lanes or if the cyclist makes an unexpected maneuver, such as swerving or stopping. The preferred action is to maintain a safe following distance and be prepared to adjust speed or change lanes if necessary to ensure a safe clearance from the cyclist.",
      "frame_name": "05e1f9ef75f7e47d67f096e05d167dcb-214",
      "anchor_image_path": "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-214_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "05e1f9ef75f7e47d67f096e05d167dcb",
      "scenario_cluster": "Cyclist",
      "bundle_id": "05e1f9ef75f7e47d67f096e05d167dcb:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 194,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "086adac6586a693811e6e0901532bc3c:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a city street, starting on a wide, multi-lane road with modern buildings and palm trees. The scene transitions into a construction zone with orange barriers and a narrow, single-lane path. The vehicle continues through the construction area and emerges onto a wider street with traffic lights and other vehicles, with a large building on the right and a mix of commercial and residential buildings on the left. The main risk is The construction zone presents a significant risk due to the narrow path, potential for unexpected movements from workers, and the presence of heavy machinery. The vehicle must maintain a safe speed and be prepared to stop if necessary. The preferred action is to the vehicle should slow down as it approaches the construction zone, maintain a safe distance from the barriers, and be prepared to stop if a worker or vehicle enters the path. The driver should also be aware of any changes in the road layout and follow any temporary traffic signals or signs.",
      "bundle_frame_names": [
        "086adac6586a693811e6e0901532bc3c-174",
        "086adac6586a693811e6e0901532bc3c-179",
        "086adac6586a693811e6e0901532bc3c-184",
        "086adac6586a693811e6e0901532bc3c-189",
        "086adac6586a693811e6e0901532bc3c-194"
      ],
      "ground_truth_text": "A vehicle drives through a city street, starting on a wide, multi-lane road with modern buildings and palm trees. The scene transitions into a construction zone with orange barriers and a narrow, single-lane path. The vehicle continues through the construction area and emerges onto a wider street with traffic lights and other vehicles, with a large building on the right and a mix of commercial and residential buildings on the left. The main risk is The construction zone presents a significant risk due to the narrow path, potential for unexpected movements from workers, and the presence of heavy machinery. The vehicle must maintain a safe speed and be prepared to stop if necessary. The preferred action is to the vehicle should slow down as it approaches the construction zone, maintain a safe distance from the barriers, and be prepared to stop if a worker or vehicle enters the path. The driver should also be aware of any changes in the road layout and follow any temporary traffic signals or signs.",
      "frame_name": "086adac6586a693811e6e0901532bc3c-194",
      "anchor_image_path": "086adac6586a693811e6e0901532bc3c/CAM_FRONT/086adac6586a693811e6e0901532bc3c-194_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "086adac6586a693811e6e0901532bc3c",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "086adac6586a693811e6e0901532bc3c:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_032",
      "frame_index": 93,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
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      "question_format": "OEQ",
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      "id": "SC-4",
      "ground_truth": "A nighttime urban driving scene on a wet road, with the ego vehicle navigating a construction zone. The left side of the road is blocked by a long construction barrier with scaffolding and bright lights. The road surface is visibly wet, reflecting the city lights, and the camera lens is covered in raindrops, creating a bokeh effect. The vehicle proceeds through the construction zone and then onto a wider street with parked cars on the left. The main risk is Foreign Object Debris The preferred action is to proceed with caution through the construction zone, maintaining a safe distance from the barriers and being vigilant for any unexpected debris or changes in the road surface.",
      "bundle_frame_names": [
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      ],
      "ground_truth_text": "A nighttime urban driving scene on a wet road, with the ego vehicle navigating a construction zone. The left side of the road is blocked by a long construction barrier with scaffolding and bright lights. The road surface is visibly wet, reflecting the city lights, and the camera lens is covered in raindrops, creating a bokeh effect. The vehicle proceeds through the construction zone and then onto a wider street with parked cars on the left. The main risk is Foreign Object Debris The preferred action is to proceed with caution through the construction zone, maintaining a safe distance from the barriers and being vigilant for any unexpected debris or changes in the road surface.",
      "frame_name": "08c980dc4d5291e938e3aa785fced231-093",
      "anchor_image_path": "08c980dc4d5291e938e3aa785fced231/CAM_FRONT/08c980dc4d5291e938e3aa785fced231-093_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Foreign Object Debris",
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      "scoring_type": "open_oeq_text"
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    {
      "group_id": "group_014",
      "frame_index": 230,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "0cdf4bedea5f52bfb54edea6cbdbb15e:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene where the ego vehicle approaches and navigates through an intersection. The vehicle travels under a series of overpasses and tunnels, with the road curving slightly to the right. The environment is urban, with streetlights, utility poles, and buildings visible. A pedestrian is seen walking on the sidewalk, and a few parked cars are present. The scene is characterized by low visibility due to the darkness, with artificial lighting providing the primary illumination. The main risk is The main risk is the low visibility at night, which can obscure other vehicles, pedestrians, and road markings. The presence of a pedestrian on the sidewalk and the potential for other vehicles to enter the intersection from side streets pose a significant risk. The preferred action is to the ego vehicle should proceed with caution, maintaining a safe speed and being prepared to stop if necessary. The driver should be vigilant for any unexpected movements from pedestrians or other vehicles, especially as the vehicle approaches the intersection and navigates the curve.",
      "bundle_frame_names": [
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        "0cdf4bedea5f52bfb54edea6cbdbb15e-215",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-220",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-225",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-230"
      ],
      "ground_truth_text": "A nighttime driving scene where the ego vehicle approaches and navigates through an intersection. The vehicle travels under a series of overpasses and tunnels, with the road curving slightly to the right. The environment is urban, with streetlights, utility poles, and buildings visible. A pedestrian is seen walking on the sidewalk, and a few parked cars are present. The scene is characterized by low visibility due to the darkness, with artificial lighting providing the primary illumination. The main risk is The main risk is the low visibility at night, which can obscure other vehicles, pedestrians, and road markings. The presence of a pedestrian on the sidewalk and the potential for other vehicles to enter the intersection from side streets pose a significant risk. The preferred action is to the ego vehicle should proceed with caution, maintaining a safe speed and being prepared to stop if necessary. The driver should be vigilant for any unexpected movements from pedestrians or other vehicles, especially as the vehicle approaches the intersection and navigates the curve.",
      "frame_name": "0cdf4bedea5f52bfb54edea6cbdbb15e-230",
      "anchor_image_path": "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-230_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "0cdf4bedea5f52bfb54edea6cbdbb15e",
      "scenario_cluster": "Intersection",
      "bundle_id": "0cdf4bedea5f52bfb54edea6cbdbb15e:group_014",
      "image_paths": [
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 173,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "0f09faf7f1cd29a7478b2c034c9045d2:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "The ego vehicle is driving under a large elevated structure on a multi-lane urban road, approaching a traffic intersection with visible lane markings and distant cross-traffic. The main risk is Vehicles from side streets or cross-traffic may enter the intersection, creating potential conflicts with the ego vehicle's path. The preferred action is to proceed with caution, monitor intersection activity, and yield when necessary.",
      "bundle_frame_names": [
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        "0f09faf7f1cd29a7478b2c034c9045d2-173"
      ],
      "ground_truth_text": "The ego vehicle is driving under a large elevated structure on a multi-lane urban road, approaching a traffic intersection with visible lane markings and distant cross-traffic. The main risk is Vehicles from side streets or cross-traffic may enter the intersection, creating potential conflicts with the ego vehicle's path. The preferred action is to proceed with caution, monitor intersection activity, and yield when necessary.",
      "frame_name": "0f09faf7f1cd29a7478b2c034c9045d2-173",
      "anchor_image_path": "0f09faf7f1cd29a7478b2c034c9045d2/CAM_FRONT/0f09faf7f1cd29a7478b2c034c9045d2-173_FRONT.jpg",
      "task": "scenario-corner-case",
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 28,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "11d68b183960928432c0ab7af24ac86d:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A clear, sunny day scene of a vehicle driving through an urban intersection. The vehicle approaches and crosses a multi-lane road with traffic lights, which are red. On the right, there is a gas station and a construction zone marked by orange barriers. The vehicle then proceeds straight down the road, which is lined with buildings, trees, and ongoing construction, with workers and equipment visible. The main risk is The primary risk is the construction zone on the right side of the road, which includes workers, equipment, and temporary barriers. The vehicle must navigate carefully to avoid any potential hazards from the construction activity. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being vigilant for any workers or equipment in the construction zone. Ensure to follow traffic signals and be prepared to stop if necessary.",
      "bundle_frame_names": [
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      ],
      "ground_truth_text": "A clear, sunny day scene of a vehicle driving through an urban intersection. The vehicle approaches and crosses a multi-lane road with traffic lights, which are red. On the right, there is a gas station and a construction zone marked by orange barriers. The vehicle then proceeds straight down the road, which is lined with buildings, trees, and ongoing construction, with workers and equipment visible. The main risk is The primary risk is the construction zone on the right side of the road, which includes workers, equipment, and temporary barriers. The vehicle must navigate carefully to avoid any potential hazards from the construction activity. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being vigilant for any workers or equipment in the construction zone. Ensure to follow traffic signals and be prepared to stop if necessary.",
      "frame_name": "11d68b183960928432c0ab7af24ac86d-028",
      "anchor_image_path": "11d68b183960928432c0ab7af24ac86d/CAM_FRONT/11d68b183960928432c0ab7af24ac86d-028_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Intersection",
      "bundle_id": "11d68b183960928432c0ab7af24ac86d:group_012",
      "image_paths": [
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    },
    {
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      "frame_index": 53,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "12a6bf38f129fd612291f2fe25113cad:group_032_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime urban scene at an intersection. A cyclist crosses the intersection from right to left, followed by a blue car making a right turn. The scene is illuminated by streetlights, with buildings and traffic visible in the background. The main risk is A cyclist crossing the intersection and a car making a right turn, both of which are potential collision risks for the ego vehicle. The preferred action is to proceed through the intersection with caution, maintaining awareness of the cyclist and the turning vehicle.",
      "bundle_frame_names": [
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      ],
      "ground_truth_text": "A nighttime urban scene at an intersection. A cyclist crosses the intersection from right to left, followed by a blue car making a right turn. The scene is illuminated by streetlights, with buildings and traffic visible in the background. The main risk is A cyclist crossing the intersection and a car making a right turn, both of which are potential collision risks for the ego vehicle. The preferred action is to proceed through the intersection with caution, maintaining awareness of the cyclist and the turning vehicle.",
      "frame_name": "12a6bf38f129fd612291f2fe25113cad-053",
      "anchor_image_path": "12a6bf38f129fd612291f2fe25113cad/CAM_FRONT/12a6bf38f129fd612291f2fe25113cad-053_FRONT.jpg",
      "task": "scenario-corner-case",
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      "scenario_cluster": "Intersection",
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    {
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      "frame_index": 200,
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
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      "question_format": "OEQ",
      "question_id": "14f0b68cf50bad87e90a490a70109cad:group_034_SC-4",
      "id": "SC-4",
      "ground_truth": "A clear day in a city with a long stretch of road undergoing construction. The scene features a multi-lane road with traffic cones and barriers lining the right side, indicating a work zone. A tall building under construction is visible on the right, with scaffolding and a protective facade. Palm trees line the street, and other buildings are visible in the background. The road is mostly clear of traffic, with a few cars parked or driving slowly. The main risk is The construction zone presents a risk of unexpected movements from workers or equipment, and the narrow lane may require the ego vehicle to slow down or change lanes. The preferred action is to the ego vehicle should proceed with caution, maintain a safe speed, and be prepared to stop or change lanes if necessary due to the construction zone.",
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        "14f0b68cf50bad87e90a490a70109cad-200"
      ],
      "ground_truth_text": "A clear day in a city with a long stretch of road undergoing construction. The scene features a multi-lane road with traffic cones and barriers lining the right side, indicating a work zone. A tall building under construction is visible on the right, with scaffolding and a protective facade. Palm trees line the street, and other buildings are visible in the background. The road is mostly clear of traffic, with a few cars parked or driving slowly. The main risk is The construction zone presents a risk of unexpected movements from workers or equipment, and the narrow lane may require the ego vehicle to slow down or change lanes. The preferred action is to the ego vehicle should proceed with caution, maintain a safe speed, and be prepared to stop or change lanes if necessary due to the construction zone.",
      "frame_name": "14f0b68cf50bad87e90a490a70109cad-200",
      "anchor_image_path": "14f0b68cf50bad87e90a490a70109cad/CAM_FRONT/14f0b68cf50bad87e90a490a70109cad-200_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "14f0b68cf50bad87e90a490a70109cad",
      "scenario_cluster": "Construction",
      "bundle_id": "14f0b68cf50bad87e90a490a70109cad:group_034",
      "image_paths": [
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      ],
      "image_path": "14f0b68cf50bad87e90a490a70109cad/CAM_FRONT/14f0b68cf50bad87e90a490a70109cad-200_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 198,
      "history_image_paths": [
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        "155c466015696f7d5b11cf838422f5a9/CAM_FRONT/155c466015696f7d5b11cf838422f5a9-198_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "155c466015696f7d5b11cf838422f5a9:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A clear, sunny day on a multi-lane urban street with buildings on both sides. The scene begins with a construction zone on the right, which clears up as the vehicle proceeds. The street is lined with parked cars, and a few pedestrians and vehicles are visible. A distinctive tower is visible in the distance. The vehicle approaches and crosses a major intersection with a crosswalk. The main risk is The primary risk is the cyclist in the right lane. The vehicle must maintain a safe distance and be prepared for the cyclist to change lanes, stop, or make a turn. The presence of a construction zone and parked cars on the right also creates a narrow and potentially hazardous environment. The preferred action is to the vehicle should continue forward at a controlled speed, maintaining a safe lateral distance from the cyclist. The driver should be vigilant and ready to react to any sudden movements from the cyclist or other road users.",
      "bundle_frame_names": [
        "155c466015696f7d5b11cf838422f5a9-178",
        "155c466015696f7d5b11cf838422f5a9-183",
        "155c466015696f7d5b11cf838422f5a9-188",
        "155c466015696f7d5b11cf838422f5a9-193",
        "155c466015696f7d5b11cf838422f5a9-198"
      ],
      "ground_truth_text": "A clear, sunny day on a multi-lane urban street with buildings on both sides. The scene begins with a construction zone on the right, which clears up as the vehicle proceeds. The street is lined with parked cars, and a few pedestrians and vehicles are visible. A distinctive tower is visible in the distance. The vehicle approaches and crosses a major intersection with a crosswalk. The main risk is The primary risk is the cyclist in the right lane. The vehicle must maintain a safe distance and be prepared for the cyclist to change lanes, stop, or make a turn. The presence of a construction zone and parked cars on the right also creates a narrow and potentially hazardous environment. The preferred action is to the vehicle should continue forward at a controlled speed, maintaining a safe lateral distance from the cyclist. The driver should be vigilant and ready to react to any sudden movements from the cyclist or other road users.",
      "frame_name": "155c466015696f7d5b11cf838422f5a9-198",
      "anchor_image_path": "155c466015696f7d5b11cf838422f5a9/CAM_FRONT/155c466015696f7d5b11cf838422f5a9-198_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "155c466015696f7d5b11cf838422f5a9",
      "scenario_cluster": "Cyclist",
      "bundle_id": "155c466015696f7d5b11cf838422f5a9:group_012",
      "image_paths": [
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      ],
      "image_path": "155c466015696f7d5b11cf838422f5a9/CAM_FRONT/155c466015696f7d5b11cf838422f5a9-198_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 123,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "15ae1d98c512c1d6e8e1b71c57da8a97:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a city street with ongoing road construction. The scene features multi-story buildings, traffic cones, and construction barriers. The vehicle approaches and crosses a major intersection with a crosswalk and traffic lights. The main risk is The main risk is the construction zone on the right, which narrows the lane and may have workers or equipment present. The vehicle must also be cautious of other vehicles and pedestrians at the intersection. The preferred action is to proceed through the intersection at a controlled speed, maintaining awareness of the construction zone and other road users. The vehicle should be prepared to stop if the traffic light changes or if a pedestrian crosses.",
      "bundle_frame_names": [
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        "15ae1d98c512c1d6e8e1b71c57da8a97-108",
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        "15ae1d98c512c1d6e8e1b71c57da8a97-118",
        "15ae1d98c512c1d6e8e1b71c57da8a97-123"
      ],
      "ground_truth_text": "A vehicle drives through a city street with ongoing road construction. The scene features multi-story buildings, traffic cones, and construction barriers. The vehicle approaches and crosses a major intersection with a crosswalk and traffic lights. The main risk is The main risk is the construction zone on the right, which narrows the lane and may have workers or equipment present. The vehicle must also be cautious of other vehicles and pedestrians at the intersection. The preferred action is to proceed through the intersection at a controlled speed, maintaining awareness of the construction zone and other road users. The vehicle should be prepared to stop if the traffic light changes or if a pedestrian crosses.",
      "frame_name": "15ae1d98c512c1d6e8e1b71c57da8a97-123",
      "anchor_image_path": "15ae1d98c512c1d6e8e1b71c57da8a97/CAM_FRONT/15ae1d98c512c1d6e8e1b71c57da8a97-123_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "15ae1d98c512c1d6e8e1b71c57da8a97",
      "scenario_cluster": "Intersection",
      "bundle_id": "15ae1d98c512c1d6e8e1b71c57da8a97:group_012",
      "image_paths": [
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      "image_path": "15ae1d98c512c1d6e8e1b71c57da8a97/CAM_FRONT/15ae1d98c512c1d6e8e1b71c57da8a97-123_FRONT.jpg",
      "scoring_type": "open_oeq_text"
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    {
      "group_id": "group_014",
      "frame_index": 180,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "15fd18190eab4e2b20c45153a7781017:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle is driving through a city intersection on a sunny day. The traffic light is initially red, and the vehicle stops. After the light turns green, the vehicle proceeds through the intersection and continues down the road. A large orange and white special vehicle, likely a utility or service truck, is visible in the distance, traveling in the same direction as the ego vehicle. The scene includes other cars, a pedestrian on the sidewalk, and various urban elements like buildings, trees, and a large billboard. The main risk is The special vehicle is in the same lane as the ego vehicle, and its actions (e.g., stopping, turning, or changing speed) could pose a risk. The presence of a pedestrian on the sidewalk also requires attention. The preferred action is to proceed through the intersection and maintain a safe following distance from the special vehicle ahead.",
      "bundle_frame_names": [
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        "15fd18190eab4e2b20c45153a7781017-165",
        "15fd18190eab4e2b20c45153a7781017-170",
        "15fd18190eab4e2b20c45153a7781017-175",
        "15fd18190eab4e2b20c45153a7781017-180"
      ],
      "ground_truth_text": "A vehicle is driving through a city intersection on a sunny day. The traffic light is initially red, and the vehicle stops. After the light turns green, the vehicle proceeds through the intersection and continues down the road. A large orange and white special vehicle, likely a utility or service truck, is visible in the distance, traveling in the same direction as the ego vehicle. The scene includes other cars, a pedestrian on the sidewalk, and various urban elements like buildings, trees, and a large billboard. The main risk is The special vehicle is in the same lane as the ego vehicle, and its actions (e.g., stopping, turning, or changing speed) could pose a risk. The presence of a pedestrian on the sidewalk also requires attention. The preferred action is to proceed through the intersection and maintain a safe following distance from the special vehicle ahead.",
      "frame_name": "15fd18190eab4e2b20c45153a7781017-180",
      "anchor_image_path": "15fd18190eab4e2b20c45153a7781017/CAM_FRONT/15fd18190eab4e2b20c45153a7781017-180_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "15fd18190eab4e2b20c45153a7781017",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "15fd18190eab4e2b20c45153a7781017:group_014",
      "image_paths": [
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_014",
      "frame_index": 175,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "1bd3a510a00a6764f2ca0c08d2635c92:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "A longitudinal view of a city street undergoing construction. The scene begins with a wide shot of a crosswalk and a stop sign, then pans left to reveal a line of construction equipment, including excavators, parked on the side of the road. The road is partially blocked by construction materials and traffic cones. As the camera moves forward, a pedestrian is seen walking in the middle of the road, and a cyclist is visible further down the street. The street is lined with parked cars and residential buildings, with bare trees indicating a winter or early spring season. The main risk is A pedestrian walking in the middle of the road, which is an unexpected and dangerous situation for a vehicle to encounter. The preferred action is to the ego vehicle should slow down and prepare to stop, maintaining a safe distance from the pedestrian to avoid a collision.",
      "bundle_frame_names": [
        "1bd3a510a00a6764f2ca0c08d2635c92-155",
        "1bd3a510a00a6764f2ca0c08d2635c92-160",
        "1bd3a510a00a6764f2ca0c08d2635c92-165",
        "1bd3a510a00a6764f2ca0c08d2635c92-170",
        "1bd3a510a00a6764f2ca0c08d2635c92-175"
      ],
      "ground_truth_text": "A longitudinal view of a city street undergoing construction. The scene begins with a wide shot of a crosswalk and a stop sign, then pans left to reveal a line of construction equipment, including excavators, parked on the side of the road. The road is partially blocked by construction materials and traffic cones. As the camera moves forward, a pedestrian is seen walking in the middle of the road, and a cyclist is visible further down the street. The street is lined with parked cars and residential buildings, with bare trees indicating a winter or early spring season. The main risk is A pedestrian walking in the middle of the road, which is an unexpected and dangerous situation for a vehicle to encounter. The preferred action is to the ego vehicle should slow down and prepare to stop, maintaining a safe distance from the pedestrian to avoid a collision.",
      "frame_name": "1bd3a510a00a6764f2ca0c08d2635c92-175",
      "anchor_image_path": "1bd3a510a00a6764f2ca0c08d2635c92/CAM_FRONT/1bd3a510a00a6764f2ca0c08d2635c92-175_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "1bd3a510a00a6764f2ca0c08d2635c92",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "1bd3a510a00a6764f2ca0c08d2635c92:group_014",
      "image_paths": [
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      "scoring_type": "open_oeq_text"
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    {
      "group_id": "group_012",
      "frame_index": 193,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "1c2695b6dc58bde4cdaacebf572c199a:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a wet, rain-slicked city street. The view is from a vehicle's front camera, with raindrops on the windshield creating prominent bokeh effects. The vehicle approaches and stops at a red light, then proceeds through the intersection as the light turns green. Other vehicles are visible, including a white SUV crossing the intersection and oncoming traffic. The main risk is The main risk is the poor visibility due to rain and the resulting reflections on the wet road, which can obscure other vehicles and road markings. The risk of hydroplaning or reduced traction is also present. The preferred action is to the vehicle should slow down as it approaches the intersection, come to a complete stop at the red light, and then proceed with caution when the light turns green, maintaining a safe distance from other vehicles.",
      "bundle_frame_names": [
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        "1c2695b6dc58bde4cdaacebf572c199a-178",
        "1c2695b6dc58bde4cdaacebf572c199a-183",
        "1c2695b6dc58bde4cdaacebf572c199a-188",
        "1c2695b6dc58bde4cdaacebf572c199a-193"
      ],
      "ground_truth_text": "A nighttime driving scene on a wet, rain-slicked city street. The view is from a vehicle's front camera, with raindrops on the windshield creating prominent bokeh effects. The vehicle approaches and stops at a red light, then proceeds through the intersection as the light turns green. Other vehicles are visible, including a white SUV crossing the intersection and oncoming traffic. The main risk is The main risk is the poor visibility due to rain and the resulting reflections on the wet road, which can obscure other vehicles and road markings. The risk of hydroplaning or reduced traction is also present. The preferred action is to the vehicle should slow down as it approaches the intersection, come to a complete stop at the red light, and then proceed with caution when the light turns green, maintaining a safe distance from other vehicles.",
      "frame_name": "1c2695b6dc58bde4cdaacebf572c199a-193",
      "anchor_image_path": "1c2695b6dc58bde4cdaacebf572c199a/CAM_FRONT/1c2695b6dc58bde4cdaacebf572c199a-193_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "1c2695b6dc58bde4cdaacebf572c199a",
      "scenario_cluster": "Others",
      "bundle_id": "1c2695b6dc58bde4cdaacebf572c199a:group_012",
      "image_paths": [
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      ],
      "image_path": "1c2695b6dc58bde4cdaacebf572c199a/CAM_FRONT/1c2695b6dc58bde4cdaacebf572c199a-193_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 134,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "1ed2ba95b036f4a0d22347e9f6cc987a:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a sunny, urban street in a residential area with commercial buildings. The road has a dedicated bike lane on the left, separated by a painted buffer. The vehicle proceeds straight, passing a crosswalk and a gas station, and continues down the street, which is lined with trees and parked cars. Pedestrians are visible on the sidewalks, and a cyclist is seen riding in the bike lane. The main risk is The primary risk is the presence of the cyclist in the bike lane. While the cyclist is in a designated area, there is a potential for conflict if the cyclist were to swerve into the traffic lane or if the ego vehicle were to drift into the bike lane. Additionally, the cyclist's position near the intersection could pose a risk if they are not visible to other drivers. The preferred action is to the ego vehicle should maintain a safe following distance and stay within its lane. The driver should be aware of the cyclist's presence and be prepared to react if the cyclist changes position or if other vehicles or pedestrians enter the scene. The vehicle should continue to drive cautiously and follow traffic rules.",
      "bundle_frame_names": [
        "1ed2ba95b036f4a0d22347e9f6cc987a-114",
        "1ed2ba95b036f4a0d22347e9f6cc987a-119",
        "1ed2ba95b036f4a0d22347e9f6cc987a-124",
        "1ed2ba95b036f4a0d22347e9f6cc987a-129",
        "1ed2ba95b036f4a0d22347e9f6cc987a-134"
      ],
      "ground_truth_text": "A vehicle drives through a sunny, urban street in a residential area with commercial buildings. The road has a dedicated bike lane on the left, separated by a painted buffer. The vehicle proceeds straight, passing a crosswalk and a gas station, and continues down the street, which is lined with trees and parked cars. Pedestrians are visible on the sidewalks, and a cyclist is seen riding in the bike lane. The main risk is The primary risk is the presence of the cyclist in the bike lane. While the cyclist is in a designated area, there is a potential for conflict if the cyclist were to swerve into the traffic lane or if the ego vehicle were to drift into the bike lane. Additionally, the cyclist's position near the intersection could pose a risk if they are not visible to other drivers. The preferred action is to the ego vehicle should maintain a safe following distance and stay within its lane. The driver should be aware of the cyclist's presence and be prepared to react if the cyclist changes position or if other vehicles or pedestrians enter the scene. The vehicle should continue to drive cautiously and follow traffic rules.",
      "frame_name": "1ed2ba95b036f4a0d22347e9f6cc987a-134",
      "anchor_image_path": "1ed2ba95b036f4a0d22347e9f6cc987a/CAM_FRONT/1ed2ba95b036f4a0d22347e9f6cc987a-134_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "1ed2ba95b036f4a0d22347e9f6cc987a",
      "scenario_cluster": "Cyclist",
      "bundle_id": "1ed2ba95b036f4a0d22347e9f6cc987a:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 64,
      "history_image_paths": [
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        "203e27fe231ca825208905171156a339/CAM_FRONT/203e27fe231ca825208905171156a339-064_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "203e27fe231ca825208905171156a339:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A multi-lane highway scene with moderate traffic, viewed from a vehicle's front perspective. The vehicle is driving under a large concrete overpass, with other cars in adjacent lanes. The traffic is dense, with a mix of sedans, SUVs, and a motorcycle visible. The sky is overcast, and the road is flanked by greenery and buildings in the distance. The main risk is The main risk source is the presence of other vehicles, especially the motorcycle on the left, which may be in the ego vehicle's blind spot, and the dense traffic which requires constant monitoring and potential lane changes. The preferred action is to maintain a safe following distance, stay in the current lane, and be vigilant of the motorcycle and other vehicles in adjacent lanes, especially when considering lane changes or merging.",
      "bundle_frame_names": [
        "203e27fe231ca825208905171156a339-044",
        "203e27fe231ca825208905171156a339-049",
        "203e27fe231ca825208905171156a339-054",
        "203e27fe231ca825208905171156a339-059",
        "203e27fe231ca825208905171156a339-064"
      ],
      "ground_truth_text": "A multi-lane highway scene with moderate traffic, viewed from a vehicle's front perspective. The vehicle is driving under a large concrete overpass, with other cars in adjacent lanes. The traffic is dense, with a mix of sedans, SUVs, and a motorcycle visible. The sky is overcast, and the road is flanked by greenery and buildings in the distance. The main risk is The main risk source is the presence of other vehicles, especially the motorcycle on the left, which may be in the ego vehicle's blind spot, and the dense traffic which requires constant monitoring and potential lane changes. The preferred action is to maintain a safe following distance, stay in the current lane, and be vigilant of the motorcycle and other vehicles in adjacent lanes, especially when considering lane changes or merging.",
      "frame_name": "203e27fe231ca825208905171156a339-064",
      "anchor_image_path": "203e27fe231ca825208905171156a339/CAM_FRONT/203e27fe231ca825208905171156a339-064_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "203e27fe231ca825208905171156a339",
      "scenario_cluster": "Others",
      "bundle_id": "203e27fe231ca825208905171156a339:group_013",
      "image_paths": [
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      ],
      "image_path": "203e27fe231ca825208905171156a339/CAM_FRONT/203e27fe231ca825208905171156a339-064_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 73,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "21c8f36a1a5a4075a7439ba47b302a89:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A wet, urban street scene on an overcast day. The camera moves forward along a two-way road lined with parked cars and trees. Pedestrians are seen crossing the street at a crosswalk in the distance and later walking on the sidewalk. A FedEx delivery truck is parked on the left side of the road. The main risk is Pedestrians crossing the road, which could lead to a collision if the ego vehicle does not yield. The preferred action is to slow down and yield to pedestrians crossing the street.",
      "bundle_frame_names": [
        "21c8f36a1a5a4075a7439ba47b302a89-053",
        "21c8f36a1a5a4075a7439ba47b302a89-058",
        "21c8f36a1a5a4075a7439ba47b302a89-063",
        "21c8f36a1a5a4075a7439ba47b302a89-068",
        "21c8f36a1a5a4075a7439ba47b302a89-073"
      ],
      "ground_truth_text": "A wet, urban street scene on an overcast day. The camera moves forward along a two-way road lined with parked cars and trees. Pedestrians are seen crossing the street at a crosswalk in the distance and later walking on the sidewalk. A FedEx delivery truck is parked on the left side of the road. The main risk is Pedestrians crossing the road, which could lead to a collision if the ego vehicle does not yield. The preferred action is to slow down and yield to pedestrians crossing the street.",
      "frame_name": "21c8f36a1a5a4075a7439ba47b302a89-073",
      "anchor_image_path": "21c8f36a1a5a4075a7439ba47b302a89/CAM_FRONT/21c8f36a1a5a4075a7439ba47b302a89-073_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "21c8f36a1a5a4075a7439ba47b302a89",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "21c8f36a1a5a4075a7439ba47b302a89:group_012",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_014",
      "frame_index": 30,
      "history_image_paths": [
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        "2422554652766dd7dbf56df649d5600a/CAM_FRONT/2422554652766dd7dbf56df649d5600a-030_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "2422554652766dd7dbf56df649d5600a:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime scene of a narrow urban street lined with parked cars and multi-story buildings. The camera vehicle is moving forward, revealing a long, dark street with a tall, illuminated building in the distance. The road surface is visible, and the scene is lit by streetlights and building lights. The main risk is Foreign Object Debris The preferred action is to proceed with caution, maintaining a steady speed and being prepared to stop if an obstacle is detected on the road.",
      "bundle_frame_names": [
        "2422554652766dd7dbf56df649d5600a-010",
        "2422554652766dd7dbf56df649d5600a-015",
        "2422554652766dd7dbf56df649d5600a-020",
        "2422554652766dd7dbf56df649d5600a-025",
        "2422554652766dd7dbf56df649d5600a-030"
      ],
      "ground_truth_text": "A nighttime scene of a narrow urban street lined with parked cars and multi-story buildings. The camera vehicle is moving forward, revealing a long, dark street with a tall, illuminated building in the distance. The road surface is visible, and the scene is lit by streetlights and building lights. The main risk is Foreign Object Debris The preferred action is to proceed with caution, maintaining a steady speed and being prepared to stop if an obstacle is detected on the road.",
      "frame_name": "2422554652766dd7dbf56df649d5600a-030",
      "anchor_image_path": "2422554652766dd7dbf56df649d5600a/CAM_FRONT/2422554652766dd7dbf56df649d5600a-030_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "2422554652766dd7dbf56df649d5600a",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "2422554652766dd7dbf56df649d5600a:group_014",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 214,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "24d9f46a085987f998d5df96e11f0b92:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives along a narrow, winding residential road lined with parked cars and tall trees. The road curves to the right, leading to a T-intersection with a stop sign and a crosswalk. The scene is set in a quiet, suburban neighborhood with a mix of houses and greenery under an overcast sky. The main risk is The intersection presents a risk of conflict with cross-traffic, especially if a vehicle or pedestrian is not visible. The narrow road and parked cars on the left limit visibility and maneuvering space. The preferred action is to the ego vehicle should decelerate and come to a complete stop at the stop sign, ensuring the intersection is clear before proceeding.",
      "bundle_frame_names": [
        "24d9f46a085987f998d5df96e11f0b92-194",
        "24d9f46a085987f998d5df96e11f0b92-199",
        "24d9f46a085987f998d5df96e11f0b92-204",
        "24d9f46a085987f998d5df96e11f0b92-209",
        "24d9f46a085987f998d5df96e11f0b92-214"
      ],
      "ground_truth_text": "A vehicle drives along a narrow, winding residential road lined with parked cars and tall trees. The road curves to the right, leading to a T-intersection with a stop sign and a crosswalk. The scene is set in a quiet, suburban neighborhood with a mix of houses and greenery under an overcast sky. The main risk is The intersection presents a risk of conflict with cross-traffic, especially if a vehicle or pedestrian is not visible. The narrow road and parked cars on the left limit visibility and maneuvering space. The preferred action is to the ego vehicle should decelerate and come to a complete stop at the stop sign, ensuring the intersection is clear before proceeding.",
      "frame_name": "24d9f46a085987f998d5df96e11f0b92-214",
      "anchor_image_path": "24d9f46a085987f998d5df96e11f0b92/CAM_FRONT/24d9f46a085987f998d5df96e11f0b92-214_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "24d9f46a085987f998d5df96e11f0b92",
      "scenario_cluster": "Intersection",
      "bundle_id": "24d9f46a085987f998d5df96e11f0b92:group_013",
      "image_paths": [
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_033",
      "frame_index": 124,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "262e00554925cca1cf924ed5ea430d66:group_033_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a quiet, residential neighborhood at night. The scene is dark, illuminated only by streetlights and the vehicle's headlights. The vehicle approaches and then passes through a four-way intersection, with a stop sign visible on the right. The road is lined with houses, fences, and trees, and the environment is completely devoid of other traffic or pedestrians. The main risk is The main risk is the potential for a collision with another vehicle or pedestrian at the intersection, especially given the low visibility at night. The vehicle must also ensure it stops completely at the stop sign before proceeding. The preferred action is to the vehicle should slow down as it approaches the intersection, come to a complete stop at the stop sign, check for any cross traffic or pedestrians, and then proceed with caution through the intersection.",
      "bundle_frame_names": [
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        "262e00554925cca1cf924ed5ea430d66-109",
        "262e00554925cca1cf924ed5ea430d66-114",
        "262e00554925cca1cf924ed5ea430d66-119",
        "262e00554925cca1cf924ed5ea430d66-124"
      ],
      "ground_truth_text": "A vehicle drives through a quiet, residential neighborhood at night. The scene is dark, illuminated only by streetlights and the vehicle's headlights. The vehicle approaches and then passes through a four-way intersection, with a stop sign visible on the right. The road is lined with houses, fences, and trees, and the environment is completely devoid of other traffic or pedestrians. The main risk is The main risk is the potential for a collision with another vehicle or pedestrian at the intersection, especially given the low visibility at night. The vehicle must also ensure it stops completely at the stop sign before proceeding. The preferred action is to the vehicle should slow down as it approaches the intersection, come to a complete stop at the stop sign, check for any cross traffic or pedestrians, and then proceed with caution through the intersection.",
      "frame_name": "262e00554925cca1cf924ed5ea430d66-124",
      "anchor_image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-124_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "262e00554925cca1cf924ed5ea430d66",
      "scenario_cluster": "Intersection",
      "bundle_id": "262e00554925cca1cf924ed5ea430d66:group_033",
      "image_paths": [
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      ],
      "image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-124_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 214,
      "history_image_paths": [
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        "2bf8a58be87bb47c9fbe45b12b84b4ea/CAM_FRONT/2bf8a58be87bb47c9fbe45b12b84b4ea-214_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "2bf8a58be87bb47c9fbe45b12b84b4ea:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a city street with ongoing construction, indicated by orange and white barriers on the left. The scene is set in a dense urban environment with tall buildings and trees lining the road. As the vehicle progresses, it approaches an intersection where several pedestrians are visible on the sidewalk and at a crosswalk. The main risk is Pedestrians may enter the roadway, especially at the upcoming intersection, requiring the ego vehicle to be vigilant and prepared to stop. The preferred action is to proceed with caution, maintain a safe speed, and be prepared to yield to pedestrians at the crosswalk.",
      "bundle_frame_names": [
        "2bf8a58be87bb47c9fbe45b12b84b4ea-194",
        "2bf8a58be87bb47c9fbe45b12b84b4ea-199",
        "2bf8a58be87bb47c9fbe45b12b84b4ea-204",
        "2bf8a58be87bb47c9fbe45b12b84b4ea-209",
        "2bf8a58be87bb47c9fbe45b12b84b4ea-214"
      ],
      "ground_truth_text": "A vehicle drives through a city street with ongoing construction, indicated by orange and white barriers on the left. The scene is set in a dense urban environment with tall buildings and trees lining the road. As the vehicle progresses, it approaches an intersection where several pedestrians are visible on the sidewalk and at a crosswalk. The main risk is Pedestrians may enter the roadway, especially at the upcoming intersection, requiring the ego vehicle to be vigilant and prepared to stop. The preferred action is to proceed with caution, maintain a safe speed, and be prepared to yield to pedestrians at the crosswalk.",
      "frame_name": "2bf8a58be87bb47c9fbe45b12b84b4ea-214",
      "anchor_image_path": "2bf8a58be87bb47c9fbe45b12b84b4ea/CAM_FRONT/2bf8a58be87bb47c9fbe45b12b84b4ea-214_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "2bf8a58be87bb47c9fbe45b12b84b4ea",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "2bf8a58be87bb47c9fbe45b12b84b4ea:group_013",
      "image_paths": [
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        "2bf8a58be87bb47c9fbe45b12b84b4ea/CAM_FRONT/2bf8a58be87bb47c9fbe45b12b84b4ea-214_FRONT.jpg"
      ],
      "image_path": "2bf8a58be87bb47c9fbe45b12b84b4ea/CAM_FRONT/2bf8a58be87bb47c9fbe45b12b84b4ea-214_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 138,
      "history_image_paths": [
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        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-138_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "31935e26a0bd05d0f9bf5285154cb25e:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a multi-lane road with streetlights. The vehicle is moving forward, and the environment transitions from a relatively empty road to an area with commercial buildings and traffic lights. A cyclist appears on the right side of the road in the later frames. The main risk is The cyclist is on the right shoulder, which is a non-designated path for cycling. The low visibility at night and the proximity to the road create a risk of collision, especially if the cyclist moves into the travel lane or the ego vehicle needs to change lanes. The preferred action is to maintain a safe following distance and be prepared to slow down or stop if the cyclist moves into the travel lane. The driver should be vigilant and ensure they have a clear view of the cyclist's movements.",
      "bundle_frame_names": [
        "31935e26a0bd05d0f9bf5285154cb25e-118",
        "31935e26a0bd05d0f9bf5285154cb25e-123",
        "31935e26a0bd05d0f9bf5285154cb25e-128",
        "31935e26a0bd05d0f9bf5285154cb25e-133",
        "31935e26a0bd05d0f9bf5285154cb25e-138"
      ],
      "ground_truth_text": "A nighttime driving scene on a multi-lane road with streetlights. The vehicle is moving forward, and the environment transitions from a relatively empty road to an area with commercial buildings and traffic lights. A cyclist appears on the right side of the road in the later frames. The main risk is The cyclist is on the right shoulder, which is a non-designated path for cycling. The low visibility at night and the proximity to the road create a risk of collision, especially if the cyclist moves into the travel lane or the ego vehicle needs to change lanes. The preferred action is to maintain a safe following distance and be prepared to slow down or stop if the cyclist moves into the travel lane. The driver should be vigilant and ensure they have a clear view of the cyclist's movements.",
      "frame_name": "31935e26a0bd05d0f9bf5285154cb25e-138",
      "anchor_image_path": "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-138_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "31935e26a0bd05d0f9bf5285154cb25e",
      "scenario_cluster": "Cyclist",
      "bundle_id": "31935e26a0bd05d0f9bf5285154cb25e:group_012",
      "image_paths": [
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      ],
      "image_path": "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-138_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_033",
      "frame_index": 64,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "34c1dae8ea57da92ae7ee1c3bce3ad74:group_033_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime urban scene at a multi-lane intersection with traffic lights. The traffic light for the main road is red, causing vehicles to stop. A pedestrian is seen crossing the street from right to left in the crosswalk. The traffic light eventually turns green, and the vehicles begin to move forward. The scene is illuminated by streetlights and vehicle headlights, with a restaurant sign visible on the right. The main risk is The pedestrian is crossing the intersection while the traffic light for the main road is red. The ego vehicle must be prepared to stop or yield to the pedestrian, even if it has a green light, to avoid a collision. The preferred action is to the ego vehicle should come to a complete stop and yield to the pedestrian crossing the street. The vehicle should remain stopped until the pedestrian has safely cleared the crosswalk.",
      "bundle_frame_names": [
        "34c1dae8ea57da92ae7ee1c3bce3ad74-044",
        "34c1dae8ea57da92ae7ee1c3bce3ad74-049",
        "34c1dae8ea57da92ae7ee1c3bce3ad74-054",
        "34c1dae8ea57da92ae7ee1c3bce3ad74-059",
        "34c1dae8ea57da92ae7ee1c3bce3ad74-064"
      ],
      "ground_truth_text": "A nighttime urban scene at a multi-lane intersection with traffic lights. The traffic light for the main road is red, causing vehicles to stop. A pedestrian is seen crossing the street from right to left in the crosswalk. The traffic light eventually turns green, and the vehicles begin to move forward. The scene is illuminated by streetlights and vehicle headlights, with a restaurant sign visible on the right. The main risk is The pedestrian is crossing the intersection while the traffic light for the main road is red. The ego vehicle must be prepared to stop or yield to the pedestrian, even if it has a green light, to avoid a collision. The preferred action is to the ego vehicle should come to a complete stop and yield to the pedestrian crossing the street. The vehicle should remain stopped until the pedestrian has safely cleared the crosswalk.",
      "frame_name": "34c1dae8ea57da92ae7ee1c3bce3ad74-064",
      "anchor_image_path": "34c1dae8ea57da92ae7ee1c3bce3ad74/CAM_FRONT/34c1dae8ea57da92ae7ee1c3bce3ad74-064_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "34c1dae8ea57da92ae7ee1c3bce3ad74",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "34c1dae8ea57da92ae7ee1c3bce3ad74:group_033",
      "image_paths": [
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      ],
      "image_path": "34c1dae8ea57da92ae7ee1c3bce3ad74/CAM_FRONT/34c1dae8ea57da92ae7ee1c3bce3ad74-064_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 203,
      "history_image_paths": [
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        "34e1b86e21b615ac813b81b09831b7b9/CAM_FRONT/34e1b86e21b615ac813b81b09831b7b9-203_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "34e1b86e21b615ac813b81b09831b7b9:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a multi-lane highway. The ego vehicle is following a black SUV in the same lane. The traffic is moderate, with other cars visible ahead and in the adjacent lanes. The scene is dark, illuminated only by vehicle lights and streetlights. The ego vehicle is driving under an overpass and past a series of green highway signs indicating exits for Ocean Ave, Geneva Ave, and City College. The main risk is The presence of a special vehicle (the black SUV) in the immediate path, which may have unpredictable maneuvers or require specific attention. The low visibility at night also increases the risk of a collision. The preferred action is to maintain a safe following distance and be prepared to adjust speed or change lanes if the special vehicle ahead makes an unexpected move.",
      "bundle_frame_names": [
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        "34e1b86e21b615ac813b81b09831b7b9-188",
        "34e1b86e21b615ac813b81b09831b7b9-193",
        "34e1b86e21b615ac813b81b09831b7b9-198",
        "34e1b86e21b615ac813b81b09831b7b9-203"
      ],
      "ground_truth_text": "A nighttime driving scene on a multi-lane highway. The ego vehicle is following a black SUV in the same lane. The traffic is moderate, with other cars visible ahead and in the adjacent lanes. The scene is dark, illuminated only by vehicle lights and streetlights. The ego vehicle is driving under an overpass and past a series of green highway signs indicating exits for Ocean Ave, Geneva Ave, and City College. The main risk is The presence of a special vehicle (the black SUV) in the immediate path, which may have unpredictable maneuvers or require specific attention. The low visibility at night also increases the risk of a collision. The preferred action is to maintain a safe following distance and be prepared to adjust speed or change lanes if the special vehicle ahead makes an unexpected move.",
      "frame_name": "34e1b86e21b615ac813b81b09831b7b9-203",
      "anchor_image_path": "34e1b86e21b615ac813b81b09831b7b9/CAM_FRONT/34e1b86e21b615ac813b81b09831b7b9-203_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "34e1b86e21b615ac813b81b09831b7b9",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "34e1b86e21b615ac813b81b09831b7b9:group_012",
      "image_paths": [
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      "image_path": "34e1b86e21b615ac813b81b09831b7b9/CAM_FRONT/34e1b86e21b615ac813b81b09831b7b9-203_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 178,
      "history_image_paths": [
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        "353964d5a1cab38f73f579bd87397519/CAM_FRONT/353964d5a1cab38f73f579bd87397519-178_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "353964d5a1cab38f73f579bd87397519:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A clear, sunny day on a busy urban street with moderate traffic. The scene is viewed from a vehicle's front perspective, showing a multi-lane road with cars parked on the right and moving traffic ahead. The street is lined with multi-story buildings and trees. A cyclist is visible in the left lane, riding away from the camera, and is the primary focus of the annotation. The main risk is The cyclist is in the left lane, which is the path of travel for the ego vehicle. The risk is that the cyclist may make an unexpected turn or lane change, or the ego vehicle may need to pass, requiring careful maneuvering. The preferred action is to maintain a safe following distance and be prepared to adjust speed or lane position if the cyclist changes direction or if traffic conditions change.",
      "bundle_frame_names": [
        "353964d5a1cab38f73f579bd87397519-158",
        "353964d5a1cab38f73f579bd87397519-163",
        "353964d5a1cab38f73f579bd87397519-168",
        "353964d5a1cab38f73f579bd87397519-173",
        "353964d5a1cab38f73f579bd87397519-178"
      ],
      "ground_truth_text": "A clear, sunny day on a busy urban street with moderate traffic. The scene is viewed from a vehicle's front perspective, showing a multi-lane road with cars parked on the right and moving traffic ahead. The street is lined with multi-story buildings and trees. A cyclist is visible in the left lane, riding away from the camera, and is the primary focus of the annotation. The main risk is The cyclist is in the left lane, which is the path of travel for the ego vehicle. The risk is that the cyclist may make an unexpected turn or lane change, or the ego vehicle may need to pass, requiring careful maneuvering. The preferred action is to maintain a safe following distance and be prepared to adjust speed or lane position if the cyclist changes direction or if traffic conditions change.",
      "frame_name": "353964d5a1cab38f73f579bd87397519-178",
      "anchor_image_path": "353964d5a1cab38f73f579bd87397519/CAM_FRONT/353964d5a1cab38f73f579bd87397519-178_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "353964d5a1cab38f73f579bd87397519",
      "scenario_cluster": "Cyclist",
      "bundle_id": "353964d5a1cab38f73f579bd87397519:group_012",
      "image_paths": [
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      ],
      "image_path": "353964d5a1cab38f73f579bd87397519/CAM_FRONT/353964d5a1cab38f73f579bd87397519-178_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 64,
      "history_image_paths": [
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        "36bcd46c350c6037c1b9f817636455db/CAM_FRONT/36bcd46c350c6037c1b9f817636455db-049_FRONT.jpg",
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        "36bcd46c350c6037c1b9f817636455db/CAM_FRONT/36bcd46c350c6037c1b9f817636455db-064_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "36bcd46c350c6037c1b9f817636455db:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "The ego vehicle is driving down a city street, which transitions from a wide, multi-lane road with a bike lane and commercial buildings into a narrow alleyway undergoing construction. The scene begins on a typical urban street with traffic and then turns into a more confined space with construction equipment and orange traffic cones. The main risk is The narrow alley with construction equipment and traffic cones, which limits the vehicle's maneuvering space and requires careful navigation. The preferred action is to proceed with caution through the intersection and into the alley, maintaining a safe speed and being prepared to stop if necessary.",
      "bundle_frame_names": [
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        "36bcd46c350c6037c1b9f817636455db-054",
        "36bcd46c350c6037c1b9f817636455db-059",
        "36bcd46c350c6037c1b9f817636455db-064"
      ],
      "ground_truth_text": "The ego vehicle is driving down a city street, which transitions from a wide, multi-lane road with a bike lane and commercial buildings into a narrow alleyway undergoing construction. The scene begins on a typical urban street with traffic and then turns into a more confined space with construction equipment and orange traffic cones. The main risk is The narrow alley with construction equipment and traffic cones, which limits the vehicle's maneuvering space and requires careful navigation. The preferred action is to proceed with caution through the intersection and into the alley, maintaining a safe speed and being prepared to stop if necessary.",
      "frame_name": "36bcd46c350c6037c1b9f817636455db-064",
      "anchor_image_path": "36bcd46c350c6037c1b9f817636455db/CAM_FRONT/36bcd46c350c6037c1b9f817636455db-064_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "36bcd46c350c6037c1b9f817636455db",
      "scenario_cluster": "Intersection",
      "bundle_id": "36bcd46c350c6037c1b9f817636455db:group_013",
      "image_paths": [
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      ],
      "image_path": "36bcd46c350c6037c1b9f817636455db/CAM_FRONT/36bcd46c350c6037c1b9f817636455db-064_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 133,
      "history_image_paths": [
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        "38641951b2266550863c2af0a871485b/CAM_FRONT/38641951b2266550863c2af0a871485b-133_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "38641951b2266550863c2af0a871485b:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A clear, sunny day in a coastal city with palm trees lining a multi-lane road. The ego vehicle is driving straight through a series of intersections, passing by a mix of modern buildings, shops, and a large billboard. The traffic is light, and the road is mostly clear, with a few other cars visible ahead and to the sides. The scene captures a typical urban environment with a relaxed, suburban feel. The main risk is The main risk source is the approaching intersection with traffic lights. The yellow light indicates that the ego vehicle must decide whether to stop or proceed, which requires careful judgment to avoid a potential collision with other vehicles or pedestrians. Additionally, the presence of a pedestrian crosswalk and other vehicles in the vicinity adds to the complexity of the situation. The preferred action is to the ego vehicle should slow down and prepare to stop at the intersection, as the traffic light is yellow. This action ensures safety and compliance with traffic regulations, especially given the potential for other vehicles and pedestrians to be present at the crosswalk.",
      "bundle_frame_names": [
        "38641951b2266550863c2af0a871485b-113",
        "38641951b2266550863c2af0a871485b-118",
        "38641951b2266550863c2af0a871485b-123",
        "38641951b2266550863c2af0a871485b-128",
        "38641951b2266550863c2af0a871485b-133"
      ],
      "ground_truth_text": "A clear, sunny day in a coastal city with palm trees lining a multi-lane road. The ego vehicle is driving straight through a series of intersections, passing by a mix of modern buildings, shops, and a large billboard. The traffic is light, and the road is mostly clear, with a few other cars visible ahead and to the sides. The scene captures a typical urban environment with a relaxed, suburban feel. The main risk is The main risk source is the approaching intersection with traffic lights. The yellow light indicates that the ego vehicle must decide whether to stop or proceed, which requires careful judgment to avoid a potential collision with other vehicles or pedestrians. Additionally, the presence of a pedestrian crosswalk and other vehicles in the vicinity adds to the complexity of the situation. The preferred action is to the ego vehicle should slow down and prepare to stop at the intersection, as the traffic light is yellow. This action ensures safety and compliance with traffic regulations, especially given the potential for other vehicles and pedestrians to be present at the crosswalk.",
      "frame_name": "38641951b2266550863c2af0a871485b-133",
      "anchor_image_path": "38641951b2266550863c2af0a871485b/CAM_FRONT/38641951b2266550863c2af0a871485b-133_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "38641951b2266550863c2af0a871485b",
      "scenario_cluster": "Intersection",
      "bundle_id": "38641951b2266550863c2af0a871485b:group_012",
      "image_paths": [
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      ],
      "image_path": "38641951b2266550863c2af0a871485b/CAM_FRONT/38641951b2266550863c2af0a871485b-133_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 149,
      "history_image_paths": [
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        "3c87e8251965c3ffbc95a320e49d20f6/CAM_FRONT/3c87e8251965c3ffbc95a320e49d20f6-149_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "3c87e8251965c3ffbc95a320e49d20f6:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime urban driving scene in a city with tall buildings and a prominent overhead structure. The vehicle travels through an intersection, proceeds under a large, illuminated, latticed canopy, and continues down a multi-lane road. A cyclist is visible on the right side of the road, riding in the same direction as the ego vehicle. The main risk is The cyclist is a vulnerable road user. The risk is that the ego vehicle may not detect the cyclist in time, especially in the low-light conditions, or may not maintain a safe lateral distance, potentially leading to a collision. The preferred action is to the ego vehicle should maintain a safe following distance and a clear lane, ensuring it has a clear view of the cyclist and is prepared to react to any sudden movements. The vehicle should be cautious of the cyclist's position and potential for changing lanes or stopping.",
      "bundle_frame_names": [
        "3c87e8251965c3ffbc95a320e49d20f6-129",
        "3c87e8251965c3ffbc95a320e49d20f6-134",
        "3c87e8251965c3ffbc95a320e49d20f6-139",
        "3c87e8251965c3ffbc95a320e49d20f6-144",
        "3c87e8251965c3ffbc95a320e49d20f6-149"
      ],
      "ground_truth_text": "A nighttime urban driving scene in a city with tall buildings and a prominent overhead structure. The vehicle travels through an intersection, proceeds under a large, illuminated, latticed canopy, and continues down a multi-lane road. A cyclist is visible on the right side of the road, riding in the same direction as the ego vehicle. The main risk is The cyclist is a vulnerable road user. The risk is that the ego vehicle may not detect the cyclist in time, especially in the low-light conditions, or may not maintain a safe lateral distance, potentially leading to a collision. The preferred action is to the ego vehicle should maintain a safe following distance and a clear lane, ensuring it has a clear view of the cyclist and is prepared to react to any sudden movements. The vehicle should be cautious of the cyclist's position and potential for changing lanes or stopping.",
      "frame_name": "3c87e8251965c3ffbc95a320e49d20f6-149",
      "anchor_image_path": "3c87e8251965c3ffbc95a320e49d20f6/CAM_FRONT/3c87e8251965c3ffbc95a320e49d20f6-149_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "3c87e8251965c3ffbc95a320e49d20f6",
      "scenario_cluster": "Cyclist",
      "bundle_id": "3c87e8251965c3ffbc95a320e49d20f6:group_013",
      "image_paths": [
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      ],
      "image_path": "3c87e8251965c3ffbc95a320e49d20f6/CAM_FRONT/3c87e8251965c3ffbc95a320e49d20f6-149_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 54,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "4114fc22dfe5b28572e503ee4ed21636:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a multi-lane urban street in a commercial area. The scene begins at an intersection with a gas station and a Circle K, then proceeds down a straight road with traffic, including cars and a bus. The road is lined with buildings, including a large construction site on the left and a school on the right. The vehicle navigates through traffic, changing lanes and following the flow of traffic. The main risk is The main risk is the potential for collisions with other vehicles, especially during lane changes or when other drivers make unexpected maneuvers. The presence of a construction site and a school also introduces the risk of pedestrians or workers entering the roadway. The preferred action is to the vehicle should maintain a safe following distance, stay aware of the traffic in adjacent lanes, and be prepared to change lanes or adjust speed as needed. It should also be vigilant for pedestrians and other road users, especially near the school.",
      "bundle_frame_names": [
        "4114fc22dfe5b28572e503ee4ed21636-034",
        "4114fc22dfe5b28572e503ee4ed21636-039",
        "4114fc22dfe5b28572e503ee4ed21636-044",
        "4114fc22dfe5b28572e503ee4ed21636-049",
        "4114fc22dfe5b28572e503ee4ed21636-054"
      ],
      "ground_truth_text": "A vehicle drives through a multi-lane urban street in a commercial area. The scene begins at an intersection with a gas station and a Circle K, then proceeds down a straight road with traffic, including cars and a bus. The road is lined with buildings, including a large construction site on the left and a school on the right. The vehicle navigates through traffic, changing lanes and following the flow of traffic. The main risk is The main risk is the potential for collisions with other vehicles, especially during lane changes or when other drivers make unexpected maneuvers. The presence of a construction site and a school also introduces the risk of pedestrians or workers entering the roadway. The preferred action is to the vehicle should maintain a safe following distance, stay aware of the traffic in adjacent lanes, and be prepared to change lanes or adjust speed as needed. It should also be vigilant for pedestrians and other road users, especially near the school.",
      "frame_name": "4114fc22dfe5b28572e503ee4ed21636-054",
      "anchor_image_path": "4114fc22dfe5b28572e503ee4ed21636/CAM_FRONT/4114fc22dfe5b28572e503ee4ed21636-054_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "4114fc22dfe5b28572e503ee4ed21636",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "4114fc22dfe5b28572e503ee4ed21636:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_032",
      "frame_index": 48,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "493017885990698147ed936640aee3f8:group_032_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a multi-lane road. The vehicle approaches and crosses a major intersection with traffic lights, then continues straight through a commercial area with construction zones and roadwork on the right side. The main risk is The intersection presents a risk of conflicting traffic, and the roadwork on the right introduces potential hazards from construction equipment and lane shifts. The preferred action is to proceed through the intersection with caution, then navigate the construction zone by maintaining a safe distance from the work area and following the lane markings.",
      "bundle_frame_names": [
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        "493017885990698147ed936640aee3f8-033",
        "493017885990698147ed936640aee3f8-038",
        "493017885990698147ed936640aee3f8-043",
        "493017885990698147ed936640aee3f8-048"
      ],
      "ground_truth_text": "A nighttime driving scene on a multi-lane road. The vehicle approaches and crosses a major intersection with traffic lights, then continues straight through a commercial area with construction zones and roadwork on the right side. The main risk is The intersection presents a risk of conflicting traffic, and the roadwork on the right introduces potential hazards from construction equipment and lane shifts. The preferred action is to proceed through the intersection with caution, then navigate the construction zone by maintaining a safe distance from the work area and following the lane markings.",
      "frame_name": "493017885990698147ed936640aee3f8-048",
      "anchor_image_path": "493017885990698147ed936640aee3f8/CAM_FRONT/493017885990698147ed936640aee3f8-048_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "493017885990698147ed936640aee3f8",
      "scenario_cluster": "Intersection",
      "bundle_id": "493017885990698147ed936640aee3f8:group_032",
      "image_paths": [
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      ],
      "image_path": "493017885990698147ed936640aee3f8/CAM_FRONT/493017885990698147ed936640aee3f8-048_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 234,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "517edb9d2be14368685cc9f6184e9afe:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives down a city street lined with modern buildings and parked cars. The sky is overcast. As the vehicle proceeds, a cyclist appears on the right side of the road, riding in the same direction. The cyclist is visible in the final frames of the clip, crossing a crosswalk and continuing along the sidewalk. The main risk is A cyclist crossing the road in front of the ego vehicle, which could pose a risk if the vehicle does not yield or slow down. The preferred action is to the ego vehicle should slow down and yield to the cyclist to ensure a safe passage.",
      "bundle_frame_names": [
        "517edb9d2be14368685cc9f6184e9afe-214",
        "517edb9d2be14368685cc9f6184e9afe-219",
        "517edb9d2be14368685cc9f6184e9afe-224",
        "517edb9d2be14368685cc9f6184e9afe-229",
        "517edb9d2be14368685cc9f6184e9afe-234"
      ],
      "ground_truth_text": "A vehicle drives down a city street lined with modern buildings and parked cars. The sky is overcast. As the vehicle proceeds, a cyclist appears on the right side of the road, riding in the same direction. The cyclist is visible in the final frames of the clip, crossing a crosswalk and continuing along the sidewalk. The main risk is A cyclist crossing the road in front of the ego vehicle, which could pose a risk if the vehicle does not yield or slow down. The preferred action is to the ego vehicle should slow down and yield to the cyclist to ensure a safe passage.",
      "frame_name": "517edb9d2be14368685cc9f6184e9afe-234",
      "anchor_image_path": "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-234_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "517edb9d2be14368685cc9f6184e9afe",
      "scenario_cluster": "Cyclist",
      "bundle_id": "517edb9d2be14368685cc9f6184e9afe:group_013",
      "image_paths": [
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 64,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "53ce363ce589d19f746a296cd8d81dff:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "The ego vehicle is driving on a multi-lane city street, navigating through traffic. It approaches an intersection, then makes a right turn onto a narrower, one-way street lined with parked cars and multi-story buildings. The vehicle continues down this side street, which is flanked by a blue building on the left and a red building on the right, with a tall skyscraper visible in the distance. The main risk is The main risk is the narrowness of the side street after the turn, which limits the vehicle's maneuvering space. The presence of parked cars on both sides increases the risk of a collision, especially if the vehicle is not perfectly aligned. Additionally, the tall skyscraper in the distance creates a visual tunnel effect, which can impact depth perception. The preferred action is to the ego vehicle should proceed with caution, maintaining a steady speed and keeping a safe distance from the vehicle ahead. It should stay centered in its lane and be prepared to adjust its position to avoid any potential obstacles, such as pedestrians or vehicles pulling out from parking spots.",
      "bundle_frame_names": [
        "53ce363ce589d19f746a296cd8d81dff-044",
        "53ce363ce589d19f746a296cd8d81dff-049",
        "53ce363ce589d19f746a296cd8d81dff-054",
        "53ce363ce589d19f746a296cd8d81dff-059",
        "53ce363ce589d19f746a296cd8d81dff-064"
      ],
      "ground_truth_text": "The ego vehicle is driving on a multi-lane city street, navigating through traffic. It approaches an intersection, then makes a right turn onto a narrower, one-way street lined with parked cars and multi-story buildings. The vehicle continues down this side street, which is flanked by a blue building on the left and a red building on the right, with a tall skyscraper visible in the distance. The main risk is The main risk is the narrowness of the side street after the turn, which limits the vehicle's maneuvering space. The presence of parked cars on both sides increases the risk of a collision, especially if the vehicle is not perfectly aligned. Additionally, the tall skyscraper in the distance creates a visual tunnel effect, which can impact depth perception. The preferred action is to the ego vehicle should proceed with caution, maintaining a steady speed and keeping a safe distance from the vehicle ahead. It should stay centered in its lane and be prepared to adjust its position to avoid any potential obstacles, such as pedestrians or vehicles pulling out from parking spots.",
      "frame_name": "53ce363ce589d19f746a296cd8d81dff-064",
      "anchor_image_path": "53ce363ce589d19f746a296cd8d81dff/CAM_FRONT/53ce363ce589d19f746a296cd8d81dff-064_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "53ce363ce589d19f746a296cd8d81dff",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "53ce363ce589d19f746a296cd8d81dff:group_013",
      "image_paths": [
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      ],
      "image_path": "53ce363ce589d19f746a296cd8d81dff/CAM_FRONT/53ce363ce589d19f746a296cd8d81dff-064_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 63,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "57e34daab2df82dfd78edad9dfc8d56d:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "The ego vehicle is stopped at a traffic light at an intersection on a multi-lane highway. The traffic light is red, and the vehicle is waiting behind a utility truck in the adjacent lane. After the light turns green, the ego vehicle proceeds through the intersection and continues driving on the highway, passing under an overhead gantry sign. The main risk is The primary risk is the potential for a collision with the vehicle in the adjacent lane if it does not yield or if the ego vehicle misjudges the timing of the light change. The risk is highest during the stop and initial acceleration phase. The preferred action is to the ego vehicle should stop at the red light, maintain a safe following distance from the vehicle ahead, and proceed only when the light turns green and the intersection is clear.",
      "bundle_frame_names": [
        "57e34daab2df82dfd78edad9dfc8d56d-043",
        "57e34daab2df82dfd78edad9dfc8d56d-048",
        "57e34daab2df82dfd78edad9dfc8d56d-053",
        "57e34daab2df82dfd78edad9dfc8d56d-058",
        "57e34daab2df82dfd78edad9dfc8d56d-063"
      ],
      "ground_truth_text": "The ego vehicle is stopped at a traffic light at an intersection on a multi-lane highway. The traffic light is red, and the vehicle is waiting behind a utility truck in the adjacent lane. After the light turns green, the ego vehicle proceeds through the intersection and continues driving on the highway, passing under an overhead gantry sign. The main risk is The primary risk is the potential for a collision with the vehicle in the adjacent lane if it does not yield or if the ego vehicle misjudges the timing of the light change. The risk is highest during the stop and initial acceleration phase. The preferred action is to the ego vehicle should stop at the red light, maintain a safe following distance from the vehicle ahead, and proceed only when the light turns green and the intersection is clear.",
      "frame_name": "57e34daab2df82dfd78edad9dfc8d56d-063",
      "anchor_image_path": "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-063_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "57e34daab2df82dfd78edad9dfc8d56d",
      "scenario_cluster": "Intersection",
      "bundle_id": "57e34daab2df82dfd78edad9dfc8d56d:group_012",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 194,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle is driving on a multi-lane highway in a desert environment under a clear blue sky. The scene begins on a relatively empty road and progresses into a more congested area with multiple vehicles in adjacent lanes. The vehicle approaches an interchange, with other cars changing lanes and merging, demonstrating typical highway traffic flow. The main risk is The main risk source is the potential for collisions with other vehicles during lane changes and merges. The presence of multiple vehicles in close proximity, especially in the adjacent lanes, increases the risk of a collision if the maneuvers are not executed safely. The preferred action is to the preferred action is to maintain a safe following distance and be prepared to adjust speed or change lanes if necessary. The vehicle should remain vigilant and monitor the movements of surrounding vehicles, especially those in the adjacent lanes, to ensure safe navigation through the traffic.",
      "bundle_frame_names": [
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-174",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-179",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-184",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-189",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-194"
      ],
      "ground_truth_text": "A vehicle is driving on a multi-lane highway in a desert environment under a clear blue sky. The scene begins on a relatively empty road and progresses into a more congested area with multiple vehicles in adjacent lanes. The vehicle approaches an interchange, with other cars changing lanes and merging, demonstrating typical highway traffic flow. The main risk is The main risk source is the potential for collisions with other vehicles during lane changes and merges. The presence of multiple vehicles in close proximity, especially in the adjacent lanes, increases the risk of a collision if the maneuvers are not executed safely. The preferred action is to the preferred action is to maintain a safe following distance and be prepared to adjust speed or change lanes if necessary. The vehicle should remain vigilant and monitor the movements of surrounding vehicles, especially those in the adjacent lanes, to ensure safe navigation through the traffic.",
      "frame_name": "5a71aee52b4764ceb8c0bef7f3e3a6c9-194",
      "anchor_image_path": "5a71aee52b4764ceb8c0bef7f3e3a6c9/CAM_FRONT/5a71aee52b4764ceb8c0bef7f3e3a6c9-194_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9:group_013",
      "image_paths": [
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      ],
      "image_path": "5a71aee52b4764ceb8c0bef7f3e3a6c9/CAM_FRONT/5a71aee52b4764ceb8c0bef7f3e3a6c9-194_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 33,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "5c1bfbe96f5d1b4f0aa8684d52e1785a:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a residential intersection on a sunny day. The scene features a two-lane road with a crosswalk, flanked by multi-story townhouses and parked cars. The vehicle proceeds through the intersection, navigating a slight curve in the road, and continues up a hill. The main risk is A pedestrian crossing sign indicates potential pedestrian activity. The intersection is a point of conflict where the ego vehicle must yield to cross traffic and be vigilant for pedestrians crossing the street. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being prepared to stop for any pedestrians or cross traffic.",
      "bundle_frame_names": [
        "5c1bfbe96f5d1b4f0aa8684d52e1785a-013",
        "5c1bfbe96f5d1b4f0aa8684d52e1785a-018",
        "5c1bfbe96f5d1b4f0aa8684d52e1785a-023",
        "5c1bfbe96f5d1b4f0aa8684d52e1785a-028",
        "5c1bfbe96f5d1b4f0aa8684d52e1785a-033"
      ],
      "ground_truth_text": "A vehicle drives through a residential intersection on a sunny day. The scene features a two-lane road with a crosswalk, flanked by multi-story townhouses and parked cars. The vehicle proceeds through the intersection, navigating a slight curve in the road, and continues up a hill. The main risk is A pedestrian crossing sign indicates potential pedestrian activity. The intersection is a point of conflict where the ego vehicle must yield to cross traffic and be vigilant for pedestrians crossing the street. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being prepared to stop for any pedestrians or cross traffic.",
      "frame_name": "5c1bfbe96f5d1b4f0aa8684d52e1785a-033",
      "anchor_image_path": "5c1bfbe96f5d1b4f0aa8684d52e1785a/CAM_FRONT/5c1bfbe96f5d1b4f0aa8684d52e1785a-033_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "5c1bfbe96f5d1b4f0aa8684d52e1785a",
      "scenario_cluster": "Intersection",
      "bundle_id": "5c1bfbe96f5d1b4f0aa8684d52e1785a:group_012",
      "image_paths": [
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_033",
      "frame_index": 94,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "5cc285613c15ac5d461d3bdcdb2654a4:group_033_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a multi-lane road. The vehicle approaches and crosses a major intersection with traffic lights and street signs. After the intersection, the road ahead is under construction, marked by a long line of orange and white traffic cones and barriers, with a 'SCHOOL' marking painted on the road surface. The main risk is The construction zone presents a risk of unexpected obstacles or changes in the road layout. The dark environment and lack of other vehicles reduce situational awareness. The preferred action is to proceed through the intersection and then slow down to navigate the construction zone safely, maintaining a steady course between the traffic cones.",
      "bundle_frame_names": [
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        "5cc285613c15ac5d461d3bdcdb2654a4-079",
        "5cc285613c15ac5d461d3bdcdb2654a4-084",
        "5cc285613c15ac5d461d3bdcdb2654a4-089",
        "5cc285613c15ac5d461d3bdcdb2654a4-094"
      ],
      "ground_truth_text": "A nighttime driving scene on a multi-lane road. The vehicle approaches and crosses a major intersection with traffic lights and street signs. After the intersection, the road ahead is under construction, marked by a long line of orange and white traffic cones and barriers, with a 'SCHOOL' marking painted on the road surface. The main risk is The construction zone presents a risk of unexpected obstacles or changes in the road layout. The dark environment and lack of other vehicles reduce situational awareness. The preferred action is to proceed through the intersection and then slow down to navigate the construction zone safely, maintaining a steady course between the traffic cones.",
      "frame_name": "5cc285613c15ac5d461d3bdcdb2654a4-094",
      "anchor_image_path": "5cc285613c15ac5d461d3bdcdb2654a4/CAM_FRONT/5cc285613c15ac5d461d3bdcdb2654a4-094_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "5cc285613c15ac5d461d3bdcdb2654a4",
      "scenario_cluster": "Intersection",
      "bundle_id": "5cc285613c15ac5d461d3bdcdb2654a4:group_033",
      "image_paths": [
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 184,
      "history_image_paths": [
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        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-169_FRONT.jpg",
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "5f0d91d9bebc6af381629682682edcf8:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle is driving on a multi-lane highway during the day under a bright, sunny sky. The scene shows a complex interchange with multiple overpasses and other traffic. The sun is low in the sky, causing significant glare and lens flare in the upper right of the frames. The vehicle is moving forward, and the surrounding environment includes concrete barriers, road signs, and distant buildings. The main risk is The primary risk is the sun glare, which significantly impairs the driver's vision, particularly on the right side of the road. This reduced visibility increases the risk of not detecting foreign objects, debris, or other vehicles in the blind spots created by the glare. The preferred action is to the driver should reduce speed, increase following distance, and use the vehicle's sun visor to minimize the glare. They should also be extra vigilant for any signs of debris or other hazards, especially on the right shoulder and in the adjacent lanes, and be prepared to brake or maneuver if necessary.",
      "bundle_frame_names": [
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        "5f0d91d9bebc6af381629682682edcf8-169",
        "5f0d91d9bebc6af381629682682edcf8-174",
        "5f0d91d9bebc6af381629682682edcf8-179",
        "5f0d91d9bebc6af381629682682edcf8-184"
      ],
      "ground_truth_text": "A vehicle is driving on a multi-lane highway during the day under a bright, sunny sky. The scene shows a complex interchange with multiple overpasses and other traffic. The sun is low in the sky, causing significant glare and lens flare in the upper right of the frames. The vehicle is moving forward, and the surrounding environment includes concrete barriers, road signs, and distant buildings. The main risk is The primary risk is the sun glare, which significantly impairs the driver's vision, particularly on the right side of the road. This reduced visibility increases the risk of not detecting foreign objects, debris, or other vehicles in the blind spots created by the glare. The preferred action is to the driver should reduce speed, increase following distance, and use the vehicle's sun visor to minimize the glare. They should also be extra vigilant for any signs of debris or other hazards, especially on the right shoulder and in the adjacent lanes, and be prepared to brake or maneuver if necessary.",
      "frame_name": "5f0d91d9bebc6af381629682682edcf8-184",
      "anchor_image_path": "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-184_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "5f0d91d9bebc6af381629682682edcf8",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "5f0d91d9bebc6af381629682682edcf8:group_013",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 114,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "606924060f4d8b8d78a4241f44af9d24:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives under a concrete overpass on a multi-lane road. The scene transitions from a relatively congested area with multiple cars to a more open, less crowded street with industrial buildings and trees. The vehicle navigates through the intersection and continues straight, passing other vehicles, including a white utility truck and a black sedan, which are seen changing lanes and moving in the same direction. The main risk is The main risk source is the potential for collision or conflict with the black sedan and the white utility truck, which are both performing lane changes and maneuvers. The presence of other vehicles in the vicinity increases the complexity of the driving environment, requiring careful monitoring of lane changes and potential sudden movements. The preferred action is to the ego vehicle should maintain a safe following distance and be prepared to adjust its speed or lane position in response to the maneuvers of the black sedan and the white utility truck. It should also be vigilant for any sudden changes in the behavior of these vehicles, ensuring a safe and smooth passage through the intersection.",
      "bundle_frame_names": [
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        "606924060f4d8b8d78a4241f44af9d24-099",
        "606924060f4d8b8d78a4241f44af9d24-104",
        "606924060f4d8b8d78a4241f44af9d24-109",
        "606924060f4d8b8d78a4241f44af9d24-114"
      ],
      "ground_truth_text": "A vehicle drives under a concrete overpass on a multi-lane road. The scene transitions from a relatively congested area with multiple cars to a more open, less crowded street with industrial buildings and trees. The vehicle navigates through the intersection and continues straight, passing other vehicles, including a white utility truck and a black sedan, which are seen changing lanes and moving in the same direction. The main risk is The main risk source is the potential for collision or conflict with the black sedan and the white utility truck, which are both performing lane changes and maneuvers. The presence of other vehicles in the vicinity increases the complexity of the driving environment, requiring careful monitoring of lane changes and potential sudden movements. The preferred action is to the ego vehicle should maintain a safe following distance and be prepared to adjust its speed or lane position in response to the maneuvers of the black sedan and the white utility truck. It should also be vigilant for any sudden changes in the behavior of these vehicles, ensuring a safe and smooth passage through the intersection.",
      "frame_name": "606924060f4d8b8d78a4241f44af9d24-114",
      "anchor_image_path": "606924060f4d8b8d78a4241f44af9d24/CAM_FRONT/606924060f4d8b8d78a4241f44af9d24-114_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "606924060f4d8b8d78a4241f44af9d24",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "606924060f4d8b8d78a4241f44af9d24:group_013",
      "image_paths": [
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      ],
      "image_path": "606924060f4d8b8d78a4241f44af9d24/CAM_FRONT/606924060f4d8b8d78a4241f44af9d24-114_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_014",
      "frame_index": 50,
      "history_image_paths": [
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        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-050_FRONT.jpg"
      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "60d8cd7bcba63512079dd96f6b8fdfab:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives under a large concrete overpass in a busy urban environment. The scene is characterized by heavy traffic, including cars, trucks, and a white delivery van. The camera vehicle proceeds through an intersection and continues along the road, with the overpass structure dominating the upper portion of the frame. The lighting suggests it is daytime, possibly late afternoon. The main risk is The main risk is the presence of a cyclist, who is a small and potentially hard-to-see object in the traffic. The cyclist is located in the left lane, which is a high-risk area for collisions with other vehicles, especially when changing lanes or turning. The overpass structure can also create blind spots and affect visibility. The preferred action is to the vehicle should maintain a safe following distance, be vigilant for the cyclist, and be prepared to stop or yield if necessary. The driver should check mirrors and blind spots before making any maneuvers, especially when approaching or passing the cyclist.",
      "bundle_frame_names": [
        "60d8cd7bcba63512079dd96f6b8fdfab-030",
        "60d8cd7bcba63512079dd96f6b8fdfab-035",
        "60d8cd7bcba63512079dd96f6b8fdfab-040",
        "60d8cd7bcba63512079dd96f6b8fdfab-045",
        "60d8cd7bcba63512079dd96f6b8fdfab-050"
      ],
      "ground_truth_text": "A vehicle drives under a large concrete overpass in a busy urban environment. The scene is characterized by heavy traffic, including cars, trucks, and a white delivery van. The camera vehicle proceeds through an intersection and continues along the road, with the overpass structure dominating the upper portion of the frame. The lighting suggests it is daytime, possibly late afternoon. The main risk is The main risk is the presence of a cyclist, who is a small and potentially hard-to-see object in the traffic. The cyclist is located in the left lane, which is a high-risk area for collisions with other vehicles, especially when changing lanes or turning. The overpass structure can also create blind spots and affect visibility. The preferred action is to the vehicle should maintain a safe following distance, be vigilant for the cyclist, and be prepared to stop or yield if necessary. The driver should check mirrors and blind spots before making any maneuvers, especially when approaching or passing the cyclist.",
      "frame_name": "60d8cd7bcba63512079dd96f6b8fdfab-050",
      "anchor_image_path": "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-050_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "60d8cd7bcba63512079dd96f6b8fdfab",
      "scenario_cluster": "Cyclist",
      "bundle_id": "60d8cd7bcba63512079dd96f6b8fdfab:group_014",
      "image_paths": [
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      ],
      "image_path": "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-050_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 209,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "622e0fc649b4053ecfd9f35962a55107:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a multi-lane road with construction barriers on the left. The ego vehicle approaches an intersection with red traffic lights. A dark minivan is seen making a right turn across the intersection, while other vehicles are visible in the distance. The scene is illuminated by streetlights, and the traffic lights eventually turn green for the cross-traffic. The main risk is The main risk is the potential conflict with the minivan making a right turn, which could be an unpredictable maneuver. Additionally, the ego vehicle must be cautious of other vehicles in the intersection as the traffic light changes from red to green. The preferred action is to the ego vehicle should maintain a safe distance and be prepared to stop or yield if necessary, especially as the traffic light for the cross-traffic turns green. The driver should be vigilant for the turning minivan and other vehicles in the intersection.",
      "bundle_frame_names": [
        "622e0fc649b4053ecfd9f35962a55107-189",
        "622e0fc649b4053ecfd9f35962a55107-194",
        "622e0fc649b4053ecfd9f35962a55107-199",
        "622e0fc649b4053ecfd9f35962a55107-204",
        "622e0fc649b4053ecfd9f35962a55107-209"
      ],
      "ground_truth_text": "A nighttime driving scene on a multi-lane road with construction barriers on the left. The ego vehicle approaches an intersection with red traffic lights. A dark minivan is seen making a right turn across the intersection, while other vehicles are visible in the distance. The scene is illuminated by streetlights, and the traffic lights eventually turn green for the cross-traffic. The main risk is The main risk is the potential conflict with the minivan making a right turn, which could be an unpredictable maneuver. Additionally, the ego vehicle must be cautious of other vehicles in the intersection as the traffic light changes from red to green. The preferred action is to the ego vehicle should maintain a safe distance and be prepared to stop or yield if necessary, especially as the traffic light for the cross-traffic turns green. The driver should be vigilant for the turning minivan and other vehicles in the intersection.",
      "frame_name": "622e0fc649b4053ecfd9f35962a55107-209",
      "anchor_image_path": "622e0fc649b4053ecfd9f35962a55107/CAM_FRONT/622e0fc649b4053ecfd9f35962a55107-209_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "622e0fc649b4053ecfd9f35962a55107",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "622e0fc649b4053ecfd9f35962a55107:group_013",
      "image_paths": [
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      ],
      "image_path": "622e0fc649b4053ecfd9f35962a55107/CAM_FRONT/622e0fc649b4053ecfd9f35962a55107-209_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_013",
      "frame_index": 204,
      "history_image_paths": [
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        "62b67666791605074bbbbe60dad987f2/CAM_FRONT/62b67666791605074bbbbe60dad987f2-189_FRONT.jpg",
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "62b67666791605074bbbbe60dad987f2:group_013_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime urban scene where a dark-colored car is making a right turn at an intersection. After the turn, the vehicle proceeds down a street lined with parked cars and a large building. On the sidewalk, a group of people are gathered near a bus stop, and a cyclist is visible among them. The main risk is The cyclist on the sidewalk is a potential risk, as they may enter the roadway unexpectedly, especially near the bus stop where pedestrians are gathering. The preferred action is to the ego vehicle should proceed with caution, maintaining a safe speed and being vigilant for any pedestrians or cyclists who may cross the street or enter the travel lane.",
      "bundle_frame_names": [
        "62b67666791605074bbbbe60dad987f2-184",
        "62b67666791605074bbbbe60dad987f2-189",
        "62b67666791605074bbbbe60dad987f2-194",
        "62b67666791605074bbbbe60dad987f2-199",
        "62b67666791605074bbbbe60dad987f2-204"
      ],
      "ground_truth_text": "A nighttime urban scene where a dark-colored car is making a right turn at an intersection. After the turn, the vehicle proceeds down a street lined with parked cars and a large building. On the sidewalk, a group of people are gathered near a bus stop, and a cyclist is visible among them. The main risk is The cyclist on the sidewalk is a potential risk, as they may enter the roadway unexpectedly, especially near the bus stop where pedestrians are gathering. The preferred action is to the ego vehicle should proceed with caution, maintaining a safe speed and being vigilant for any pedestrians or cyclists who may cross the street or enter the travel lane.",
      "frame_name": "62b67666791605074bbbbe60dad987f2-204",
      "anchor_image_path": "62b67666791605074bbbbe60dad987f2/CAM_FRONT/62b67666791605074bbbbe60dad987f2-204_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "62b67666791605074bbbbe60dad987f2",
      "scenario_cluster": "Cyclist",
      "bundle_id": "62b67666791605074bbbbe60dad987f2:group_013",
      "image_paths": [
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      ],
      "image_path": "62b67666791605074bbbbe60dad987f2/CAM_FRONT/62b67666791605074bbbbe60dad987f2-204_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_032",
      "frame_index": 193,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "64c3f8ce8c99d46f5ef849049cdc1141:group_032_SC-4",
      "id": "SC-4",
      "ground_truth": "A clear, sunny day in a city with tall buildings on both sides of a multi-lane road. The camera vehicle is driving forward through an intersection and down the street. The road surface is visible, with painted lane markings and shadows from the buildings and trees. The scene is relatively quiet with minimal traffic and pedestrian activity. The main risk is Foreign Object Debris The preferred action is to continue driving forward, maintaining a safe speed and being vigilant for any potential hazards on the road surface.",
      "bundle_frame_names": [
        "64c3f8ce8c99d46f5ef849049cdc1141-173",
        "64c3f8ce8c99d46f5ef849049cdc1141-178",
        "64c3f8ce8c99d46f5ef849049cdc1141-183",
        "64c3f8ce8c99d46f5ef849049cdc1141-188",
        "64c3f8ce8c99d46f5ef849049cdc1141-193"
      ],
      "ground_truth_text": "A clear, sunny day in a city with tall buildings on both sides of a multi-lane road. The camera vehicle is driving forward through an intersection and down the street. The road surface is visible, with painted lane markings and shadows from the buildings and trees. The scene is relatively quiet with minimal traffic and pedestrian activity. The main risk is Foreign Object Debris The preferred action is to continue driving forward, maintaining a safe speed and being vigilant for any potential hazards on the road surface.",
      "frame_name": "64c3f8ce8c99d46f5ef849049cdc1141-193",
      "anchor_image_path": "64c3f8ce8c99d46f5ef849049cdc1141/CAM_FRONT/64c3f8ce8c99d46f5ef849049cdc1141-193_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "64c3f8ce8c99d46f5ef849049cdc1141",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "64c3f8ce8c99d46f5ef849049cdc1141:group_032",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_014",
      "frame_index": 210,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "65c0ae39052f249ae43d0303243f2fe8:group_014_SC-4",
      "id": "SC-4",
      "ground_truth": "A nighttime driving scene on a wet highway during a rainstorm. The vehicle is moving forward, passing under an overpass and approaching a construction zone with traffic cones and a 'Lane Closed' sign. The road is slick with reflections from the headlights and streetlights, and the view is partially obscured by raindrops on the windshield. The main risk is The primary risk is the construction zone on the right, which narrows the lane and requires the ego vehicle to navigate carefully. The wet road surface and poor visibility due to rain and darkness increase the risk of skidding or losing control. The preferred action is to the ego vehicle should slow down, maintain a safe distance from the vehicle ahead, and prepare to merge into the left lane to avoid the construction zone.",
      "bundle_frame_names": [
        "65c0ae39052f249ae43d0303243f2fe8-190",
        "65c0ae39052f249ae43d0303243f2fe8-195",
        "65c0ae39052f249ae43d0303243f2fe8-200",
        "65c0ae39052f249ae43d0303243f2fe8-205",
        "65c0ae39052f249ae43d0303243f2fe8-210"
      ],
      "ground_truth_text": "A nighttime driving scene on a wet highway during a rainstorm. The vehicle is moving forward, passing under an overpass and approaching a construction zone with traffic cones and a 'Lane Closed' sign. The road is slick with reflections from the headlights and streetlights, and the view is partially obscured by raindrops on the windshield. The main risk is The primary risk is the construction zone on the right, which narrows the lane and requires the ego vehicle to navigate carefully. The wet road surface and poor visibility due to rain and darkness increase the risk of skidding or losing control. The preferred action is to the ego vehicle should slow down, maintain a safe distance from the vehicle ahead, and prepare to merge into the left lane to avoid the construction zone.",
      "frame_name": "65c0ae39052f249ae43d0303243f2fe8-210",
      "anchor_image_path": "65c0ae39052f249ae43d0303243f2fe8/CAM_FRONT/65c0ae39052f249ae43d0303243f2fe8-210_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "65c0ae39052f249ae43d0303243f2fe8",
      "scenario_cluster": "Intersection",
      "bundle_id": "65c0ae39052f249ae43d0303243f2fe8:group_014",
      "image_paths": [
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      ],
      "image_path": "65c0ae39052f249ae43d0303243f2fe8/CAM_FRONT/65c0ae39052f249ae43d0303243f2fe8-210_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_032",
      "frame_index": 198,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "6c96c45f414e3570f93d29c9c2117d80:group_032_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a city street at night. The scene is characterized by dark, quiet streets with modern buildings on both sides, illuminated by streetlights. The vehicle approaches and stops at a red traffic light, which then turns green, allowing it to proceed through the intersection. The road surface shows visible lane markings and some faded orange spray paint, which could be related to construction or road work. The main risk is The primary risk is the potential for unexpected obstacles or changes in the road environment, such as construction equipment or workers, which are not clearly visible but are implied by the annotation. The dark conditions and lack of other traffic also contribute to the risk. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being vigilant for any potential hazards, such as construction zones or pedestrians, that may be present but not immediately visible.",
      "bundle_frame_names": [
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        "6c96c45f414e3570f93d29c9c2117d80-183",
        "6c96c45f414e3570f93d29c9c2117d80-188",
        "6c96c45f414e3570f93d29c9c2117d80-193",
        "6c96c45f414e3570f93d29c9c2117d80-198"
      ],
      "ground_truth_text": "A vehicle drives through a city street at night. The scene is characterized by dark, quiet streets with modern buildings on both sides, illuminated by streetlights. The vehicle approaches and stops at a red traffic light, which then turns green, allowing it to proceed through the intersection. The road surface shows visible lane markings and some faded orange spray paint, which could be related to construction or road work. The main risk is The primary risk is the potential for unexpected obstacles or changes in the road environment, such as construction equipment or workers, which are not clearly visible but are implied by the annotation. The dark conditions and lack of other traffic also contribute to the risk. The preferred action is to proceed through the intersection with caution, maintaining a safe speed and being vigilant for any potential hazards, such as construction zones or pedestrians, that may be present but not immediately visible.",
      "frame_name": "6c96c45f414e3570f93d29c9c2117d80-198",
      "anchor_image_path": "6c96c45f414e3570f93d29c9c2117d80/CAM_FRONT/6c96c45f414e3570f93d29c9c2117d80-198_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "6c96c45f414e3570f93d29c9c2117d80",
      "scenario_cluster": "Construction",
      "bundle_id": "6c96c45f414e3570f93d29c9c2117d80:group_032",
      "image_paths": [
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      "image_path": "6c96c45f414e3570f93d29c9c2117d80/CAM_FRONT/6c96c45f414e3570f93d29c9c2117d80-198_FRONT.jpg",
      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_033",
      "frame_index": 194,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "6d2556548b36274db8d073ef6c240ace:group_033_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a construction zone on a multi-lane road. The scene begins with the vehicle passing under a large, modern pedestrian bridge with a distinctive white X-brace design. As the vehicle proceeds, it navigates a curve to the left, passing numerous orange and white striped construction barrels and temporary barriers. The road is flanked by trees and a large building, identified as the Robert F. Spetzler Neurosciences Tower, is visible on the left. The sky is clear and blue, indicating a sunny day. The main risk is The construction zone presents a significant risk due to the presence of temporary barriers, barrels, and a large, mobile traffic sign that can obstruct the view of the road ahead. The vehicle must maintain a safe distance and be prepared for potential changes in traffic flow or the sudden appearance of workers or equipment. The preferred action is to the vehicle should proceed with caution, maintaining a steady speed and staying within the designated lane. It should be prepared to slow down or stop if necessary, and remain vigilant for any changes in the construction setup or the presence of workers.",
      "bundle_frame_names": [
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        "6d2556548b36274db8d073ef6c240ace-179",
        "6d2556548b36274db8d073ef6c240ace-184",
        "6d2556548b36274db8d073ef6c240ace-189",
        "6d2556548b36274db8d073ef6c240ace-194"
      ],
      "ground_truth_text": "A vehicle drives through a construction zone on a multi-lane road. The scene begins with the vehicle passing under a large, modern pedestrian bridge with a distinctive white X-brace design. As the vehicle proceeds, it navigates a curve to the left, passing numerous orange and white striped construction barrels and temporary barriers. The road is flanked by trees and a large building, identified as the Robert F. Spetzler Neurosciences Tower, is visible on the left. The sky is clear and blue, indicating a sunny day. The main risk is The construction zone presents a significant risk due to the presence of temporary barriers, barrels, and a large, mobile traffic sign that can obstruct the view of the road ahead. The vehicle must maintain a safe distance and be prepared for potential changes in traffic flow or the sudden appearance of workers or equipment. The preferred action is to the vehicle should proceed with caution, maintaining a steady speed and staying within the designated lane. It should be prepared to slow down or stop if necessary, and remain vigilant for any changes in the construction setup or the presence of workers.",
      "frame_name": "6d2556548b36274db8d073ef6c240ace-194",
      "anchor_image_path": "6d2556548b36274db8d073ef6c240ace/CAM_FRONT/6d2556548b36274db8d073ef6c240ace-194_FRONT.jpg",
      "task": "scenario-corner-case",
      "scene_id": "6d2556548b36274db8d073ef6c240ace",
      "scenario_cluster": "Construction",
      "bundle_id": "6d2556548b36274db8d073ef6c240ace:group_033",
      "image_paths": [
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      ],
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      "scoring_type": "open_oeq_text"
    },
    {
      "group_id": "group_012",
      "frame_index": 223,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "6eea094dcdafb7b521a5b4f210e98556:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives along a multi-lane road on a clear, sunny day. The road is flanked by trees, hills, and power lines. As the vehicle progresses, it approaches a construction zone with traffic cones and workers in the distance. The scene transitions from a relatively open road to a more complex intersection with traffic lights and other vehicles. The main risk is Pedestrians (construction workers) are present in the active work zone, which is a high-risk area for vehicle-pedestrian conflicts. The risk is heightened by the potential for sudden movements and the need for the ego vehicle to navigate around the work zone. The preferred action is to the ego vehicle should slow down and proceed with extreme caution through the construction zone. It must be prepared to stop if necessary, maintain a safe following distance, and be vigilant for any signals or movements from the workers.",
      "bundle_frame_names": [
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        "6eea094dcdafb7b521a5b4f210e98556-223"
      ],
      "ground_truth_text": "A vehicle drives along a multi-lane road on a clear, sunny day. The road is flanked by trees, hills, and power lines. As the vehicle progresses, it approaches a construction zone with traffic cones and workers in the distance. The scene transitions from a relatively open road to a more complex intersection with traffic lights and other vehicles. The main risk is Pedestrians (construction workers) are present in the active work zone, which is a high-risk area for vehicle-pedestrian conflicts. The risk is heightened by the potential for sudden movements and the need for the ego vehicle to navigate around the work zone. The preferred action is to the ego vehicle should slow down and proceed with extreme caution through the construction zone. It must be prepared to stop if necessary, maintain a safe following distance, and be vigilant for any signals or movements from the workers.",
      "frame_name": "6eea094dcdafb7b521a5b4f210e98556-223",
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      "task": "scenario-corner-case",
      "scene_id": "6eea094dcdafb7b521a5b4f210e98556",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "6eea094dcdafb7b521a5b4f210e98556:group_012",
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      "scoring_type": "open_oeq_text"
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    {
      "group_id": "group_012",
      "frame_index": 193,
      "history_image_paths": [
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      ],
      "question": "Briefly describe the scene, the main risk, and the preferred action.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "70a5a1a0881dc89625434d15b7919660:group_012_SC-4",
      "id": "SC-4",
      "ground_truth": "A vehicle drives through a city street flanked by tall skyscrapers under an overcast sky. The road is multi-laned with a crosswalk in the foreground. Pedestrians are visible on the sidewalks, and traffic lights are present at the intersection. The vehicle proceeds forward, passing under a pedestrian bridge with a banner. The main risk is Pedestrians crossing the street or walking on the sidewalk, which could lead to a collision if not properly detected and reacted to. The preferred action is to maintain a safe speed, be vigilant for pedestrians, and be prepared to stop if a pedestrian enters the crosswalk or the path of the vehicle.",
      "bundle_frame_names": [
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        "70a5a1a0881dc89625434d15b7919660-193"
      ],
      "ground_truth_text": "A vehicle drives through a city street flanked by tall skyscrapers under an overcast sky. The road is multi-laned with a crosswalk in the foreground. Pedestrians are visible on the sidewalks, and traffic lights are present at the intersection. The vehicle proceeds forward, passing under a pedestrian bridge with a banner. The main risk is Pedestrians crossing the street or walking on the sidewalk, which could lead to a collision if not properly detected and reacted to. The preferred action is to maintain a safe speed, be vigilant for pedestrians, and be prepared to stop if a pedestrian enters the crosswalk or the path of the vehicle.",
      "frame_name": "70a5a1a0881dc89625434d15b7919660-193",
      "anchor_image_path": "70a5a1a0881dc89625434d15b7919660/CAM_FRONT/70a5a1a0881dc89625434d15b7919660-193_FRONT.jpg",
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        "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-197_FRONT.jpg",
        "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-202_FRONT.jpg",
        "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-207_FRONT.jpg",
        "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-212_FRONT.jpg"
      ],
      "question": "What was the recent motion pattern of the object in the red bounding box?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "waymo_val_f4af235555fe56e0093c3c9c1c5452ab_group_212_TM-3_obj00",
      "id": "TM-3",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "f4af235555fe56e0093c3c9c1c5452ab-197",
        "f4af235555fe56e0093c3c9c1c5452ab-202",
        "f4af235555fe56e0093c3c9c1c5452ab-207",
        "f4af235555fe56e0093c3c9c1c5452ab-212"
      ],
      "choices": {
        "A": "Turning right",
        "B": "Slowing down",
        "C": "Accelerating",
        "D": "Turning left",
        "E": "Moving straight",
        "F": "Stopped previously"
      },
      "object_id": "f4af235555fe56e0093c3c9c1c5452ab-212#obj00",
      "ground_truth_text": "Turning right",
      "frame_name": "f4af235555fe56e0093c3c9c1c5452ab-212",
      "anchor_image_path": "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-212_FRONT.jpg",
      "task": "time-memory",
      "bbox_xyxy": [
        617.81,
        702.493,
        707.921,
        960.783
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      "scene_id": "f4af235555fe56e0093c3c9c1c5452ab",
      "scenario_cluster": "Cyclist",
      "bundle_id": "f4af235555fe56e0093c3c9c1c5452ab:group_212",
      "image_paths": [
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        "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-202_FRONT.jpg",
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        "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-212_FRONT.jpg"
      ],
      "object_reference": "the object in the red bounding box",
      "image_path": "f4af235555fe56e0093c3c9c1c5452ab/CAM_FRONT/f4af235555fe56e0093c3c9c1c5452ab-212_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_033",
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      "history_image_paths": [
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        "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-028_FRONT.jpg",
        "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-033_FRONT.jpg"
      ],
      "question": "In which lane was the object in the red bounding box located earlier in the sequence?",
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      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "waymo_val_05e1f9ef75f7e47d67f096e05d167dcb_group_033_TM-5_obj01",
      "id": "TM-5",
      "ground_truth": "A",
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        "05e1f9ef75f7e47d67f096e05d167dcb-018",
        "05e1f9ef75f7e47d67f096e05d167dcb-023",
        "05e1f9ef75f7e47d67f096e05d167dcb-028",
        "05e1f9ef75f7e47d67f096e05d167dcb-033"
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      "choices": {
        "A": "Right adjacent lane",
        "B": "Left adjacent lane",
        "C": "Same lane",
        "D": "Outside roadway"
      },
      "object_id": "05e1f9ef75f7e47d67f096e05d167dcb-033#obj01",
      "ground_truth_text": "Right adjacent lane",
      "frame_name": "05e1f9ef75f7e47d67f096e05d167dcb-033",
      "anchor_image_path": "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-033_FRONT.jpg",
      "task": "time-memory",
      "bbox_xyxy": [
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        843.309,
        896.467
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      "scene_id": "05e1f9ef75f7e47d67f096e05d167dcb",
      "scenario_cluster": "Cyclist",
      "bundle_id": "05e1f9ef75f7e47d67f096e05d167dcb:group_033",
      "image_paths": [
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        "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-023_FRONT.jpg",
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        "05e1f9ef75f7e47d67f096e05d167dcb/CAM_FRONT/05e1f9ef75f7e47d67f096e05d167dcb-033_FRONT.jpg"
      ],
      "object_reference": "the object in the red bounding box",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_198",
      "frame_index": 198,
      "history_image_paths": [
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        "0753089b73ce77d5c9e37dc924786b7f/CAM_FRONT/0753089b73ce77d5c9e37dc924786b7f-198_FRONT.jpg"
      ],
      "question": "In which lane was the object in the red bounding box located earlier in the sequence?",
      "source_dataset": "waymo_validation",
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      "question_format": "MCQ",
      "question_id": "waymo_val_0753089b73ce77d5c9e37dc924786b7f_group_198_TM-5_obj03",
      "id": "TM-5",
      "ground_truth": "B",
      "bundle_frame_names": [
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        "0753089b73ce77d5c9e37dc924786b7f-183",
        "0753089b73ce77d5c9e37dc924786b7f-188",
        "0753089b73ce77d5c9e37dc924786b7f-193",
        "0753089b73ce77d5c9e37dc924786b7f-198"
      ],
      "choices": {
        "A": "Left adjacent lane",
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        "C": "Right adjacent lane",
        "D": "Outside roadway"
      },
      "object_id": "0753089b73ce77d5c9e37dc924786b7f-198#obj03",
      "ground_truth_text": "Same lane",
      "frame_name": "0753089b73ce77d5c9e37dc924786b7f-198",
      "anchor_image_path": "0753089b73ce77d5c9e37dc924786b7f/CAM_FRONT/0753089b73ce77d5c9e37dc924786b7f-198_FRONT.jpg",
      "task": "time-memory",
      "bbox_xyxy": [
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        565.729,
        915.193,
        617.211
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      "scene_id": "0753089b73ce77d5c9e37dc924786b7f",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "0753089b73ce77d5c9e37dc924786b7f:group_198",
      "image_paths": [
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        "0753089b73ce77d5c9e37dc924786b7f/CAM_FRONT/0753089b73ce77d5c9e37dc924786b7f-198_FRONT.jpg"
      ],
      "object_reference": "the object in the red bounding box",
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      "scoring_type": "mcq"
    },
    {
      "group_id": "group_072",
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      "history_image_paths": [
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      ],
      "question": "In which lane was the object in the red bounding box located earlier in the sequence?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "waymo_val_07b4716982f88d85c6e5b350883b2d9b_group_072_TM-5_obj02",
      "id": "TM-5",
      "ground_truth": "D",
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        "07b4716982f88d85c6e5b350883b2d9b-057",
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        "07b4716982f88d85c6e5b350883b2d9b-067",
        "07b4716982f88d85c6e5b350883b2d9b-072"
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      "choices": {
        "A": "Same lane",
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        "C": "Right adjacent lane",
        "D": "Left adjacent lane"
      },
      "object_id": "07b4716982f88d85c6e5b350883b2d9b-072#obj02",
      "ground_truth_text": "Left adjacent lane",
      "frame_name": "07b4716982f88d85c6e5b350883b2d9b-072",
      "anchor_image_path": "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-072_FRONT.jpg",
      "task": "time-memory",
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      "scene_id": "07b4716982f88d85c6e5b350883b2d9b",
      "scenario_cluster": "Cut-ins",
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      "object_reference": "the object in the red bounding box",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_037",
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      "history_image_paths": [
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      ],
      "question": "In which lane was the object in the red bounding box located earlier in the sequence?",
      "source_dataset": "waymo_validation",
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      "question_format": "MCQ",
      "question_id": "waymo_val_0893a68205b49db42f5aeff87a9412ff_group_037_TM-5_obj02",
      "id": "TM-5",
      "ground_truth": "B",
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      "choices": {
        "A": "Same lane",
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        "C": "Left adjacent lane",
        "D": "Outside roadway"
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      "object_id": "0893a68205b49db42f5aeff87a9412ff-037#obj02",
      "ground_truth_text": "Right adjacent lane",
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      "task": "time-memory",
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        973.967,
        885.058
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      "scene_id": "0893a68205b49db42f5aeff87a9412ff",
      "scenario_cluster": "Foreign Object Debris",
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      "object_reference": "the object in the red bounding box",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_182",
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      "question": "In which lane was the object in the red bounding box located earlier in the sequence?",
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      "ground_truth": "B",
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      "choices": {
        "A": "Same lane",
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        "C": "Right adjacent lane",
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      "object_id": "09e7f15270ce9c3ea038d36c67f8ecc9-182#obj00",
      "ground_truth_text": "Left adjacent lane",
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      "task": "time-memory",
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        832.244
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      "scene_id": "09e7f15270ce9c3ea038d36c67f8ecc9",
      "scenario_cluster": "Multi-Lane Maneuvers",
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      "object_reference": "the object in the red bounding box",
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      "scoring_type": "mcq"
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    {
      "group_id": "group_084",
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    {
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    {
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      "question": "Based on the past 5 consecutive 360-degree multi-camera frames, what best describes the ego vehicle's recent maneuver over those past frames?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "waymo_val_fd52a15b681dc3920df93665305231f5_group_148_TRJ-1",
      "id": "TRJ-1",
      "ground_truth": "D",
      "bundle_frame_names": [
        "fd52a15b681dc3920df93665305231f5-128",
        "fd52a15b681dc3920df93665305231f5-133",
        "fd52a15b681dc3920df93665305231f5-138",
        "fd52a15b681dc3920df93665305231f5-143",
        "fd52a15b681dc3920df93665305231f5-148"
      ],
      "choices": {
        "A": "Go straight",
        "B": "Slightly veer left",
        "C": "Slightly veer right",
        "D": "Slow to near stop",
        "E": "Turn right",
        "F": "Turn left"
      },
      "ground_truth_text": "Slow to near stop",
      "frame_name": "fd52a15b681dc3920df93665305231f5-148",
      "anchor_image_path": "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "fd52a15b681dc3920df93665305231f5",
      "scenario_cluster": "Intersection",
      "bundle_id": "fd52a15b681dc3920df93665305231f5:group_148",
      "image_paths": [
        "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-128_FRONT.jpg",
        "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-133_FRONT.jpg",
        "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-138_FRONT.jpg",
        "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-143_FRONT.jpg",
        "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-148_FRONT.jpg"
      ],
      "image_path": "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-148_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-129_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-134_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-139_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-144_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-149_FRONT.jpg"
      ],
      "question": "What is the most likely consequence if the ego vehicle follows the alternative trajectory to move or turn left, instead of the best trajectory? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            7.448,
            0.003
          ],
          [
            26.799,
            0.036
          ],
          [
            44.708,
            0.198
          ],
          [
            57.887,
            0.705
          ],
          [
            73.019,
            2.114
          ]
        ],
        "B": [
          [
            7.448,
            0.006
          ],
          [
            27.458,
            0.029
          ],
          [
            47.661,
            0.398
          ],
          [
            63.946,
            1.569
          ],
          [
            84.343,
            4.7
          ]
        ],
        "C": [
          [
            7.071,
            0.002
          ],
          [
            26.608,
            0.044
          ],
          [
            45.651,
            0.387
          ],
          [
            61.046,
            1.105
          ],
          [
            80.524,
            3.246
          ]
        ]
      },
      "question_id": "waymo_val_01d758be9706c2534b80c2dd3a4234b9_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "01d758be9706c2534b80c2dd3a4234b9-129",
        "01d758be9706c2534b80c2dd3a4234b9-134",
        "01d758be9706c2534b80c2dd3a4234b9-139",
        "01d758be9706c2534b80c2dd3a4234b9-144",
        "01d758be9706c2534b80c2dd3a4234b9-149"
      ],
      "choices": {
        "A": "It would likely crash into another car or vehicle",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely drift too far laterally toward another lane or the road edge"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "01d758be9706c2534b80c2dd3a4234b9-149",
      "anchor_image_path": "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "01d758be9706c2534b80c2dd3a4234b9",
      "scenario_cluster": "Others",
      "bundle_id": "01d758be9706c2534b80c2dd3a4234b9:group_149",
      "image_paths": [
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-129_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-134_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-139_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-144_FRONT.jpg",
        "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-149_FRONT.jpg"
      ],
      "image_path": "01d758be9706c2534b80c2dd3a4234b9/CAM_FRONT/01d758be9706c2534b80c2dd3a4234b9-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            501.43,
            1046.21
          ],
          [
            496.18,
            787.01
          ],
          [
            492.11,
            753.99
          ],
          [
            483.1,
            743.11
          ],
          [
            463.95,
            735.58
          ]
        ],
        "B": [
          [
            500.86,
            1046.21
          ],
          [
            496.49,
            784.99
          ],
          [
            487.56,
            751.02
          ],
          [
            468.96,
            739.67
          ],
          [
            433.58,
            731.79
          ]
        ],
        "C": [
          [
            501.96,
            1069.14
          ],
          [
            495.82,
            787.62
          ],
          [
            487.43,
            753.01
          ],
          [
            476.31,
            741.22
          ],
          [
            451.06,
            732.93
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-128_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-133_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-138_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-143_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle acclerates straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            1.241,
            0.001
          ],
          [
            2.211,
            -0.004
          ],
          [
            2.668,
            -0.005
          ],
          [
            3.797,
            -0.005
          ],
          [
            3.833,
            -0.004
          ]
        ],
        "B": [
          [
            1.106,
            -0.002
          ],
          [
            2.943,
            -0.001
          ],
          [
            5.045,
            0.003
          ],
          [
            8.805,
            0.043
          ],
          [
            15.959,
            0.243
          ]
        ],
        "C": [
          [
            1.099,
            0.003
          ],
          [
            2.545,
            0.008
          ],
          [
            2.599,
            0.007
          ],
          [
            2.61,
            0.014
          ],
          [
            2.567,
            0.038
          ]
        ]
      },
      "question_id": "waymo_val_0441374eb46e1d94536cf7fce1237e9f_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "0441374eb46e1d94536cf7fce1237e9f-128",
        "0441374eb46e1d94536cf7fce1237e9f-133",
        "0441374eb46e1d94536cf7fce1237e9f-138",
        "0441374eb46e1d94536cf7fce1237e9f-143",
        "0441374eb46e1d94536cf7fce1237e9f-148"
      ],
      "choices": {
        "A": "It would likely crash into another car or vehicle",
        "B": "it would likely crash",
        "C": "It would likely hit a barrier, curb, cone, or roadside object",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "it would likely crash",
      "frame_name": "0441374eb46e1d94536cf7fce1237e9f-148",
      "anchor_image_path": "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0441374eb46e1d94536cf7fce1237e9f",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "0441374eb46e1d94536cf7fce1237e9f:group_148",
      "image_paths": [
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-128_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-133_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-138_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-143_FRONT.jpg",
        "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-148_FRONT.jpg"
      ],
      "image_path": "0441374eb46e1d94536cf7fce1237e9f/CAM_FRONT/0441374eb46e1d94536cf7fce1237e9f-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            515.01,
            3620.06
          ],
          [
            504.62,
            2467.91
          ],
          [
            495.59,
            1601.35
          ],
          [
            494.97,
            1587.62
          ]
        ],
        "B": [
          [
            497.98,
            2130.43
          ],
          [
            489.81,
            1288.79
          ],
          [
            481.93,
            994.15
          ],
          [
            468.69,
            857.14
          ]
        ],
        "C": [
          [
            492.7,
            2677.23
          ],
          [
            493.41,
            2579.45
          ],
          [
            486.2,
            2560.69
          ],
          [
            460.7,
            2636.05
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-127_FRONT.jpg",
        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-132_FRONT.jpg",
        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-137_FRONT.jpg",
        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-142_FRONT.jpg",
        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            4.351,
            -0.059
          ],
          [
            15.003,
            -0.455
          ],
          [
            25.865,
            -0.935
          ],
          [
            34.588,
            -1.202
          ],
          [
            45.295,
            -1.206
          ]
        ],
        "B": [
          [
            4.187,
            -0.033
          ],
          [
            11.624,
            -0.152
          ],
          [
            14.302,
            -0.153
          ],
          [
            14.905,
            -0.136
          ],
          [
            16.208,
            -0.161
          ]
        ],
        "C": [
          [
            4.371,
            -0.07
          ],
          [
            15.371,
            -0.583
          ],
          [
            27.221,
            -1.267
          ],
          [
            37.142,
            -1.574
          ],
          [
            49.703,
            -1.565
          ]
        ]
      },
      "question_id": "waymo_val_07b4716982f88d85c6e5b350883b2d9b_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "07b4716982f88d85c6e5b350883b2d9b-127",
        "07b4716982f88d85c6e5b350883b2d9b-132",
        "07b4716982f88d85c6e5b350883b2d9b-137",
        "07b4716982f88d85c6e5b350883b2d9b-142",
        "07b4716982f88d85c6e5b350883b2d9b-147"
      ],
      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely crash into another car or vehicle.",
        "D": "It would likely enter opposing traffic or create a head-on conflict"
      },
      "ground_truth_text": "It would likely crash into another car or vehicle.",
      "frame_name": "07b4716982f88d85c6e5b350883b2d9b-147",
      "anchor_image_path": "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "07b4716982f88d85c6e5b350883b2d9b",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "07b4716982f88d85c6e5b350883b2d9b:group_147",
      "image_paths": [
        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-127_FRONT.jpg",
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        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-142_FRONT.jpg",
        "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-147_FRONT.jpg"
      ],
      "image_path": "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            522.28,
            1420.05
          ],
          [
            530.21,
            862.44
          ],
          [
            534.63,
            796.12
          ],
          [
            532.1,
            774.38
          ],
          [
            522.15,
            759.52
          ]
        ],
        "B": [
          [
            513.41,
            1463.29
          ],
          [
            510.02,
            912.06
          ],
          [
            506.13,
            870.51
          ],
          [
            504.04,
            863.45
          ],
          [
            504.77,
            850.17
          ]
        ],
        "C": [
          [
            526.34,
            1415.13
          ],
          [
            539.43,
            858.52
          ],
          [
            546.68,
            791.81
          ],
          [
            540.75,
            770.06
          ],
          [
            527.58,
            755.34
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-129_FRONT.jpg",
        "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-134_FRONT.jpg",
        "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-139_FRONT.jpg",
        "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-144_FRONT.jpg",
        "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            3.873,
            -0.079
          ],
          [
            14.122,
            -0.845
          ],
          [
            24.523,
            -2.117
          ],
          [
            32.891,
            -3.34
          ],
          [
            43.324,
            -4.997
          ]
        ],
        "B": [
          [
            3.513,
            -0.057
          ],
          [
            13.692,
            -0.91
          ],
          [
            23.799,
            -2.44
          ],
          [
            31.889,
            -3.8
          ],
          [
            42.009,
            -5.504
          ]
        ],
        "C": [
          [
            3.84,
            -0.082
          ],
          [
            12.929,
            -0.806
          ],
          [
            19.67,
            -1.728
          ],
          [
            23.648,
            -2.345
          ],
          [
            27.97,
            -3.022
          ]
        ]
      },
      "question_id": "waymo_val_0c8bd5520f4262ef6c37e79913c82e8d_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "0c8bd5520f4262ef6c37e79913c82e8d-129",
        "0c8bd5520f4262ef6c37e79913c82e8d-134",
        "0c8bd5520f4262ef6c37e79913c82e8d-139",
        "0c8bd5520f4262ef6c37e79913c82e8d-144",
        "0c8bd5520f4262ef6c37e79913c82e8d-149"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely to crash with a car.",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely crash into another car or vehicle"
      },
      "ground_truth_text": "It wou",
      "frame_name": "0c8bd5520f4262ef6c37e79913c82e8d-149",
      "anchor_image_path": "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0c8bd5520f4262ef6c37e79913c82e8d",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "0c8bd5520f4262ef6c37e79913c82e8d:group_149",
      "image_paths": [
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        "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-149_FRONT.jpg"
      ],
      "image_path": "0c8bd5520f4262ef6c37e79913c82e8d/CAM_FRONT/0c8bd5520f4262ef6c37e79913c82e8d-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            544.3,
            1569.23
          ],
          [
            570.34,
            875.53
          ],
          [
            597.02,
            803.31
          ],
          [
            612.74,
            779.94
          ],
          [
            627.06,
            763.9
          ]
        ],
        "B": [
          [
            541.27,
            1722.89
          ],
          [
            579.02,
            881.16
          ],
          [
            616.57,
            806.12
          ],
          [
            633.59,
            782.02
          ],
          [
            645.38,
            765.43
          ]
        ],
        "C": [
          [
            546.47,
            1581.33
          ],
          [
            574.62,
            892.22
          ],
          [
            600.94,
            826.69
          ],
          [
            612.62,
            806.74
          ],
          [
            621.62,
            791.86
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-129_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-134_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-139_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-144_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            4.405,
            -0.015
          ],
          [
            15.37,
            -0.107
          ],
          [
            26.994,
            -0.276
          ],
          [
            36.665,
            -0.499
          ],
          [
            48.67,
            -0.732
          ]
        ],
        "B": [
          [
            4.39,
            -0.016
          ],
          [
            15.019,
            -0.121
          ],
          [
            25.764,
            -0.301
          ],
          [
            34.431,
            -0.451
          ],
          [
            45.116,
            -0.708
          ]
        ],
        "C": [
          [
            4.359,
            -0.012
          ],
          [
            13.862,
            -0.025
          ],
          [
            20.502,
            -0.023
          ],
          [
            23.595,
            -0.028
          ],
          [
            25.747,
            -0.045
          ]
        ]
      },
      "question_id": "waymo_val_0d94d2a36405c21b1fc19601c41e8d23_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "0d94d2a36405c21b1fc19601c41e8d23-129",
        "0d94d2a36405c21b1fc19601c41e8d23-134",
        "0d94d2a36405c21b1fc19601c41e8d23-139",
        "0d94d2a36405c21b1fc19601c41e8d23-144",
        "0d94d2a36405c21b1fc19601c41e8d23-149"
      ],
      "choices": {
        "A": "It would likely hit a barrier, curb, cone, or roadside object",
        "B": "It would likely crash into another car or vehicle",
        "C": "It may cross the double yellow line and enter the opposite lane",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "0d94d2a36405c21b1fc19601c41e8d23-149",
      "anchor_image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0d94d2a36405c21b1fc19601c41e8d23",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "0d94d2a36405c21b1fc19601c41e8d23:group_149",
      "image_paths": [
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        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-139_FRONT.jpg",
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        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg"
      ],
      "image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            498.52,
            1410.9
          ],
          [
            496.57,
            861.0
          ],
          [
            499.46,
            794.87
          ],
          [
            503.01,
            773.2
          ],
          [
            504.36,
            758.66
          ]
        ],
        "B": [
          [
            498.97,
            1414.52
          ],
          [
            497.98,
            864.77
          ],
          [
            501.25,
            798.88
          ],
          [
            502.49,
            777.07
          ],
          [
            505.19,
            762.13
          ]
        ],
        "C": [
          [
            497.54,
            1422.13
          ],
          [
            490.39,
            878.67
          ],
          [
            488.99,
            821.77
          ],
          [
            488.93,
            806.95
          ],
          [
            489.51,
            798.87
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-127_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-132_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-137_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-142_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle slightly veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            4.557,
            0.038
          ],
          [
            16.306,
            0.266
          ],
          [
            28.423,
            0.211
          ],
          [
            38.045,
            -0.098
          ],
          [
            49.767,
            -0.646
          ]
        ],
        "B": [
          [
            4.521,
            0.049
          ],
          [
            15.72,
            0.41
          ],
          [
            25.807,
            0.647
          ],
          [
            32.385,
            0.612
          ],
          [
            38.746,
            0.434
          ]
        ],
        "C": [
          [
            4.568,
            0.041
          ],
          [
            16.546,
            0.304
          ],
          [
            29.329,
            0.252
          ],
          [
            39.797,
            -0.174
          ],
          [
            52.824,
            -1.029
          ]
        ]
      },
      "question_id": "waymo_val_1a13b64b69b568c4ccbb572bc3f0257b_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "1a13b64b69b568c4ccbb572bc3f0257b-127",
        "1a13b64b69b568c4ccbb572bc3f0257b-132",
        "1a13b64b69b568c4ccbb572bc3f0257b-137",
        "1a13b64b69b568c4ccbb572bc3f0257b-142",
        "1a13b64b69b568c4ccbb572bc3f0257b-147"
      ],
      "choices": {
        "A": "It would likely enter opposing traffic or create a head-on conflict",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely cause the ego vehicle to drift out of its lane toward the right curb or roadside area."
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "1a13b64b69b568c4ccbb572bc3f0257b-147",
      "anchor_image_path": "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "1a13b64b69b568c4ccbb572bc3f0257b",
      "scenario_cluster": "Cyclist",
      "bundle_id": "1a13b64b69b568c4ccbb572bc3f0257b:group_147",
      "image_paths": [
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-127_FRONT.jpg",
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        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-137_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-142_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg"
      ],
      "image_path": "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            490.89,
            1376.54
          ],
          [
            475.93,
            850.69
          ],
          [
            486.22,
            789.29
          ],
          [
            497.64,
            769.53
          ],
          [
            509.37,
            756.09
          ]
        ],
        "B": [
          [
            486.77,
            1384.46
          ],
          [
            463.88,
            856.34
          ],
          [
            465.38,
            797.41
          ],
          [
            472.68,
            779.71
          ],
          [
            481.61,
            768.53
          ]
        ],
        "C": [
          [
            489.8,
            1374.16
          ],
          [
            473.39,
            848.52
          ],
          [
            484.82,
            786.86
          ],
          [
            499.68,
            767.0
          ],
          [
            516.77,
            753.59
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-128_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-133_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-138_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-143_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle stops, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.002,
            0.0
          ],
          [
            1.27,
            -0.002
          ],
          [
            6.508,
            -0.02
          ],
          [
            13.518,
            -0.037
          ],
          [
            25.317,
            -0.002
          ]
        ],
        "B": [
          [
            0.021,
            0.0
          ],
          [
            1.151,
            -0.004
          ],
          [
            5.821,
            -0.065
          ],
          [
            12.104,
            -0.214
          ],
          [
            22.822,
            -0.48
          ]
        ],
        "C": [
          [
            0.025,
            0.0
          ],
          [
            1.481,
            0.001
          ],
          [
            7.09,
            -0.026
          ],
          [
            14.353,
            -0.097
          ],
          [
            26.244,
            -0.203
          ]
        ]
      },
      "question_id": "waymo_val_1c606b71cbbb08251345d152bd5d4d4b_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "1c606b71cbbb08251345d152bd5d4d4b-128",
        "1c606b71cbbb08251345d152bd5d4d4b-133",
        "1c606b71cbbb08251345d152bd5d4d4b-138",
        "1c606b71cbbb08251345d152bd5d4d4b-143",
        "1c606b71cbbb08251345d152bd5d4d4b-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely block the lane and disrupt traffic flow",
        "C": "It would likely hit a barrier, curb, cone, or roadside object",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "1c606b71cbbb08251345d152bd5d4d4b-148",
      "anchor_image_path": "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "1c606b71cbbb08251345d152bd5d4d4b",
      "scenario_cluster": "Others",
      "bundle_id": "1c606b71cbbb08251345d152bd5d4d4b:group_148",
      "image_paths": [
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-128_FRONT.jpg",
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        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-138_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-143_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-148_FRONT.jpg"
      ],
      "image_path": "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            500.01,
            1111.29
          ],
          [
            496.01,
            876.99
          ],
          [
            491.62,
            794.11
          ]
        ],
        "B": [
          [
            513.13,
            1175.22
          ],
          [
            515.32,
            899.33
          ],
          [
            516.7,
            804.03
          ]
        ],
        "C": [
          [
            500.21,
            1069.45
          ],
          [
            500.84,
            866.13
          ],
          [
            500.61,
            790.96
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-127_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-132_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-137_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-142_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle slows down or nearly stops, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.403,
            0.001
          ],
          [
            1.435,
            0.004
          ],
          [
            5.025,
            -0.023
          ],
          [
            9.33,
            -0.105
          ],
          [
            15.105,
            -0.252
          ]
        ],
        "B": [
          [
            0.38,
            0.001
          ],
          [
            0.588,
            0.003
          ],
          [
            0.939,
            0.013
          ],
          [
            1.24,
            0.02
          ],
          [
            1.819,
            0.028
          ]
        ],
        "C": [
          [
            0.306,
            -0.001
          ],
          [
            0.891,
            -0.004
          ],
          [
            2.955,
            0.007
          ],
          [
            6.252,
            0.088
          ],
          [
            12.751,
            0.321
          ]
        ]
      },
      "question_id": "waymo_val_241e6e041eacfe34b21cde7b630c270c_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "241e6e041eacfe34b21cde7b630c270c-127",
        "241e6e041eacfe34b21cde7b630c270c-132",
        "241e6e041eacfe34b21cde7b630c270c-137",
        "241e6e041eacfe34b21cde7b630c270c-142",
        "241e6e041eacfe34b21cde7b630c270c-147"
      ],
      "choices": {
        "A": "It would block the pedestrain walking.",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "241e6e041eacfe34b21cde7b630c270c-147",
      "anchor_image_path": "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "241e6e041eacfe34b21cde7b630c270c",
      "scenario_cluster": "Cyclist",
      "bundle_id": "241e6e041eacfe34b21cde7b630c270c:group_147",
      "image_paths": [
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-127_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-132_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-137_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-142_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg"
      ],
      "image_path": "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            502.6,
            1290.28
          ],
          [
            506.93,
            974.01
          ],
          [
            511.47,
            864.41
          ]
        ],
        "B": [
          [
            456.78,
            7325.28
          ]
        ],
        "C": [
          [
            498.61,
            2115.16
          ],
          [
            472.98,
            1141.45
          ],
          [
            459.22,
            895.29
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-129_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-134_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-139_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-144_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle slightly veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            4.209,
            -0.025
          ],
          [
            11.929,
            -0.141
          ],
          [
            17.999,
            -0.239
          ],
          [
            22.686,
            -0.463
          ],
          [
            29.196,
            -1.308
          ]
        ],
        "B": [
          [
            4.284,
            -0.008
          ],
          [
            13.375,
            -0.135
          ],
          [
            22.894,
            -0.325
          ],
          [
            31.041,
            -0.497
          ],
          [
            41.402,
            -0.747
          ]
        ],
        "C": [
          [
            4.094,
            -0.0
          ],
          [
            9.756,
            -0.02
          ],
          [
            12.278,
            -0.035
          ],
          [
            14.617,
            -0.048
          ],
          [
            19.524,
            -0.053
          ]
        ]
      },
      "question_id": "waymo_val_27b862196b866184a3c6b28dd1cdfbba_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "27b862196b866184a3c6b28dd1cdfbba-129",
        "27b862196b866184a3c6b28dd1cdfbba-134",
        "27b862196b866184a3c6b28dd1cdfbba-139",
        "27b862196b866184a3c6b28dd1cdfbba-144",
        "27b862196b866184a3c6b28dd1cdfbba-149"
      ],
      "choices": {
        "A": "It would likely crash into another car or vehicle",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely hit a barrier, curb, cone, or roadside object",
        "D": "It would likely drift too far laterally toward another lane or the road edge"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "27b862196b866184a3c6b28dd1cdfbba-149",
      "anchor_image_path": "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "27b862196b866184a3c6b28dd1cdfbba",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "27b862196b866184a3c6b28dd1cdfbba:group_149",
      "image_paths": [
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-129_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-134_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-139_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-144_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg"
      ],
      "image_path": "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            500.45,
            1468.77
          ],
          [
            502.57,
            913.55
          ],
          [
            503.3,
            842.33
          ],
          [
            511.39,
            815.36
          ],
          [
            539.56,
            793.26
          ]
        ],
        "B": [
          [
            493.22,
            1448.39
          ],
          [
            500.05,
            889.94
          ],
          [
            503.95,
            814.36
          ],
          [
            505.65,
            788.39
          ],
          [
            507.67,
            770.7
          ]
        ],
        "C": [
          [
            490.25,
            1502.13
          ],
          [
            490.42,
            964.45
          ],
          [
            491.07,
            907.17
          ],
          [
            491.37,
            873.76
          ],
          [
            490.37,
            831.86
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-128_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-133_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-138_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-143_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            2.407,
            0.004
          ],
          [
            8.423,
            0.014
          ],
          [
            14.415,
            -0.01
          ],
          [
            18.701,
            -0.246
          ],
          [
            23.084,
            -0.984
          ]
        ],
        "B": [
          [
            2.419,
            0.008
          ],
          [
            8.494,
            0.116
          ],
          [
            15.119,
            0.301
          ],
          [
            20.614,
            0.367
          ],
          [
            27.407,
            0.344
          ]
        ],
        "C": [
          [
            2.007,
            0.004
          ],
          [
            8.161,
            0.022
          ],
          [
            14.586,
            0.065
          ],
          [
            19.614,
            0.068
          ],
          [
            25.436,
            0.08
          ]
        ]
      },
      "question_id": "waymo_val_349479e335692cf038b24348f274d575_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "349479e335692cf038b24348f274d575-128",
        "349479e335692cf038b24348f274d575-133",
        "349479e335692cf038b24348f274d575-138",
        "349479e335692cf038b24348f274d575-143",
        "349479e335692cf038b24348f274d575-148"
      ],
      "choices": {
        "A": "It would likely crash into another car or vehicle",
        "B": "It would likely enter opposing traffic or create a head-on conflict",
        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "349479e335692cf038b24348f274d575-148",
      "anchor_image_path": "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "349479e335692cf038b24348f274d575",
      "scenario_cluster": "Intersection",
      "bundle_id": "349479e335692cf038b24348f274d575:group_148",
      "image_paths": [
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-128_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-133_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-138_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-143_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg"
      ],
      "image_path": "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            502.4,
            2979.93
          ],
          [
            488.32,
            1009.41
          ],
          [
            490.28,
            874.01
          ],
          [
            505.04,
            835.18
          ],
          [
            539.72,
            811.57
          ]
        ],
        "B": [
          [
            497.26,
            2949.81
          ],
          [
            472.02,
            1006.35
          ],
          [
            464.69,
            865.79
          ],
          [
            467.57,
            823.23
          ],
          [
            473.94,
            795.61
          ]
        ],
        "C": [
          [
            514.08,
            4826.97
          ],
          [
            486.98,
            1020.89
          ],
          [
            483.86,
            871.94
          ],
          [
            484.85,
            829.15
          ],
          [
            485.08,
            802.08
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-128_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-133_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-138_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-143_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle slightly veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            4.305,
            0.11
          ],
          [
            13.193,
            0.642
          ],
          [
            17.618,
            0.932
          ],
          [
            18.953,
            1.014
          ],
          [
            19.881,
            1.068
          ]
        ],
        "B": [
          [
            4.321,
            0.133
          ],
          [
            14.342,
            1.124
          ],
          [
            23.593,
            2.301
          ],
          [
            31.531,
            2.999
          ],
          [
            42.634,
            3.508
          ]
        ],
        "C": [
          [
            4.392,
            0.108
          ],
          [
            16.1,
            0.946
          ],
          [
            28.937,
            1.783
          ],
          [
            39.68,
            2.364
          ],
          [
            53.43,
            3.149
          ]
        ]
      },
      "question_id": "waymo_val_3863d816ff1b1aa7a40de54c4ba821eb_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "3863d816ff1b1aa7a40de54c4ba821eb-128",
        "3863d816ff1b1aa7a40de54c4ba821eb-133",
        "3863d816ff1b1aa7a40de54c4ba821eb-138",
        "3863d816ff1b1aa7a40de54c4ba821eb-143",
        "3863d816ff1b1aa7a40de54c4ba821eb-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely crash into another car or vehicle",
        "C": "It would likely hit a barrier, curb, cone, or roadside object",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "3863d816ff1b1aa7a40de54c4ba821eb-148",
      "anchor_image_path": "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "3863d816ff1b1aa7a40de54c4ba821eb",
      "scenario_cluster": "Cyclist",
      "bundle_id": "3863d816ff1b1aa7a40de54c4ba821eb:group_148",
      "image_paths": [
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-128_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-133_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-138_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-143_FRONT.jpg",
        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-148_FRONT.jpg"
      ],
      "image_path": "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            452.06,
            1440.02
          ],
          [
            430.21,
            890.81
          ],
          [
            426.59,
            843.48
          ],
          [
            426.21,
            833.92
          ],
          [
            426.15,
            828.1
          ]
        ],
        "B": [
          [
            443.15,
            1435.84
          ],
          [
            393.75,
            875.17
          ],
          [
            374.76,
            809.39
          ],
          [
            379.35,
            785.34
          ],
          [
            395.44,
            767.36
          ]
        ],
        "C": [
          [
            453.98,
            1418.2
          ],
          [
            418.92,
            856.43
          ],
          [
            418.21,
            791.91
          ],
          [
            421.47,
            771.3
          ],
          [
            422.78,
            757.36
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-128_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-133_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-138_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-143_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle slightly veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            5.305,
            -0.003
          ],
          [
            17.461,
            0.003
          ],
          [
            28.773,
            0.202
          ],
          [
            37.377,
            0.515
          ],
          [
            48.068,
            0.894
          ]
        ],
        "B": [
          [
            5.34,
            -0.005
          ],
          [
            18.292,
            0.024
          ],
          [
            31.618,
            0.187
          ],
          [
            42.272,
            0.356
          ],
          [
            55.409,
            0.535
          ]
        ],
        "C": [
          [
            5.309,
            -0.008
          ],
          [
            17.718,
            -0.027
          ],
          [
            30.046,
            0.035
          ],
          [
            39.876,
            0.136
          ],
          [
            52.011,
            0.27
          ]
        ]
      },
      "question_id": "waymo_val_3c7eb4b2fd0190520cff3f6fd63deba7_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "3c7eb4b2fd0190520cff3f6fd63deba7-128",
        "3c7eb4b2fd0190520cff3f6fd63deba7-133",
        "3c7eb4b2fd0190520cff3f6fd63deba7-138",
        "3c7eb4b2fd0190520cff3f6fd63deba7-143",
        "3c7eb4b2fd0190520cff3f6fd63deba7-148"
      ],
      "choices": {
        "A": "It would likely crash into another car or vehicle",
        "B": "It would likely drift into the dedicated bus/bike lane.",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "3c7eb4b2fd0190520cff3f6fd63deba7-148",
      "anchor_image_path": "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "3c7eb4b2fd0190520cff3f6fd63deba7",
      "scenario_cluster": "Intersection",
      "bundle_id": "3c7eb4b2fd0190520cff3f6fd63deba7:group_148",
      "image_paths": [
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-128_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-133_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-138_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-143_FRONT.jpg",
        "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-148_FRONT.jpg"
      ],
      "image_path": "3c7eb4b2fd0190520cff3f6fd63deba7/CAM_FRONT/3c7eb4b2fd0190520cff3f6fd63deba7-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            504.36,
            1241.87
          ],
          [
            496.27,
            839.08
          ],
          [
            487.34,
            786.92
          ],
          [
            479.31,
            769.3
          ],
          [
            473.71,
            756.47
          ]
        ],
        "B": [
          [
            504.85,
            1237.04
          ],
          [
            494.78,
            832.86
          ],
          [
            488.55,
            779.97
          ],
          [
            485.44,
            762.56
          ],
          [
            483.91,
            750.57
          ]
        ],
        "C": [
          [
            505.81,
            1241.31
          ],
          [
            498.29,
            837.08
          ],
          [
            494.15,
            783.62
          ],
          [
            491.26,
            765.62
          ],
          [
            489.04,
            753.06
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-129_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-134_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-139_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-144_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.847,
            0.001
          ],
          [
            2.035,
            0.005
          ],
          [
            2.906,
            0.011
          ],
          [
            3.726,
            0.017
          ],
          [
            4.115,
            0.021
          ]
        ],
        "B": [
          [
            0.95,
            0.002
          ],
          [
            4.133,
            0.269
          ],
          [
            9.366,
            1.601
          ],
          [
            14.937,
            2.745
          ],
          [
            23.491,
            3.165
          ]
        ],
        "C": [
          [
            0.894,
            0.001
          ],
          [
            2.95,
            0.007
          ],
          [
            5.176,
            0.022
          ],
          [
            7.664,
            0.035
          ],
          [
            11.497,
            0.048
          ]
        ]
      },
      "question_id": "waymo_val_4758699520985474f05538b7856de1b7_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "4758699520985474f05538b7856de1b7-129",
        "4758699520985474f05538b7856de1b7-134",
        "4758699520985474f05538b7856de1b7-139",
        "4758699520985474f05538b7856de1b7-144",
        "4758699520985474f05538b7856de1b7-149"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "4758699520985474f05538b7856de1b7-149",
      "anchor_image_path": "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "4758699520985474f05538b7856de1b7",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "4758699520985474f05538b7856de1b7:group_149",
      "image_paths": [
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-129_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-134_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-139_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-144_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg"
      ],
      "image_path": "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            483.79,
            4459.24
          ],
          [
            487.12,
            2134.29
          ],
          [
            487.7,
            1608.63
          ],
          [
            487.36,
            1474.56
          ]
        ],
        "B": [
          [
            382.95,
            1467.21
          ],
          [
            271.07,
            958.95
          ],
          [
            270.32,
            853.39
          ],
          [
            337.18,
            796.47
          ]
        ],
        "C": [
          [
            490.48,
            2091.07
          ],
          [
            489.78,
            1253.21
          ],
          [
            490.28,
            1033.0
          ],
          [
            491.34,
            908.27
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-133_FRONT.jpg",
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-138_FRONT.jpg",
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-143_FRONT.jpg",
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            2.038,
            -0.002
          ],
          [
            6.698,
            0.005
          ],
          [
            10.071,
            0.014
          ],
          [
            11.693,
            0.017
          ],
          [
            12.401,
            0.019
          ]
        ],
        "B": [
          [
            2.072,
            0.015
          ],
          [
            6.608,
            0.034
          ],
          [
            9.27,
            0.068
          ],
          [
            10.156,
            0.082
          ],
          [
            10.396,
            0.086
          ]
        ],
        "C": [
          [
            2.078,
            -0.001
          ],
          [
            6.519,
            -0.006
          ],
          [
            9.5,
            -0.023
          ],
          [
            10.996,
            -0.055
          ],
          [
            12.173,
            -0.108
          ]
        ]
      },
      "question_id": "waymo_val_5125dfd998e61449fe0c1f4243db286f_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "5125dfd998e61449fe0c1f4243db286f-128",
        "5125dfd998e61449fe0c1f4243db286f-133",
        "5125dfd998e61449fe0c1f4243db286f-138",
        "5125dfd998e61449fe0c1f4243db286f-143",
        "5125dfd998e61449fe0c1f4243db286f-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely enter opposing traffic or create a head-on conflict",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "5125dfd998e61449fe0c1f4243db286f-148",
      "anchor_image_path": "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5125dfd998e61449fe0c1f4243db286f",
      "scenario_cluster": "Construction",
      "bundle_id": "5125dfd998e61449fe0c1f4243db286f:group_148",
      "image_paths": [
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-128_FRONT.jpg",
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-133_FRONT.jpg",
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-138_FRONT.jpg",
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-143_FRONT.jpg",
        "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-148_FRONT.jpg"
      ],
      "image_path": "5125dfd998e61449fe0c1f4243db286f/CAM_FRONT/5125dfd998e61449fe0c1f4243db286f-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            526.7,
            4622.02
          ],
          [
            493.71,
            1109.7
          ],
          [
            491.76,
            956.48
          ],
          [
            491.43,
            918.99
          ],
          [
            491.25,
            906.13
          ]
        ],
        "B": [
          [
            490.1,
            4379.93
          ],
          [
            487.41,
            1116.52
          ],
          [
            484.01,
            980.73
          ],
          [
            483.0,
            954.11
          ],
          [
            482.74,
            947.81
          ]
        ],
        "C": [
          [
            522.18,
            4340.94
          ],
          [
            496.23,
            1123.63
          ],
          [
            496.92,
            973.35
          ],
          [
            499.86,
            933.59
          ],
          [
            504.5,
            910.18
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-128_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-133_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-138_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-143_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle slows down or nearly stops, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.029,
            0.0
          ],
          [
            1.451,
            0.005
          ],
          [
            5.158,
            0.031
          ],
          [
            8.136,
            -0.176
          ],
          [
            10.929,
            -1.341
          ]
        ],
        "B": [
          [
            0.057,
            0.001
          ],
          [
            2.184,
            0.02
          ],
          [
            7.48,
            -0.442
          ],
          [
            11.64,
            -2.811
          ],
          [
            14.56,
            -9.947
          ]
        ],
        "C": [
          [
            0.018,
            -0.002
          ],
          [
            1.131,
            0.004
          ],
          [
            4.231,
            0.048
          ],
          [
            6.749,
            0.029
          ],
          [
            8.98,
            -0.347
          ]
        ]
      },
      "question_id": "waymo_val_517edb9d2be14368685cc9f6184e9afe_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "517edb9d2be14368685cc9f6184e9afe-128",
        "517edb9d2be14368685cc9f6184e9afe-133",
        "517edb9d2be14368685cc9f6184e9afe-138",
        "517edb9d2be14368685cc9f6184e9afe-143",
        "517edb9d2be14368685cc9f6184e9afe-148"
      ],
      "choices": {
        "A": "It would likely block the traffic",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "517edb9d2be14368685cc9f6184e9afe-148",
      "anchor_image_path": "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "517edb9d2be14368685cc9f6184e9afe",
      "scenario_cluster": "Cyclist",
      "bundle_id": "517edb9d2be14368685cc9f6184e9afe:group_148",
      "image_paths": [
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-128_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-133_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-138_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-143_FRONT.jpg",
        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-148_FRONT.jpg"
      ],
      "image_path": "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            484.91,
            1268.87
          ],
          [
            521.52,
            1020.68
          ],
          [
            649.67,
            931.11
          ]
        ],
        "B": [
          [
            485.48,
            3724.04
          ],
          [
            574.77,
            1054.33
          ],
          [
            800.09,
            916.78
          ],
          [
            1339.99,
            874.7
          ]
        ],
        "C": [
          [
            476.58,
            1457.3
          ],
          [
            486.55,
            1101.03
          ],
          [
            543.34,
            986.43
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-127_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-132_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-137_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-142_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.211,
            -0.002
          ],
          [
            0.697,
            -0.003
          ],
          [
            0.798,
            -0.004
          ],
          [
            0.897,
            -0.005
          ],
          [
            1.102,
            -0.005
          ]
        ],
        "B": [
          [
            0.405,
            -0.001
          ],
          [
            4.618,
            0.04
          ],
          [
            14.381,
            0.522
          ],
          [
            25.713,
            1.291
          ],
          [
            42.875,
            2.462
          ]
        ],
        "C": [
          [
            0.125,
            -0.001
          ],
          [
            0.457,
            -0.002
          ],
          [
            2.618,
            -0.01
          ],
          [
            8.101,
            0.131
          ],
          [
            20.285,
            0.569
          ]
        ]
      },
      "question_id": "waymo_val_57e34daab2df82dfd78edad9dfc8d56d_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "57e34daab2df82dfd78edad9dfc8d56d-127",
        "57e34daab2df82dfd78edad9dfc8d56d-132",
        "57e34daab2df82dfd78edad9dfc8d56d-137",
        "57e34daab2df82dfd78edad9dfc8d56d-142",
        "57e34daab2df82dfd78edad9dfc8d56d-147"
      ],
      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely enter opposing traffic or create a head-on conflict"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "57e34daab2df82dfd78edad9dfc8d56d-147",
      "anchor_image_path": "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "57e34daab2df82dfd78edad9dfc8d56d",
      "scenario_cluster": "Intersection",
      "bundle_id": "57e34daab2df82dfd78edad9dfc8d56d:group_147",
      "image_paths": [
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        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-132_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-137_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-142_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg"
      ],
      "image_path": "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [],
        "B": [
          [
            479.35,
            1363.31
          ],
          [
            444.57,
            871.77
          ],
          [
            429.75,
            798.52
          ],
          [
            422.6,
            764.01
          ]
        ],
        "C": [
          [
            513.24,
            2536.28
          ],
          [
            468.79,
            1020.85
          ],
          [
            455.58,
            822.7
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-129_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-134_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-139_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-144_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            6.48,
            0.02
          ],
          [
            25.958,
            0.75
          ],
          [
            46.168,
            2.814
          ],
          [
            62.271,
            5.1
          ],
          [
            81.86,
            8.082
          ]
        ],
        "B": [
          [
            6.421,
            0.014
          ],
          [
            25.916,
            0.509
          ],
          [
            46.47,
            1.742
          ],
          [
            63.52,
            2.946
          ],
          [
            85.502,
            4.479
          ]
        ],
        "C": [
          [
            6.487,
            0.011
          ],
          [
            25.449,
            0.328
          ],
          [
            44.09,
            1.12
          ],
          [
            58.397,
            1.97
          ],
          [
            75.315,
            3.098
          ]
        ]
      },
      "question_id": "waymo_val_57e3f4d557f856f3b40d35329670f58f_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "57e3f4d557f856f3b40d35329670f58f-129",
        "57e3f4d557f856f3b40d35329670f58f-134",
        "57e3f4d557f856f3b40d35329670f58f-139",
        "57e3f4d557f856f3b40d35329670f58f-144",
        "57e3f4d557f856f3b40d35329670f58f-149"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "57e3f4d557f856f3b40d35329670f58f-149",
      "anchor_image_path": "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "57e3f4d557f856f3b40d35329670f58f",
      "scenario_cluster": "Construction",
      "bundle_id": "57e3f4d557f856f3b40d35329670f58f:group_149",
      "image_paths": [
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        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-139_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-144_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg"
      ],
      "image_path": "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            496.41,
            1119.4
          ],
          [
            460.18,
            796.72
          ],
          [
            423.39,
            759.66
          ],
          [
            399.84,
            747.85
          ],
          [
            381.13,
            739.88
          ]
        ],
        "B": [
          [
            497.82,
            1124.26
          ],
          [
            471.13,
            796.8
          ],
          [
            450.43,
            759.2
          ],
          [
            440.42,
            746.94
          ],
          [
            433.76,
            738.49
          ]
        ],
        "C": [
          [
            498.42,
            1118.82
          ],
          [
            479.14,
            798.36
          ],
          [
            464.35,
            761.62
          ],
          [
            454.83,
            749.78
          ],
          [
            446.48,
            741.72
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-132_FRONT.jpg",
        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-137_FRONT.jpg",
        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-142_FRONT.jpg",
        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            3.148,
            -0.03
          ],
          [
            9.423,
            -0.25
          ],
          [
            14.114,
            -0.574
          ],
          [
            16.658,
            -0.807
          ],
          [
            18.353,
            -0.979
          ]
        ],
        "B": [
          [
            3.158,
            -0.033
          ],
          [
            10.045,
            -0.369
          ],
          [
            17.732,
            -1.12
          ],
          [
            25.234,
            -1.991
          ],
          [
            36.159,
            -3.356
          ]
        ],
        "C": [
          [
            3.145,
            -0.033
          ],
          [
            9.587,
            -0.322
          ],
          [
            15.634,
            -0.874
          ],
          [
            21.151,
            -1.495
          ],
          [
            29.474,
            -2.517
          ]
        ]
      },
      "question_id": "waymo_val_605a964cc30b61fedf8a41bdf130f505_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "605a964cc30b61fedf8a41bdf130f505-127",
        "605a964cc30b61fedf8a41bdf130f505-132",
        "605a964cc30b61fedf8a41bdf130f505-137",
        "605a964cc30b61fedf8a41bdf130f505-142",
        "605a964cc30b61fedf8a41bdf130f505-147"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "605a964cc30b61fedf8a41bdf130f505-147",
      "anchor_image_path": "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "605a964cc30b61fedf8a41bdf130f505",
      "scenario_cluster": "Cyclist",
      "bundle_id": "605a964cc30b61fedf8a41bdf130f505:group_147",
      "image_paths": [
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        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-132_FRONT.jpg",
        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-137_FRONT.jpg",
        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-142_FRONT.jpg",
        "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-147_FRONT.jpg"
      ],
      "image_path": "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            524.46,
            1928.82
          ],
          [
            531.14,
            962.05
          ],
          [
            545.87,
            867.77
          ],
          [
            554.26,
            841.08
          ],
          [
            559.54,
            827.77
          ]
        ],
        "B": [
          [
            526.29,
            1921.48
          ],
          [
            543.93,
            943.67
          ],
          [
            571.73,
            832.41
          ],
          [
            587.98,
            793.36
          ],
          [
            602.16,
            766.73
          ]
        ],
        "C": [
          [
            526.55,
            1931.07
          ],
          [
            540.3,
            956.97
          ],
          [
            563.91,
            850.75
          ],
          [
            579.43,
            810.91
          ],
          [
            594.65,
            780.55
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-127_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-132_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-137_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-142_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            4.244,
            -0.292
          ],
          [
            16.708,
            -2.17
          ],
          [
            31.313,
            -4.329
          ],
          [
            43.709,
            -6.03
          ],
          [
            59.753,
            -8.174
          ]
        ],
        "B": [
          [
            4.275,
            -0.255
          ],
          [
            17.766,
            -2.127
          ],
          [
            35.552,
            -4.288
          ],
          [
            51.704,
            -5.918
          ],
          [
            73.093,
            -7.9
          ]
        ],
        "C": [
          [
            3.671,
            -0.182
          ],
          [
            16.174,
            -2.071
          ],
          [
            31.526,
            -4.469
          ],
          [
            45.258,
            -6.25
          ],
          [
            63.651,
            -8.492
          ]
        ]
      },
      "question_id": "waymo_val_7331710f59abdde8b10bce1c48d49b3f_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "7331710f59abdde8b10bce1c48d49b3f-127",
        "7331710f59abdde8b10bce1c48d49b3f-132",
        "7331710f59abdde8b10bce1c48d49b3f-137",
        "7331710f59abdde8b10bce1c48d49b3f-142",
        "7331710f59abdde8b10bce1c48d49b3f-147"
      ],
      "choices": {
        "A": "It would likely crash into another car or vehicle",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "7331710f59abdde8b10bce1c48d49b3f-147",
      "anchor_image_path": "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7331710f59abdde8b10bce1c48d49b3f",
      "scenario_cluster": "Intersection",
      "bundle_id": "7331710f59abdde8b10bce1c48d49b3f:group_147",
      "image_paths": [
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-127_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-132_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-137_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-142_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg"
      ],
      "image_path": "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            620.73,
            1448.42
          ],
          [
            653.8,
            846.23
          ],
          [
            655.81,
            781.48
          ],
          [
            652.95,
            761.68
          ],
          [
            649.97,
            748.56
          ]
        ],
        "B": [
          [
            604.42,
            1440.09
          ],
          [
            640.43,
            837.58
          ],
          [
            634.25,
            773.03
          ],
          [
            625.07,
            754.02
          ],
          [
            616.54,
            742.04
          ]
        ],
        "C": [
          [
            597.86,
            1643.02
          ],
          [
            652.13,
            851.03
          ],
          [
            659.84,
            781.02
          ],
          [
            652.9,
            760.0
          ],
          [
            645.84,
            746.39
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-128_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-133_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-138_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-143_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle goes straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.215,
            -0.005
          ],
          [
            0.9,
            -0.076
          ],
          [
            1.825,
            -0.281
          ],
          [
            2.576,
            -0.461
          ],
          [
            3.326,
            -0.601
          ]
        ],
        "B": [
          [
            0.221,
            -0.005
          ],
          [
            0.972,
            -0.069
          ],
          [
            2.162,
            -0.229
          ],
          [
            3.305,
            -0.347
          ],
          [
            4.629,
            -0.416
          ]
        ],
        "C": [
          [
            0.127,
            -0.001
          ],
          [
            0.106,
            -0.002
          ],
          [
            0.02,
            0.006
          ],
          [
            -0.019,
            0.009
          ],
          [
            -0.047,
            0.033
          ]
        ]
      },
      "question_id": "waymo_val_774c537b284a58c77de4106b1c190117_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "774c537b284a58c77de4106b1c190117-128",
        "774c537b284a58c77de4106b1c190117-133",
        "774c537b284a58c77de4106b1c190117-138",
        "774c537b284a58c77de4106b1c190117-143",
        "774c537b284a58c77de4106b1c190117-148"
      ],
      "choices": {
        "A": "It would likely enter opposing traffic or create a head-on conflict",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "774c537b284a58c77de4106b1c190117-148",
      "anchor_image_path": "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "774c537b284a58c77de4106b1c190117",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "774c537b284a58c77de4106b1c190117:group_148",
      "image_paths": [
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      "image_path": "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            1633.08,
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          [
            1017.51,
            2615.69
          ],
          [
            887.64,
            1828.26
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        ],
        "B": [
          [
            950.21,
            3829.06
          ],
          [
            733.44,
            1841.08
          ],
          [
            656.95,
            1362.39
          ]
        ],
        "C": []
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "7cf9eee8001e0e5821725f1665fa8ed9/CAM_FRONT/7cf9eee8001e0e5821725f1665fa8ed9-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle It would likely cross the intersection, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.139,
            -0.0
          ],
          [
            0.709,
            -0.002
          ],
          [
            3.425,
            -0.097
          ],
          [
            7.545,
            -0.472
          ],
          [
            14.619,
            -1.336
          ]
        ],
        "B": [
          [
            0.134,
            -0.001
          ],
          [
            0.216,
            -0.001
          ],
          [
            0.325,
            0.001
          ],
          [
            0.523,
            0.005
          ],
          [
            0.991,
            0.011
          ]
        ],
        "C": [
          [
            0.095,
            -0.001
          ],
          [
            0.396,
            -0.005
          ],
          [
            2.011,
            -0.045
          ],
          [
            4.714,
            -0.158
          ],
          [
            9.948,
            -0.452
          ]
        ]
      },
      "question_id": "waymo_val_7cf9eee8001e0e5821725f1665fa8ed9_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "7cf9eee8001e0e5821725f1665fa8ed9-128",
        "7cf9eee8001e0e5821725f1665fa8ed9-133",
        "7cf9eee8001e0e5821725f1665fa8ed9-138",
        "7cf9eee8001e0e5821725f1665fa8ed9-143",
        "7cf9eee8001e0e5821725f1665fa8ed9-148"
      ],
      "choices": {
        "A": "It would likely create a conflict with another road user or obstacle",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "7cf9eee8001e0e5821725f1665fa8ed9-148",
      "anchor_image_path": "7cf9eee8001e0e5821725f1665fa8ed9/CAM_FRONT/7cf9eee8001e0e5821725f1665fa8ed9-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7cf9eee8001e0e5821725f1665fa8ed9",
      "scenario_cluster": "Cyclist",
      "bundle_id": "7cf9eee8001e0e5821725f1665fa8ed9:group_148",
      "image_paths": [
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      ],
      "image_path": "7cf9eee8001e0e5821725f1665fa8ed9/CAM_FRONT/7cf9eee8001e0e5821725f1665fa8ed9-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            570.12,
            1754.78
          ],
          [
            585.22,
            1041.36
          ],
          [
            607.7,
            861.73
          ]
        ],
        "B": [],
        "C": [
          [
            676.6,
            4667.48
          ],
          [
            559.48,
            1334.91
          ],
          [
            555.76,
            946.88
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "7ff16a5def1effbc5a017cc9b063b9de/CAM_FRONT/7ff16a5def1effbc5a017cc9b063b9de-134_FRONT.jpg",
        "7ff16a5def1effbc5a017cc9b063b9de/CAM_FRONT/7ff16a5def1effbc5a017cc9b063b9de-139_FRONT.jpg",
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        "7ff16a5def1effbc5a017cc9b063b9de/CAM_FRONT/7ff16a5def1effbc5a017cc9b063b9de-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            1.479,
            -0.001
          ],
          [
            8.117,
            0.037
          ],
          [
            13.263,
            0.106
          ],
          [
            16.373,
            0.147
          ],
          [
            19.192,
            0.16
          ]
        ],
        "B": [
          [
            2.512,
            0.007
          ],
          [
            9.362,
            0.051
          ],
          [
            15.523,
            0.036
          ],
          [
            19.776,
            0.011
          ],
          [
            23.851,
            0.022
          ]
        ],
        "C": [
          [
            2.502,
            0.007
          ],
          [
            8.592,
            0.075
          ],
          [
            12.25,
            0.13
          ],
          [
            13.846,
            0.149
          ],
          [
            15.209,
            0.155
          ]
        ]
      },
      "question_id": "waymo_val_7ff16a5def1effbc5a017cc9b063b9de_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "7ff16a5def1effbc5a017cc9b063b9de-129",
        "7ff16a5def1effbc5a017cc9b063b9de-134",
        "7ff16a5def1effbc5a017cc9b063b9de-139",
        "7ff16a5def1effbc5a017cc9b063b9de-144",
        "7ff16a5def1effbc5a017cc9b063b9de-149"
      ],
      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely crash into another car or vehicle"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "7ff16a5def1effbc5a017cc9b063b9de-149",
      "anchor_image_path": "7ff16a5def1effbc5a017cc9b063b9de/CAM_FRONT/7ff16a5def1effbc5a017cc9b063b9de-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7ff16a5def1effbc5a017cc9b063b9de",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "7ff16a5def1effbc5a017cc9b063b9de:group_149",
      "image_paths": [
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      ],
      "image_path": "7ff16a5def1effbc5a017cc9b063b9de/CAM_FRONT/7ff16a5def1effbc5a017cc9b063b9de-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            487.05,
            1023.9
          ],
          [
            481.8,
            890.28
          ],
          [
            480.44,
            854.41
          ],
          [
            481.15,
            832.8
          ]
        ],
        "B": [
          [
            504.01,
            2744.81
          ],
          [
            485.53,
            975.49
          ],
          [
            488.69,
            862.66
          ],
          [
            490.52,
            829.2
          ],
          [
            489.88,
            809.09
          ]
        ],
        "C": [
          [
            504.15,
            2765.41
          ],
          [
            481.26,
            1003.4
          ],
          [
            478.54,
            906.43
          ],
          [
            478.3,
            882.16
          ],
          [
            478.98,
            865.92
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-132_FRONT.jpg",
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        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.58,
            0.024
          ],
          [
            2.464,
            0.44
          ],
          [
            4.789,
            1.715
          ],
          [
            6.664,
            3.731
          ],
          [
            8.153,
            7.706
          ]
        ],
        "B": [
          [
            0.635,
            0.031
          ],
          [
            3.564,
            0.904
          ],
          [
            7.165,
            4.331
          ],
          [
            8.525,
            9.368
          ],
          [
            7.129,
            17.283
          ]
        ],
        "C": [
          [
            0.518,
            0.018
          ],
          [
            1.266,
            0.105
          ],
          [
            1.334,
            0.122
          ],
          [
            1.366,
            0.126
          ],
          [
            1.405,
            0.133
          ]
        ]
      },
      "question_id": "waymo_val_800692ff67909e7d79fadbb2849c71ce_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "800692ff67909e7d79fadbb2849c71ce-127",
        "800692ff67909e7d79fadbb2849c71ce-132",
        "800692ff67909e7d79fadbb2849c71ce-137",
        "800692ff67909e7d79fadbb2849c71ce-142",
        "800692ff67909e7d79fadbb2849c71ce-147"
      ],
      "choices": {
        "A": "It would likely go to the",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely go to the ",
      "frame_name": "800692ff67909e7d79fadbb2849c71ce-147",
      "anchor_image_path": "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "800692ff67909e7d79fadbb2849c71ce",
      "scenario_cluster": "Intersection",
      "bundle_id": "800692ff67909e7d79fadbb2849c71ce:group_147",
      "image_paths": [
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      ],
      "image_path": "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            8.44,
            2855.53
          ],
          [
            -79.94,
            1337.03
          ],
          [
            -305.67,
            1113.06
          ],
          [
            -788.92,
            1025.84
          ]
        ],
        "B": [
          [
            17.15,
            1705.94
          ],
          [
            -352.56,
            1078.46
          ],
          [
            -982.45,
            1009.96
          ],
          [
            -2884.97,
            1081.96
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        ],
        "C": []
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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      ],
      "question": "If the ego vehicle keeps turning right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
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          [
            1.481,
            -0.092
          ],
          [
            5.558,
            -1.422
          ],
          [
            9.681,
            -3.785
          ],
          [
            13.132,
            -5.878
          ],
          [
            17.714,
            -8.66
          ]
        ],
        "B": [
          [
            1.447,
            -0.106
          ],
          [
            5.872,
            -1.604
          ],
          [
            11.0,
            -4.696
          ],
          [
            15.775,
            -7.724
          ],
          [
            22.343,
            -11.783
          ]
        ],
        "C": [
          [
            1.41,
            -0.097
          ],
          [
            3.972,
            -0.69
          ],
          [
            4.286,
            -0.805
          ],
          [
            4.076,
            -0.706
          ],
          [
            3.496,
            -0.406
          ]
        ]
      },
      "question_id": "waymo_val_95aad68ad5531c1764066a71c4093238_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
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        "95aad68ad5531c1764066a71c4093238-134",
        "95aad68ad5531c1764066a71c4093238-139",
        "95aad68ad5531c1764066a71c4093238-144",
        "95aad68ad5531c1764066a71c4093238-149"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely goes into the main lane",
        "D": "It would likely crash into another car or vehicle"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "95aad68ad5531c1764066a71c4093238-149",
      "anchor_image_path": "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "95aad68ad5531c1764066a71c4093238",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "95aad68ad5531c1764066a71c4093238:group_149",
      "image_paths": [
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      ],
      "image_path": "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            883.24,
            1214.89
          ],
          [
            1004.69,
            964.3
          ],
          [
            1051.07,
            891.36
          ],
          [
            1082.32,
            842.54
          ]
        ],
        "B": [
          [
            901.09,
            1179.11
          ],
          [
            1039.41,
            930.19
          ],
          [
            1090.35,
            859.42
          ],
          [
            1116.6,
            815.08
          ]
        ],
        "C": [
          [
            808.0,
            1535.54
          ],
          [
            817.73,
            1442.88
          ],
          [
            801.9,
            1502.32
          ],
          [
            725.81,
            1731.93
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-133_FRONT.jpg",
        "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-138_FRONT.jpg",
        "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-143_FRONT.jpg",
        "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            1.972,
            -0.01
          ],
          [
            3.522,
            -0.05
          ],
          [
            4.902,
            -0.12
          ],
          [
            5.579,
            -0.166
          ],
          [
            7.001,
            -0.29
          ]
        ],
        "B": [
          [
            2.087,
            -0.014
          ],
          [
            6.279,
            -0.227
          ],
          [
            9.941,
            -0.934
          ],
          [
            12.474,
            -1.712
          ],
          [
            15.302,
            -2.431
          ]
        ],
        "C": [
          [
            2.045,
            -0.008
          ],
          [
            5.236,
            -0.093
          ],
          [
            6.432,
            -0.157
          ],
          [
            6.793,
            -0.189
          ],
          [
            7.239,
            -0.231
          ]
        ]
      },
      "question_id": "waymo_val_95cd5b7d9945daff327d79cc169b8328_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "95cd5b7d9945daff327d79cc169b8328-128",
        "95cd5b7d9945daff327d79cc169b8328-133",
        "95cd5b7d9945daff327d79cc169b8328-138",
        "95cd5b7d9945daff327d79cc169b8328-143",
        "95cd5b7d9945daff327d79cc169b8328-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely crash into another car or vehicle",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "95cd5b7d9945daff327d79cc169b8328-148",
      "anchor_image_path": "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "95cd5b7d9945daff327d79cc169b8328",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "95cd5b7d9945daff327d79cc169b8328:group_148",
      "image_paths": [
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        "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-133_FRONT.jpg",
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        "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-148_FRONT.jpg"
      ],
      "image_path": "95cd5b7d9945daff327d79cc169b8328/CAM_FRONT/95cd5b7d9945daff327d79cc169b8328-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            532.68,
            5112.63
          ],
          [
            521.1,
            1718.14
          ],
          [
            531.01,
            1310.28
          ],
          [
            536.6,
            1211.5
          ],
          [
            549.41,
            1083.44
          ]
        ],
        "B": [
          [
            531.67,
            4229.27
          ],
          [
            543.85,
            1138.91
          ],
          [
            613.37,
            956.25
          ],
          [
            663.62,
            901.62
          ],
          [
            685.83,
            864.21
          ]
        ],
        "C": [
          [
            522.47,
            4507.36
          ],
          [
            519.16,
            1256.9
          ],
          [
            526.3,
            1125.6
          ],
          [
            530.5,
            1097.69
          ],
          [
            535.42,
            1068.07
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-128_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-133_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-138_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-143_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle keeps going straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            1.877,
            -0.012
          ],
          [
            5.771,
            -0.033
          ],
          [
            9.389,
            0.201
          ],
          [
            12.66,
            0.989
          ],
          [
            17.273,
            3.634
          ]
        ],
        "B": [
          [
            1.862,
            -0.02
          ],
          [
            5.314,
            -0.14
          ],
          [
            7.027,
            -0.266
          ],
          [
            7.367,
            -0.306
          ],
          [
            7.803,
            -0.375
          ]
        ],
        "C": [
          [
            0.986,
            -0.004
          ],
          [
            5.482,
            -0.062
          ],
          [
            8.654,
            -0.116
          ],
          [
            10.367,
            -0.144
          ],
          [
            11.137,
            -0.155
          ]
        ]
      },
      "question_id": "waymo_val_9d8cce1b82aeaec6173978bf27e1ff9a_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "9d8cce1b82aeaec6173978bf27e1ff9a-128",
        "9d8cce1b82aeaec6173978bf27e1ff9a-133",
        "9d8cce1b82aeaec6173978bf27e1ff9a-138",
        "9d8cce1b82aeaec6173978bf27e1ff9a-143",
        "9d8cce1b82aeaec6173978bf27e1ff9a-148"
      ],
      "choices": {
        "A": "It would likely crash into another car or vehicle",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "9d8cce1b82aeaec6173978bf27e1ff9a-148",
      "anchor_image_path": "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "9d8cce1b82aeaec6173978bf27e1ff9a",
      "scenario_cluster": "Intersection",
      "bundle_id": "9d8cce1b82aeaec6173978bf27e1ff9a:group_148",
      "image_paths": [
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        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg"
      ],
      "image_path": "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            565.36,
            6499.22
          ],
          [
            500.18,
            1198.7
          ],
          [
            461.66,
            980.61
          ],
          [
            390.86,
            905.03
          ],
          [
            232.79,
            851.0
          ]
        ],
        "B": [
          [
            595.56,
            6759.83
          ],
          [
            533.01,
            1256.09
          ],
          [
            544.56,
            1090.95
          ],
          [
            548.9,
            1069.72
          ],
          [
            556.92,
            1045.89
          ]
        ],
        "C": [
          [
            509.19,
            1233.37
          ],
          [
            508.34,
            1007.31
          ],
          [
            507.99,
            952.71
          ],
          [
            507.69,
            934.5
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-134_FRONT.jpg",
        "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-139_FRONT.jpg",
        "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-144_FRONT.jpg",
        "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            3.534,
            -0.365
          ],
          [
            10.679,
            -4.809
          ],
          [
            15.725,
            -15.124
          ],
          [
            19.525,
            -26.14
          ],
          [
            24.864,
            -41.542
          ]
        ],
        "B": [
          [
            3.447,
            -0.326
          ],
          [
            9.207,
            -3.294
          ],
          [
            12.088,
            -7.78
          ],
          [
            13.639,
            -11.743
          ],
          [
            15.522,
            -17.099
          ]
        ],
        "C": [
          [
            3.474,
            -0.321
          ],
          [
            9.891,
            -3.463
          ],
          [
            14.077,
            -9.648
          ],
          [
            16.651,
            -16.285
          ],
          [
            19.895,
            -25.916
          ]
        ]
      },
      "question_id": "waymo_val_a30e34193a7e4cbe64d571417e590fc9_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
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        "a30e34193a7e4cbe64d571417e590fc9-134",
        "a30e34193a7e4cbe64d571417e590fc9-139",
        "a30e34193a7e4cbe64d571417e590fc9-144",
        "a30e34193a7e4cbe64d571417e590fc9-149"
      ],
      "choices": {
        "A": "It would likely hit a barrier, curb, cone, or roadside object",
        "B": "It would likely entre the main traffic lane",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "a30e34193a7e4cbe64d571417e590fc9-149",
      "anchor_image_path": "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a30e34193a7e4cbe64d571417e590fc9",
      "scenario_cluster": "Cyclist",
      "bundle_id": "a30e34193a7e4cbe64d571417e590fc9:group_149",
      "image_paths": [
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        "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-149_FRONT.jpg"
      ],
      "image_path": "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            717.39,
            1710.24
          ],
          [
            1085.61,
            929.67
          ],
          [
            1690.98,
            846.74
          ],
          [
            2127.8,
            813.27
          ],
          [
            2498.95,
            784.59
          ]
        ],
        "B": [
          [
            704.93,
            1755.13
          ],
          [
            978.25,
            972.62
          ],
          [
            1322.06,
            898.53
          ],
          [
            1583.53,
            872.09
          ],
          [
            1867.58,
            847.47
          ]
        ],
        "C": [
          [
            699.17,
            1740.81
          ],
          [
            961.21,
            951.3
          ],
          [
            1357.64,
            867.96
          ],
          [
            1703.88,
            837.92
          ],
          [
            2080.9,
            811.37
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            5.036,
            0.008
          ],
          [
            16.235,
            0.867
          ],
          [
            24.848,
            2.342
          ],
          [
            29.496,
            3.334
          ],
          [
            32.957,
            4.145
          ]
        ],
        "B": [
          [
            5.012,
            0.017
          ],
          [
            14.874,
            0.53
          ],
          [
            20.198,
            1.111
          ],
          [
            21.986,
            1.378
          ],
          [
            23.07,
            1.587
          ]
        ],
        "C": [
          [
            5.106,
            0.008
          ],
          [
            17.363,
            0.93
          ],
          [
            29.864,
            2.733
          ],
          [
            40.029,
            4.163
          ],
          [
            52.72,
            5.846
          ]
        ]
      },
      "question_id": "waymo_val_a37a7047090039e5d9ad368e07aee00b_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "a37a7047090039e5d9ad368e07aee00b-133",
        "a37a7047090039e5d9ad368e07aee00b-138",
        "a37a7047090039e5d9ad368e07aee00b-143",
        "a37a7047090039e5d9ad368e07aee00b-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely crash into another car or vehicle"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "a37a7047090039e5d9ad368e07aee00b-148",
      "anchor_image_path": "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a37a7047090039e5d9ad368e07aee00b",
      "scenario_cluster": "Cyclist",
      "bundle_id": "a37a7047090039e5d9ad368e07aee00b:group_148",
      "image_paths": [
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      ],
      "image_path": "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            502.03,
            1287.76
          ],
          [
            430.63,
            853.47
          ],
          [
            383.51,
            803.27
          ],
          [
            362.34,
            789.05
          ],
          [
            348.09,
            781.21
          ]
        ],
        "B": [
          [
            499.23,
            1291.69
          ],
          [
            452.29,
            867.29
          ],
          [
            429.44,
            824.5
          ],
          [
            420.52,
            815.14
          ],
          [
            413.4,
            810.24
          ]
        ],
        "C": [
          [
            501.87,
            1276.63
          ],
          [
            430.68,
            843.75
          ],
          [
            387.63,
            788.0
          ],
          [
            374.27,
            769.35
          ],
          [
            367.26,
            756.44
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-133_FRONT.jpg",
        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-138_FRONT.jpg",
        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-143_FRONT.jpg",
        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            1.414,
            -0.004
          ],
          [
            3.791,
            -0.006
          ],
          [
            4.616,
            -0.008
          ],
          [
            5.342,
            -0.009
          ],
          [
            6.888,
            -0.003
          ]
        ],
        "B": [
          [
            1.863,
            -0.008
          ],
          [
            7.015,
            -0.104
          ],
          [
            12.543,
            -0.169
          ],
          [
            17.769,
            0.204
          ],
          [
            25.406,
            1.06
          ]
        ],
        "C": [
          [
            1.651,
            -0.006
          ],
          [
            3.401,
            0.001
          ],
          [
            2.737,
            0.031
          ],
          [
            2.206,
            0.064
          ],
          [
            1.433,
            0.136
          ]
        ]
      },
      "question_id": "waymo_val_a42ade86866dc0cd19ee95eafc1b91c8_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "a42ade86866dc0cd19ee95eafc1b91c8-128",
        "a42ade86866dc0cd19ee95eafc1b91c8-133",
        "a42ade86866dc0cd19ee95eafc1b91c8-138",
        "a42ade86866dc0cd19ee95eafc1b91c8-143",
        "a42ade86866dc0cd19ee95eafc1b91c8-148"
      ],
      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely crash into another car or vehicle",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "a42ade86866dc0cd19ee95eafc1b91c8-148",
      "anchor_image_path": "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a42ade86866dc0cd19ee95eafc1b91c8",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "a42ade86866dc0cd19ee95eafc1b91c8:group_148",
      "image_paths": [
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        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg"
      ],
      "image_path": "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            499.57,
            1598.69
          ],
          [
            497.54,
            1363.9
          ],
          [
            496.25,
            1240.99
          ],
          [
            492.99,
            1089.89
          ]
        ],
        "B": [
          [
            563.04,
            6470.58
          ],
          [
            513.34,
            1081.38
          ],
          [
            507.81,
            898.16
          ],
          [
            476.29,
            839.36
          ],
          [
            440.55,
            799.61
          ]
        ],
        "C": [
          [
            497.58,
            1781.1
          ],
          [
            474.82,
            2359.11
          ],
          [
            410.74,
            3618.86
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-128_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-133_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-138_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-143_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            1.277,
            0.0
          ],
          [
            2.889,
            -0.01
          ],
          [
            5.094,
            -0.044
          ],
          [
            8.5,
            -0.134
          ],
          [
            14.778,
            -0.28
          ]
        ],
        "B": [
          [
            1.262,
            0.006
          ],
          [
            2.132,
            0.0
          ],
          [
            2.33,
            -0.0
          ],
          [
            2.829,
            0.004
          ],
          [
            5.049,
            0.068
          ]
        ],
        "C": [
          [
            1.337,
            0.001
          ],
          [
            4.408,
            -0.023
          ],
          [
            10.245,
            -0.104
          ],
          [
            16.992,
            -0.144
          ],
          [
            26.793,
            0.008
          ]
        ]
      },
      "question_id": "waymo_val_a9d28c43ea1d48e94fce413ecf10d17c_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "a9d28c43ea1d48e94fce413ecf10d17c-128",
        "a9d28c43ea1d48e94fce413ecf10d17c-133",
        "a9d28c43ea1d48e94fce413ecf10d17c-138",
        "a9d28c43ea1d48e94fce413ecf10d17c-143",
        "a9d28c43ea1d48e94fce413ecf10d17c-148"
      ],
      "choices": {
        "A": "It would likely enter opposing traffic or create a head-on conflict",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely crash into another car or vehicle"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "a9d28c43ea1d48e94fce413ecf10d17c-148",
      "anchor_image_path": "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a9d28c43ea1d48e94fce413ecf10d17c",
      "scenario_cluster": "Intersection",
      "bundle_id": "a9d28c43ea1d48e94fce413ecf10d17c:group_148",
      "image_paths": [
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-128_FRONT.jpg",
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        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-138_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-143_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg"
      ],
      "image_path": "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            503.66,
            2181.03
          ],
          [
            504.49,
            1278.85
          ],
          [
            510.73,
            1004.69
          ],
          [
            512.16,
            868.3
          ]
        ],
        "B": [
          [
            505.01,
            3976.42
          ],
          [
            500.84,
            3184.9
          ],
          [
            492.51,
            2247.78
          ],
          [
            469.39,
            1285.7
          ]
        ],
        "C": [
          [
            500.35,
            1412.14
          ],
          [
            502.32,
            947.02
          ],
          [
            498.88,
            846.47
          ],
          [
            487.9,
            795.94
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-127_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-132_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-137_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-142_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle goes straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            2.313,
            0.009
          ],
          [
            6.188,
            0.104
          ],
          [
            9.094,
            0.386
          ],
          [
            11.332,
            0.834
          ],
          [
            13.715,
            1.627
          ]
        ],
        "B": [
          [
            2.406,
            0.008
          ],
          [
            8.883,
            0.079
          ],
          [
            17.721,
            0.136
          ],
          [
            26.061,
            0.109
          ],
          [
            37.19,
            0.021
          ]
        ],
        "C": [
          [
            2.338,
            0.007
          ],
          [
            7.534,
            0.003
          ],
          [
            12.519,
            -0.343
          ],
          [
            16.224,
            -1.013
          ],
          [
            20.532,
            -2.409
          ]
        ]
      },
      "question_id": "waymo_val_b55c6b8af0f4fbc79d6c75f4da05a1df_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "b55c6b8af0f4fbc79d6c75f4da05a1df-127",
        "b55c6b8af0f4fbc79d6c75f4da05a1df-132",
        "b55c6b8af0f4fbc79d6c75f4da05a1df-137",
        "b55c6b8af0f4fbc79d6c75f4da05a1df-142",
        "b55c6b8af0f4fbc79d6c75f4da05a1df-147"
      ],
      "choices": {
        "A": "It would likely pass the intersection",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "b55c6b8af0f4fbc79d6c75f4da05a1df-147",
      "anchor_image_path": "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "b55c6b8af0f4fbc79d6c75f4da05a1df",
      "scenario_cluster": "Cyclist",
      "bundle_id": "b55c6b8af0f4fbc79d6c75f4da05a1df:group_147",
      "image_paths": [
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-127_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-132_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-137_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-142_FRONT.jpg",
        "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-147_FRONT.jpg"
      ],
      "image_path": "b55c6b8af0f4fbc79d6c75f4da05a1df/CAM_FRONT/b55c6b8af0f4fbc79d6c75f4da05a1df-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            519.58,
            3236.69
          ],
          [
            474.21,
            1146.97
          ],
          [
            439.69,
            982.07
          ],
          [
            400.89,
            921.66
          ],
          [
            346.44,
            881.73
          ]
        ],
        "B": [
          [
            517.96,
            2973.56
          ],
          [
            484.55,
            989.63
          ],
          [
            484.78,
            840.86
          ],
          [
            488.51,
            798.72
          ],
          [
            492.39,
            773.17
          ]
        ],
        "C": [
          [
            521.45,
            3160.12
          ],
          [
            496.89,
            1050.77
          ],
          [
            529.74,
            899.45
          ],
          [
            570.97,
            853.39
          ],
          [
            634.85,
            822.34
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-128_FRONT.jpg",
        "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-133_FRONT.jpg",
        "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-138_FRONT.jpg",
        "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-143_FRONT.jpg",
        "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle goes straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.002,
            -0.002
          ],
          [
            0.053,
            0.002
          ],
          [
            0.987,
            0.025
          ],
          [
            3.132,
            0.228
          ],
          [
            7.353,
            0.92
          ]
        ],
        "B": [
          [
            -0.002,
            0.0
          ],
          [
            -0.005,
            0.001
          ],
          [
            -0.005,
            0.001
          ],
          [
            -0.005,
            0.001
          ],
          [
            -0.005,
            0.001
          ]
        ],
        "C": [
          [
            -0.005,
            -0.001
          ],
          [
            0.48,
            0.009
          ],
          [
            2.61,
            0.131
          ],
          [
            5.931,
            0.346
          ],
          [
            12.141,
            0.692
          ]
        ]
      },
      "question_id": "waymo_val_b6f3c55758b8c847063c1fe9e5154a08_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "b6f3c55758b8c847063c1fe9e5154a08-128",
        "b6f3c55758b8c847063c1fe9e5154a08-133",
        "b6f3c55758b8c847063c1fe9e5154a08-138",
        "b6f3c55758b8c847063c1fe9e5154a08-143",
        "b6f3c55758b8c847063c1fe9e5154a08-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely crash into another car or vehicle",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
      "frame_name": "b6f3c55758b8c847063c1fe9e5154a08-148",
      "anchor_image_path": "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "b6f3c55758b8c847063c1fe9e5154a08",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "b6f3c55758b8c847063c1fe9e5154a08:group_148",
      "image_paths": [
        "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-128_FRONT.jpg",
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        "b6f3c55758b8c847063c1fe9e5154a08/CAM_FRONT/b6f3c55758b8c847063c1fe9e5154a08-143_FRONT.jpg",
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      ],
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            -0.005
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            0.091
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            -0.554
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            -0.003
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            0.031
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            0.133
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            0.293
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            0.58
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            -0.004
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          [
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            0.025
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          [
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            0.111
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          [
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            0.242
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            0.462
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      "question_id": "waymo_val_c62ca0c1ed7cb3d79aae63deaeb86ca0_group_147_TRJ-10",
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        "D": "It would likely hit a barrier, curb, cone, or roadside object"
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      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "c62ca0c1ed7cb3d79aae63deaeb86ca0-147",
      "anchor_image_path": "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-147_FRONT.jpg",
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      "scenario_cluster": "Cut-ins",
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            885.44
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            840.26
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            1214.32
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            969.27
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            854.21
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            -0.009
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          [
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          [
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            -0.236
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            -3.231
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            -0.002
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          [
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            -1.553
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      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "c90a0552eff17d47f1fb4151e2c6334f-147",
      "anchor_image_path": "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-147_FRONT.jpg",
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      "scenario_cluster": "Foreign Object Debris",
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          [
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      },
      "ground_truth_text": "It would likely create a conflict with another road user or obstacle",
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          ]
        ],
        "C": [
          [
            518.7,
            1696.17
          ],
          [
            499.19,
            969.08
          ],
          [
            491.25,
            894.46
          ],
          [
            482.44,
            859.57
          ],
          [
            467.38,
            819.9
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-127_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-132_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-137_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-142_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle goes straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            2.856,
            -0.037
          ],
          [
            5.641,
            -0.47
          ],
          [
            6.293,
            -0.643
          ],
          [
            7.078,
            -0.861
          ],
          [
            9.369,
            -1.624
          ]
        ],
        "B": [
          [
            2.934,
            -0.069
          ],
          [
            6.886,
            -0.87
          ],
          [
            9.638,
            -1.872
          ],
          [
            12.027,
            -3.074
          ],
          [
            15.482,
            -5.411
          ]
        ],
        "C": [
          [
            2.9,
            -0.108
          ],
          [
            7.189,
            -0.885
          ],
          [
            11.695,
            -2.445
          ],
          [
            16.589,
            -5.144
          ],
          [
            23.876,
            -10.976
          ]
        ]
      },
      "question_id": "waymo_val_d1d943b7f0e8036803e262b8e0f9681c_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "d1d943b7f0e8036803e262b8e0f9681c-127",
        "d1d943b7f0e8036803e262b8e0f9681c-132",
        "d1d943b7f0e8036803e262b8e0f9681c-137",
        "d1d943b7f0e8036803e262b8e0f9681c-142",
        "d1d943b7f0e8036803e262b8e0f9681c-147"
      ],
      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely hit a barrier, curb, cone, or roadside objec",
        "D": "It would likely crash into another car or vehicle"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "d1d943b7f0e8036803e262b8e0f9681c-147",
      "anchor_image_path": "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d1d943b7f0e8036803e262b8e0f9681c",
      "scenario_cluster": "Construction",
      "bundle_id": "d1d943b7f0e8036803e262b8e0f9681c:group_147",
      "image_paths": [
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-127_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-132_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-137_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-142_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg"
      ],
      "image_path": "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            542.0,
            2202.11
          ],
          [
            626.75,
            1198.46
          ],
          [
            649.04,
            1132.25
          ],
          [
            670.9,
            1073.1
          ],
          [
            727.76,
            968.04
          ]
        ],
        "B": [
          [
            564.38,
            2120.9
          ],
          [
            679.07,
            1086.03
          ],
          [
            754.09,
            959.67
          ],
          [
            822.68,
            903.68
          ],
          [
            928.35,
            856.72
          ]
        ],
        "C": [
          [
            597.28,
            2155.62
          ],
          [
            672.17,
            1066.04
          ],
          [
            764.42,
            909.7
          ],
          [
            876.65,
            845.94
          ],
          [
            1043.58,
            802.97
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-128_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-133_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-138_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-143_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle continues mostly straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.034,
            0.0
          ],
          [
            0.449,
            -0.002
          ],
          [
            2.46,
            -0.027
          ],
          [
            4.707,
            -0.112
          ],
          [
            7.877,
            -0.337
          ]
        ],
        "B": [
          [
            0.055,
            0.001
          ],
          [
            2.484,
            -0.009
          ],
          [
            10.597,
            -0.026
          ],
          [
            20.289,
            0.042
          ],
          [
            34.414,
            0.215
          ]
        ],
        "C": [
          [
            0.023,
            -0.001
          ],
          [
            0.264,
            -0.002
          ],
          [
            1.969,
            -0.007
          ],
          [
            4.695,
            -0.038
          ],
          [
            9.546,
            -0.112
          ]
        ]
      },
      "question_id": "waymo_val_d42445e3d27558f8d2ed5f4e5a4138c0_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "d42445e3d27558f8d2ed5f4e5a4138c0-128",
        "d42445e3d27558f8d2ed5f4e5a4138c0-133",
        "d42445e3d27558f8d2ed5f4e5a4138c0-138",
        "d42445e3d27558f8d2ed5f4e5a4138c0-143",
        "d42445e3d27558f8d2ed5f4e5a4138c0-148"
      ],
      "choices": {
        "A": "It would likely hit a barrier, curb, cone, or roadside object",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely pass through the intersection"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "d42445e3d27558f8d2ed5f4e5a4138c0-148",
      "anchor_image_path": "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d42445e3d27558f8d2ed5f4e5a4138c0",
      "scenario_cluster": "Cyclist",
      "bundle_id": "d42445e3d27558f8d2ed5f4e5a4138c0:group_148",
      "image_paths": [
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-128_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-133_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-138_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-143_FRONT.jpg",
        "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-148_FRONT.jpg"
      ],
      "image_path": "d42445e3d27558f8d2ed5f4e5a4138c0/CAM_FRONT/d42445e3d27558f8d2ed5f4e5a4138c0-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            532.57,
            2844.47
          ],
          [
            532.73,
            1346.84
          ],
          [
            551.13,
            1033.42
          ]
        ],
        "B": [
          [
            510.86,
            2791.44
          ],
          [
            494.97,
            938.3
          ],
          [
            488.76,
            824.08
          ],
          [
            483.76,
            778.11
          ]
        ],
        "C": [
          [
            528.83,
            5148.81
          ],
          [
            507.01,
            1348.93
          ],
          [
            507.43,
            967.36
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-128_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-133_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-138_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-143_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            5.189,
            -0.017
          ],
          [
            17.216,
            -0.183
          ],
          [
            29.381,
            -0.608
          ],
          [
            39.593,
            -0.927
          ],
          [
            52.857,
            -1.147
          ]
        ],
        "B": [
          [
            5.181,
            -0.016
          ],
          [
            16.414,
            -0.081
          ],
          [
            26.141,
            -0.167
          ],
          [
            33.702,
            -0.246
          ],
          [
            43.686,
            -0.335
          ]
        ],
        "C": [
          [
            5.164,
            -0.017
          ],
          [
            16.103,
            -0.124
          ],
          [
            24.412,
            -0.582
          ],
          [
            29.294,
            -1.059
          ],
          [
            33.955,
            -1.374
          ]
        ]
      },
      "question_id": "waymo_val_d5a542df464004d76568e3ea694133bf_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
        "d5a542df464004d76568e3ea694133bf-128",
        "d5a542df464004d76568e3ea694133bf-133",
        "d5a542df464004d76568e3ea694133bf-138",
        "d5a542df464004d76568e3ea694133bf-143",
        "d5a542df464004d76568e3ea694133bf-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "d5a542df464004d76568e3ea694133bf-148",
      "anchor_image_path": "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d5a542df464004d76568e3ea694133bf",
      "scenario_cluster": "Cyclist",
      "bundle_id": "d5a542df464004d76568e3ea694133bf:group_148",
      "image_paths": [
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-128_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-133_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-138_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-143_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg"
      ],
      "image_path": "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            506.62,
            1268.22
          ],
          [
            508.15,
            847.68
          ],
          [
            518.65,
            791.65
          ],
          [
            521.17,
            772.26
          ],
          [
            518.73,
            758.59
          ]
        ],
        "B": [
          [
            506.34,
            1269.42
          ],
          [
            501.32,
            854.59
          ],
          [
            502.02,
            801.17
          ],
          [
            502.69,
            781.95
          ],
          [
            502.81,
            767.13
          ]
        ],
        "C": [
          [
            506.71,
            1271.98
          ],
          [
            504.78,
            857.47
          ],
          [
            522.9,
            807.34
          ],
          [
            536.84,
            791.87
          ],
          [
            541.4,
            781.44
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-129_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-134_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-139_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-144_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle goes straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.733,
            0.014
          ],
          [
            2.55,
            0.148
          ],
          [
            4.794,
            0.455
          ],
          [
            6.262,
            0.85
          ],
          [
            8.029,
            1.662
          ]
        ],
        "B": [
          [
            0.66,
            0.015
          ],
          [
            1.214,
            0.052
          ],
          [
            1.275,
            0.065
          ],
          [
            1.309,
            0.068
          ],
          [
            1.361,
            0.078
          ]
        ],
        "C": [
          [
            0.887,
            0.025
          ],
          [
            5.222,
            1.145
          ],
          [
            12.265,
            6.993
          ],
          [
            17.053,
            16.556
          ],
          [
            21.326,
            32.325
          ]
        ]
      },
      "question_id": "waymo_val_d7029947c6c42936fd5b9c5ee47bcd8a_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "d7029947c6c42936fd5b9c5ee47bcd8a-129",
        "d7029947c6c42936fd5b9c5ee47bcd8a-134",
        "d7029947c6c42936fd5b9c5ee47bcd8a-139",
        "d7029947c6c42936fd5b9c5ee47bcd8a-144",
        "d7029947c6c42936fd5b9c5ee47bcd8a-149"
      ],
      "choices": {
        "A": "It would likely hit a barrier, curb, cone, or roadside object",
        "B": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely enter opposing traffic or create a head-on conflict"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "d7029947c6c42936fd5b9c5ee47bcd8a-149",
      "anchor_image_path": "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d7029947c6c42936fd5b9c5ee47bcd8a",
      "scenario_cluster": "Intersection",
      "bundle_id": "d7029947c6c42936fd5b9c5ee47bcd8a:group_149",
      "image_paths": [
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-129_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-134_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-139_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-144_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg"
      ],
      "image_path": "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            373.08,
            2656.78
          ],
          [
            350.3,
            1329.62
          ],
          [
            301.56,
            1140.35
          ],
          [
            214.59,
            1025.98
          ]
        ],
        "B": [],
        "C": [
          [
            159.74,
            1259.74
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          [
            -226.82,
            906.96
          ],
          [
            -687.4,
            852.16
          ],
          [
            -1312.49,
            828.09
          ]
        ]
      }
    },
    {
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      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
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      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
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            0.021
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            0.426
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          [
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            1.014
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        "B": [
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            -0.01
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          [
            11.085,
            -0.114
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          [
            13.68,
            -0.242
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          [
            13.859,
            -0.26
          ],
          [
            13.662,
            -0.236
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        ],
        "C": [
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            -0.003
          ],
          [
            10.438,
            0.032
          ],
          [
            17.593,
            0.027
          ],
          [
            23.615,
            0.217
          ],
          [
            32.563,
            0.612
          ]
        ]
      },
      "question_id": "waymo_val_d7a1e9029ac7440b1f561e8f0dcf6cf5_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "d7a1e9029ac7440b1f561e8f0dcf6cf5-134",
        "d7a1e9029ac7440b1f561e8f0dcf6cf5-139",
        "d7a1e9029ac7440b1f561e8f0dcf6cf5-144",
        "d7a1e9029ac7440b1f561e8f0dcf6cf5-149"
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      "choices": {
        "A": "It would likely crash into another car or vehicle",
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        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely enter opposing traffic or create a head-on conflict"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "d7a1e9029ac7440b1f561e8f0dcf6cf5-149",
      "anchor_image_path": "d7a1e9029ac7440b1f561e8f0dcf6cf5/CAM_FRONT/d7a1e9029ac7440b1f561e8f0dcf6cf5-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d7a1e9029ac7440b1f561e8f0dcf6cf5",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "d7a1e9029ac7440b1f561e8f0dcf6cf5:group_149",
      "image_paths": [
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      "image_path": "d7a1e9029ac7440b1f561e8f0dcf6cf5/CAM_FRONT/d7a1e9029ac7440b1f561e8f0dcf6cf5-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
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          [
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            901.71
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          [
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            825.33
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          [
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            794.12
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          [
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        "B": [
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            1516.85
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          [
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            922.66
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          [
            514.48,
            878.01
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          [
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            875.63
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          [
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            878.25
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            8095.38
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          [
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            937.76
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          [
            490.13,
            837.77
          ],
          [
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            803.73
          ],
          [
            469.52,
            777.52
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        ]
      }
    },
    {
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      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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        "d851fd8c2b36dc5a06da14a19d103c41/CAM_FRONT/d851fd8c2b36dc5a06da14a19d103c41-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle turns right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
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            -0.086
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          [
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            -2.125
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          [
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            -6.178
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          [
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            -9.831
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          [
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            -14.558
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        ],
        "B": [
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            -0.162
          ],
          [
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            -2.353
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          [
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            -6.154
          ],
          [
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            -9.353
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          [
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            -13.104
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        ],
        "C": [
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            -0.173
          ],
          [
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            -2.683
          ],
          [
            10.567,
            -7.482
          ],
          [
            13.988,
            -11.776
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          [
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            -16.84
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        ]
      },
      "question_id": "waymo_val_d851fd8c2b36dc5a06da14a19d103c41_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
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        "d851fd8c2b36dc5a06da14a19d103c41-132",
        "d851fd8c2b36dc5a06da14a19d103c41-137",
        "d851fd8c2b36dc5a06da14a19d103c41-142",
        "d851fd8c2b36dc5a06da14a19d103c41-147"
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      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would enter the adjacent roadway",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "d851fd8c2b36dc5a06da14a19d103c41-147",
      "anchor_image_path": "d851fd8c2b36dc5a06da14a19d103c41/CAM_FRONT/d851fd8c2b36dc5a06da14a19d103c41-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d851fd8c2b36dc5a06da14a19d103c41",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "d851fd8c2b36dc5a06da14a19d103c41:group_147",
      "image_paths": [
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      "scoring_type": "mcq",
      "candidate_trajectories_image": {
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          [
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          [
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            862.24
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          [
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            817.85
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        ],
        "B": [
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            1147.55
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          [
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            942.37
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          [
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            883.91
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          [
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            845.81
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        "C": [
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            1140.57
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          [
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            935.07
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          [
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            875.13
          ],
          [
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            836.53
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        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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      ],
      "question": "If the ego vehicle veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
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            0.002
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          [
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            -0.004
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          [
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            -0.009
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          [
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            -0.044
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          [
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            -0.282
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        ],
        "B": [
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            -0.001
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          [
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            -0.002
          ],
          [
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            0.045
          ],
          [
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            0.269
          ],
          [
            4.9,
            0.6
          ]
        ],
        "C": [
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            -0.001
          ],
          [
            -0.0,
            -0.001
          ],
          [
            -0.0,
            -0.001
          ],
          [
            -0.0,
            -0.001
          ],
          [
            -0.001,
            -0.001
          ]
        ]
      },
      "question_id": "waymo_val_da4a979e45b17b8b48f6e65e3d162ac4_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
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        "da4a979e45b17b8b48f6e65e3d162ac4-134",
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        "da4a979e45b17b8b48f6e65e3d162ac4-144",
        "da4a979e45b17b8b48f6e65e3d162ac4-149"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely enter opposing traffic or create a head-on conflict"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "da4a979e45b17b8b48f6e65e3d162ac4-149",
      "anchor_image_path": "da4a979e45b17b8b48f6e65e3d162ac4/CAM_FRONT/da4a979e45b17b8b48f6e65e3d162ac4-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "da4a979e45b17b8b48f6e65e3d162ac4",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "da4a979e45b17b8b48f6e65e3d162ac4:group_149",
      "image_paths": [
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      "scoring_type": "mcq",
      "candidate_trajectories_image": {
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        "B": [
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          [
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    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
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      ],
      "question": "If the ego vehicle veers left, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
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            -0.04
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          [
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            -0.538
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          [
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            -1.828
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          [
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            -3.733
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          [
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        "B": [
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            -0.034
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          [
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            -0.371
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          [
            12.968,
            -1.073
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          [
            20.322,
            -2.099
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          [
            31.85,
            -3.931
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        ],
        "C": [
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            2.478,
            -0.013
          ],
          [
            5.563,
            -0.36
          ],
          [
            7.548,
            -0.736
          ],
          [
            8.961,
            -1.106
          ],
          [
            10.751,
            -1.649
          ]
        ]
      },
      "question_id": "waymo_val_dfa20c65dcc57c6ccabc47a7cc96d13f_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "A",
      "bundle_frame_names": [
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        "dfa20c65dcc57c6ccabc47a7cc96d13f-133",
        "dfa20c65dcc57c6ccabc47a7cc96d13f-138",
        "dfa20c65dcc57c6ccabc47a7cc96d13f-143",
        "dfa20c65dcc57c6ccabc47a7cc96d13f-148"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely enter opposing traffic or create a head-on conflict",
        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "dfa20c65dcc57c6ccabc47a7cc96d13f-148",
      "anchor_image_path": "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "dfa20c65dcc57c6ccabc47a7cc96d13f",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "dfa20c65dcc57c6ccabc47a7cc96d13f:group_148",
      "image_paths": [
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      "image_path": "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-148_FRONT.jpg",
      "scoring_type": "mcq",
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        "A": [
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          [
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          [
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            841.54
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          [
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          [
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        "B": [
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            569.01,
            2655.28
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          [
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            1095.54
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          [
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            893.94
          ],
          [
            619.61,
            824.93
          ],
          [
            638.85,
            784.05
          ]
        ],
        "C": [
          [
            551.01,
            2818.71
          ],
          [
            602.37,
            1216.42
          ],
          [
            635.93,
            1052.1
          ],
          [
            664.29,
            988.44
          ],
          [
            696.74,
            935.71
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-127_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-132_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-137_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-142_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            1.524,
            -0.004
          ],
          [
            2.146,
            0.005
          ],
          [
            2.529,
            0.02
          ],
          [
            3.957,
            0.13
          ],
          [
            8.156,
            0.41
          ]
        ],
        "B": [
          [
            1.625,
            0.003
          ],
          [
            4.811,
            0.134
          ],
          [
            11.357,
            0.133
          ],
          [
            18.558,
            -0.23
          ],
          [
            28.525,
            -0.705
          ]
        ],
        "C": [
          [
            1.531,
            -0.024
          ],
          [
            2.81,
            0.03
          ],
          [
            5.462,
            0.325
          ],
          [
            9.97,
            0.534
          ],
          [
            18.23,
            0.137
          ]
        ]
      },
      "question_id": "waymo_val_e350da1e9a0a9a8a5a0f2665ae0c4bdc_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "D",
      "bundle_frame_names": [
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-127",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-132",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-137",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-142",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-147"
      ],
      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely crash into another car or vehicle",
        "C": "It would likely enter opposing traffic or create a head-on conflict",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "e350da1e9a0a9a8a5a0f2665ae0c4bdc-147",
      "anchor_image_path": "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "e350da1e9a0a9a8a5a0f2665ae0c4bdc",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "e350da1e9a0a9a8a5a0f2665ae0c4bdc:group_147",
      "image_paths": [
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-127_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-132_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-137_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-142_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg"
      ],
      "image_path": "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            543.54,
            3919.14
          ],
          [
            507.64,
            2707.54
          ],
          [
            448.43,
            1543.16
          ],
          [
            429.2,
            1021.33
          ]
        ],
        "B": [
          [
            458.46,
            1329.21
          ],
          [
            481.11,
            922.48
          ],
          [
            509.61,
            835.89
          ],
          [
            522.85,
            792.22
          ]
        ],
        "C": [
          [
            495.69,
            2275.13
          ],
          [
            410.1,
            1228.47
          ],
          [
            426.4,
            956.26
          ],
          [
            485.46,
            838.31
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-129_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-134_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-139_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-144_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-149_FRONT.jpg"
      ],
      "question": "If the ego vehicle continues mostly straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.914,
            -0.04
          ],
          [
            3.334,
            -0.373
          ],
          [
            5.928,
            -0.51
          ],
          [
            8.224,
            -0.251
          ],
          [
            11.406,
            0.479
          ]
        ],
        "B": [
          [
            0.866,
            -0.039
          ],
          [
            2.335,
            -0.158
          ],
          [
            2.725,
            -0.153
          ],
          [
            2.829,
            -0.146
          ],
          [
            2.901,
            -0.141
          ]
        ],
        "C": [
          [
            0.874,
            -0.056
          ],
          [
            2.564,
            -0.408
          ],
          [
            3.219,
            -0.586
          ],
          [
            3.42,
            -0.638
          ],
          [
            3.532,
            -0.663
          ]
        ]
      },
      "question_id": "waymo_val_e587c134481445e3d7340b73970b41cc_group_149_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "e587c134481445e3d7340b73970b41cc-129",
        "e587c134481445e3d7340b73970b41cc-134",
        "e587c134481445e3d7340b73970b41cc-139",
        "e587c134481445e3d7340b73970b41cc-144",
        "e587c134481445e3d7340b73970b41cc-149"
      ],
      "choices": {
        "A": "It would likely hit a barrier, curb, cone, or roadside object",
        "B": "It would likely crash into another car or vehicle",
        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely enter opposing traffic or create a head-on conflict"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "e587c134481445e3d7340b73970b41cc-149",
      "anchor_image_path": "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "e587c134481445e3d7340b73970b41cc",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "e587c134481445e3d7340b73970b41cc:group_149",
      "image_paths": [
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-129_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-134_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-139_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-144_FRONT.jpg",
        "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-149_FRONT.jpg"
      ],
      "image_path": "e587c134481445e3d7340b73970b41cc/CAM_FRONT/e587c134481445e3d7340b73970b41cc-149_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            728.91,
            1830.33
          ],
          [
            623.46,
            1177.41
          ],
          [
            535.24,
            1020.81
          ],
          [
            439.09,
            923.83
          ]
        ],
        "B": [
          [
            727.24,
            3192.27
          ],
          [
            646.04,
            2390.44
          ],
          [
            627.76,
            2257.45
          ],
          [
            616.6,
            2177.15
          ]
        ],
        "C": [
          [
            942.22,
            2651.61
          ],
          [
            884.63,
            1906.85
          ],
          [
            873.4,
            1781.41
          ],
          [
            865.91,
            1722.39
          ]
        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-127_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-132_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-137_FRONT.jpg",
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        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg"
      ],
      "question": "If the ego vehicle goes straight, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            0.447,
            -0.0
          ],
          [
            2.919,
            0.051
          ],
          [
            6.6,
            0.626
          ],
          [
            9.578,
            2.06
          ],
          [
            12.442,
            5.28
          ]
        ],
        "B": [
          [
            0.433,
            -0.0
          ],
          [
            2.09,
            0.022
          ],
          [
            3.53,
            0.135
          ],
          [
            4.274,
            0.281
          ],
          [
            4.814,
            0.416
          ]
        ],
        "C": [
          [
            0.452,
            -0.002
          ],
          [
            3.654,
            -0.001
          ],
          [
            9.069,
            -0.12
          ],
          [
            13.948,
            -0.91
          ],
          [
            19.787,
            -3.858
          ]
        ]
      },
      "question_id": "waymo_val_f0236dc2f2fb6612f8d6abda39369974_group_147_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "C",
      "bundle_frame_names": [
        "f0236dc2f2fb6612f8d6abda39369974-127",
        "f0236dc2f2fb6612f8d6abda39369974-132",
        "f0236dc2f2fb6612f8d6abda39369974-137",
        "f0236dc2f2fb6612f8d6abda39369974-142",
        "f0236dc2f2fb6612f8d6abda39369974-147"
      ],
      "choices": {
        "A": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "B": "It would likely hit a barrier, curb, cone, or roadside object",
        "C": "It would likely crash into another car or vehicle",
        "D": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule"
      },
      "ground_truth_text": "It would likely make insufficient progress or cause unnecessary braking",
      "frame_name": "f0236dc2f2fb6612f8d6abda39369974-147",
      "anchor_image_path": "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f0236dc2f2fb6612f8d6abda39369974",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "f0236dc2f2fb6612f8d6abda39369974:group_147",
      "image_paths": [
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-127_FRONT.jpg",
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        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg"
      ],
      "image_path": "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg",
      "scoring_type": "mcq",
      "candidate_trajectories_image": {
        "A": [
          [
            450.87,
            2171.14
          ],
          [
            350.34,
            1120.32
          ],
          [
            202.28,
            972.13
          ],
          [
            -51.86,
            903.97
          ]
        ],
        "B": [
          [
            456.13,
            4308.41
          ],
          [
            414.95,
            1729.2
          ],
          [
            375.08,
            1456.52
          ],
          [
            347.61,
            1335.86
          ]
        ],
        "C": [
          [
            490.33,
            1671.26
          ],
          [
            504.95,
            992.06
          ],
          [
            568.37,
            887.97
          ],
          [
            721.69,
            837.8
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "trajectory_sampling": "uniform",
      "history_image_paths": [
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-128_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-133_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-138_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-143_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg"
      ],
      "question": "If the ego vehicle veers right, what is the most likely consequence? Return only one option letter (A, B, C, or D).",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "candidate_trajectories": {
        "A": [
          [
            9.683,
            -0.098
          ],
          [
            36.775,
            -1.717
          ],
          [
            63.531,
            -5.038
          ],
          [
            84.604,
            -9.026
          ],
          [
            110.543,
            -16.017
          ]
        ],
        "B": [
          [
            9.757,
            -0.099
          ],
          [
            37.417,
            -1.981
          ],
          [
            65.336,
            -6.238
          ],
          [
            87.732,
            -11.473
          ],
          [
            115.535,
            -20.345
          ]
        ],
        "C": [
          [
            9.733,
            -0.101
          ],
          [
            37.179,
            -1.972
          ],
          [
            64.682,
            -5.922
          ],
          [
            86.694,
            -10.568
          ],
          [
            114.063,
            -18.306
          ]
        ]
      },
      "question_id": "waymo_val_f1dd0e328596edd3c4cd977a0d09aef5_group_148_TRJ-10",
      "id": "TRJ-10",
      "ground_truth": "B",
      "bundle_frame_names": [
        "f1dd0e328596edd3c4cd977a0d09aef5-128",
        "f1dd0e328596edd3c4cd977a0d09aef5-133",
        "f1dd0e328596edd3c4cd977a0d09aef5-138",
        "f1dd0e328596edd3c4cd977a0d09aef5-143",
        "f1dd0e328596edd3c4cd977a0d09aef5-148"
      ],
      "choices": {
        "A": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "B": "It would likely drift into the exit lane and leave the highway",
        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely hit a barrier, curb, cone, or roadside object"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
      "frame_name": "f1dd0e328596edd3c4cd977a0d09aef5-148",
      "anchor_image_path": "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f1dd0e328596edd3c4cd977a0d09aef5",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "f1dd0e328596edd3c4cd977a0d09aef5:group_148",
      "image_paths": [
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-128_FRONT.jpg",
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        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-143_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg"
      ],
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      "scoring_type": "mcq",
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      ],
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          [
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          [
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        "C": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user",
        "D": "It would likely enter opposing traffic or create a head-on conflict"
      },
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      "anchor_image_path": "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-148_FRONT.jpg",
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      "scoring_type": "mcq",
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        "C": "It would likely violate a traffic light, sign, lane rule, or right-of-way rule",
        "D": "It would likely crash into a pedestrian, cyclist, or another vulnerable road user"
      },
      "ground_truth_text": "It would likely drift too far laterally toward another lane or the road edge",
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      "anchor_image_path": "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-148_FRONT.jpg",
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      "bundle_id": "fa3deaf57d0df0e3057722177a5e27fc:group_148",
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          [
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        "B": [
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            3698.45
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            1200.6
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            1029.27
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            949.43
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            854.77,
            879.18
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            2980.07
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            837.32
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            800.1
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    },
    {
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      ],
      "question": "Based on the past 5 consecutive 360-degree multi-camera frames, what is the most likely ego-vehicle maneuver over the next 5 future frames (approximately 2.5 seconds)?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "waymo_val_07b4716982f88d85c6e5b350883b2d9b_group_147_TRJ-2",
      "id": "TRJ-2",
      "ground_truth": "B",
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        "07b4716982f88d85c6e5b350883b2d9b-147"
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        "C": "Slightly veer right",
        "D": "Turn left",
        "E": "Turn right",
        "F": "Slow to near stop"
      },
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      "anchor_image_path": "07b4716982f88d85c6e5b350883b2d9b/CAM_FRONT/07b4716982f88d85c6e5b350883b2d9b-147_FRONT.jpg",
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      ],
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      },
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      "anchor_image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
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      ],
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      "choices": {
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        "D": "Turn left",
        "E": "Go straight",
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      },
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        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-142_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg"
      ],
      "image_path": "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-127_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-132_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-137_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-142_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 consecutive 360-degree multi-camera frames, how does the ego vehicle speed trend evolve over the next 5 future frames (approximately 2.5 seconds)?",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "MCQ",
      "question_id": "waymo_val_fb0ed944efebd34d756103188d59da85_group_147_TRJ-4",
      "id": "TRJ-4",
      "ground_truth": "D",
      "bundle_frame_names": [
        "fb0ed944efebd34d756103188d59da85-127",
        "fb0ed944efebd34d756103188d59da85-132",
        "fb0ed944efebd34d756103188d59da85-137",
        "fb0ed944efebd34d756103188d59da85-142",
        "fb0ed944efebd34d756103188d59da85-147"
      ],
      "choices": {
        "A": "Accelerating",
        "B": "Maintaining roughly constant speed",
        "C": "Stopping / nearly stopped",
        "D": "Decelerating"
      },
      "ground_truth_text": "Decelerating",
      "frame_name": "fb0ed944efebd34d756103188d59da85-147",
      "anchor_image_path": "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "fb0ed944efebd34d756103188d59da85",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "fb0ed944efebd34d756103188d59da85:group_147",
      "image_paths": [
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-127_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-132_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-137_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-142_FRONT.jpg",
        "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-147_FRONT.jpg"
      ],
      "image_path": "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-147_FRONT.jpg",
      "scoring_type": "mcq"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-128_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-133_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-138_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-143_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_06a63756c3dc03b0a1778312c6468d58_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.488, 0.091], [6.963, 0.116], [10.263, 0.352], [13.3, 1.064], [15.822, 2.086]]",
      "bundle_frame_names": [
        "06a63756c3dc03b0a1778312c6468d58-128",
        "06a63756c3dc03b0a1778312c6468d58-133",
        "06a63756c3dc03b0a1778312c6468d58-138",
        "06a63756c3dc03b0a1778312c6468d58-143",
        "06a63756c3dc03b0a1778312c6468d58-148"
      ],
      "ground_truth_text": "[[3.488, 0.091], [6.963, 0.116], [10.263, 0.352], [13.3, 1.064], [15.822, 2.086]]",
      "frame_name": "06a63756c3dc03b0a1778312c6468d58-148",
      "anchor_image_path": "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "06a63756c3dc03b0a1778312c6468d58",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "06a63756c3dc03b0a1778312c6468d58:group_148",
      "image_paths": [
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-128_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-133_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-138_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-143_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg"
      ],
      "image_path": "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-129_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-134_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-139_FRONT.jpg",
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        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_0d94d2a36405c21b1fc19601c41e8d23_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[8.311, -0.047], [15.499, -0.134], [20.323, -0.202], [22.583, -0.212], [23.172, -0.211]]",
      "bundle_frame_names": [
        "0d94d2a36405c21b1fc19601c41e8d23-129",
        "0d94d2a36405c21b1fc19601c41e8d23-134",
        "0d94d2a36405c21b1fc19601c41e8d23-139",
        "0d94d2a36405c21b1fc19601c41e8d23-144",
        "0d94d2a36405c21b1fc19601c41e8d23-149"
      ],
      "ground_truth_text": "[[8.311, -0.047], [15.499, -0.134], [20.323, -0.202], [22.583, -0.212], [23.172, -0.211]]",
      "frame_name": "0d94d2a36405c21b1fc19601c41e8d23-149",
      "anchor_image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0d94d2a36405c21b1fc19601c41e8d23",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "0d94d2a36405c21b1fc19601c41e8d23:group_149",
      "image_paths": [
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-129_FRONT.jpg",
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        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg"
      ],
      "image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-128_FRONT.jpg",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-133_FRONT.jpg",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-138_FRONT.jpg",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-143_FRONT.jpg",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_1a11d93bc5d7d5983c7f4d4a0f86dcb0_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[7.688, 0.005], [10.344, 0.021], [10.629, 0.02], [10.627, 0.021], [10.877, 0.021]]",
      "bundle_frame_names": [
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0-128",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0-133",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0-138",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0-143",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0-148"
      ],
      "ground_truth_text": "[[7.688, 0.005], [10.344, 0.021], [10.629, 0.02], [10.627, 0.021], [10.877, 0.021]]",
      "frame_name": "1a11d93bc5d7d5983c7f4d4a0f86dcb0-148",
      "anchor_image_path": "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "1a11d93bc5d7d5983c7f4d4a0f86dcb0",
      "scenario_cluster": "Cyclist",
      "bundle_id": "1a11d93bc5d7d5983c7f4d4a0f86dcb0:group_148",
      "image_paths": [
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-128_FRONT.jpg",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-133_FRONT.jpg",
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        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-143_FRONT.jpg",
        "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-148_FRONT.jpg"
      ],
      "image_path": "1a11d93bc5d7d5983c7f4d4a0f86dcb0/CAM_FRONT/1a11d93bc5d7d5983c7f4d4a0f86dcb0-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-128_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-133_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-138_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-143_FRONT.jpg",
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_1c606b71cbbb08251345d152bd5d4d4b_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[0.129, -0.0], [2.117, -0.007], [6.736, -0.025], [13.842, -0.04], [23.121, -0.008]]",
      "bundle_frame_names": [
        "1c606b71cbbb08251345d152bd5d4d4b-128",
        "1c606b71cbbb08251345d152bd5d4d4b-133",
        "1c606b71cbbb08251345d152bd5d4d4b-138",
        "1c606b71cbbb08251345d152bd5d4d4b-143",
        "1c606b71cbbb08251345d152bd5d4d4b-148"
      ],
      "ground_truth_text": "[[0.129, -0.0], [2.117, -0.007], [6.736, -0.025], [13.842, -0.04], [23.121, -0.008]]",
      "frame_name": "1c606b71cbbb08251345d152bd5d4d4b-148",
      "anchor_image_path": "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "1c606b71cbbb08251345d152bd5d4d4b",
      "scenario_cluster": "Others",
      "bundle_id": "1c606b71cbbb08251345d152bd5d4d4b:group_148",
      "image_paths": [
        "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-128_FRONT.jpg",
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      ],
      "image_path": "1c606b71cbbb08251345d152bd5d4d4b/CAM_FRONT/1c606b71cbbb08251345d152bd5d4d4b-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-128_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-133_FRONT.jpg",
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        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_262e00554925cca1cf924ed5ea430d66_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[0.422, 0.001], [2.281, 0.005], [5.215, -0.338], [8.136, -1.632], [10.483, -4.308]]",
      "bundle_frame_names": [
        "262e00554925cca1cf924ed5ea430d66-128",
        "262e00554925cca1cf924ed5ea430d66-133",
        "262e00554925cca1cf924ed5ea430d66-138",
        "262e00554925cca1cf924ed5ea430d66-143",
        "262e00554925cca1cf924ed5ea430d66-148"
      ],
      "ground_truth_text": "[[0.422, 0.001], [2.281, 0.005], [5.215, -0.338], [8.136, -1.632], [10.483, -4.308]]",
      "frame_name": "262e00554925cca1cf924ed5ea430d66-148",
      "anchor_image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "262e00554925cca1cf924ed5ea430d66",
      "scenario_cluster": "Intersection",
      "bundle_id": "262e00554925cca1cf924ed5ea430d66:group_148",
      "image_paths": [
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-128_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-133_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-138_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-143_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg"
      ],
      "image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-129_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-134_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-139_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-144_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_27b862196b866184a3c6b28dd1cdfbba_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[5.895, 0.112], [9.104, 0.271], [13.427, 0.333], [19.48, 0.296], [27.017, 0.191]]",
      "bundle_frame_names": [
        "27b862196b866184a3c6b28dd1cdfbba-129",
        "27b862196b866184a3c6b28dd1cdfbba-134",
        "27b862196b866184a3c6b28dd1cdfbba-139",
        "27b862196b866184a3c6b28dd1cdfbba-144",
        "27b862196b866184a3c6b28dd1cdfbba-149"
      ],
      "ground_truth_text": "[[5.895, 0.112], [9.104, 0.271], [13.427, 0.333], [19.48, 0.296], [27.017, 0.191]]",
      "frame_name": "27b862196b866184a3c6b28dd1cdfbba-149",
      "anchor_image_path": "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "27b862196b866184a3c6b28dd1cdfbba",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "27b862196b866184a3c6b28dd1cdfbba:group_149",
      "image_paths": [
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-129_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-134_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-139_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-144_FRONT.jpg",
        "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg"
      ],
      "image_path": "27b862196b866184a3c6b28dd1cdfbba/CAM_FRONT/27b862196b866184a3c6b28dd1cdfbba-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-127_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-132_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-137_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-142_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_291acc4578630f6231a35e718de21523_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.122, -0.011], [3.728, -0.011], [4.947, -0.01], [7.489, 0.0], [11.549, 0.036]]",
      "bundle_frame_names": [
        "291acc4578630f6231a35e718de21523-127",
        "291acc4578630f6231a35e718de21523-132",
        "291acc4578630f6231a35e718de21523-137",
        "291acc4578630f6231a35e718de21523-142",
        "291acc4578630f6231a35e718de21523-147"
      ],
      "ground_truth_text": "[[3.122, -0.011], [3.728, -0.011], [4.947, -0.01], [7.489, 0.0], [11.549, 0.036]]",
      "frame_name": "291acc4578630f6231a35e718de21523-147",
      "anchor_image_path": "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "291acc4578630f6231a35e718de21523",
      "scenario_cluster": "Cyclist",
      "bundle_id": "291acc4578630f6231a35e718de21523:group_147",
      "image_paths": [
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-127_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-132_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-137_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-142_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg"
      ],
      "image_path": "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-128_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-133_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-138_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-143_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_29b60d9832de56b7b0047692136bf918_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[8.265, -0.021], [15.121, -0.08], [21.483, -0.181], [26.51, -0.248], [30.51, -0.248]]",
      "bundle_frame_names": [
        "29b60d9832de56b7b0047692136bf918-128",
        "29b60d9832de56b7b0047692136bf918-133",
        "29b60d9832de56b7b0047692136bf918-138",
        "29b60d9832de56b7b0047692136bf918-143",
        "29b60d9832de56b7b0047692136bf918-148"
      ],
      "ground_truth_text": "[[8.265, -0.021], [15.121, -0.08], [21.483, -0.181], [26.51, -0.248], [30.51, -0.248]]",
      "frame_name": "29b60d9832de56b7b0047692136bf918-148",
      "anchor_image_path": "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "29b60d9832de56b7b0047692136bf918",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "29b60d9832de56b7b0047692136bf918:group_148",
      "image_paths": [
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-128_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-133_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-138_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-143_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg"
      ],
      "image_path": "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-128_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-133_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-138_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-143_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_2fe32b2cddf4f6e328e294fca52e482f_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[6.382, -0.027], [9.571, -0.259], [9.622, -0.266], [9.801, -0.286], [9.817, -0.288]]",
      "bundle_frame_names": [
        "2fe32b2cddf4f6e328e294fca52e482f-128",
        "2fe32b2cddf4f6e328e294fca52e482f-133",
        "2fe32b2cddf4f6e328e294fca52e482f-138",
        "2fe32b2cddf4f6e328e294fca52e482f-143",
        "2fe32b2cddf4f6e328e294fca52e482f-148"
      ],
      "ground_truth_text": "[[6.382, -0.027], [9.571, -0.259], [9.622, -0.266], [9.801, -0.286], [9.817, -0.288]]",
      "frame_name": "2fe32b2cddf4f6e328e294fca52e482f-148",
      "anchor_image_path": "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "2fe32b2cddf4f6e328e294fca52e482f",
      "scenario_cluster": "Cyclist",
      "bundle_id": "2fe32b2cddf4f6e328e294fca52e482f:group_148",
      "image_paths": [
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-128_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-133_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-138_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-143_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg"
      ],
      "image_path": "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-127_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-132_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-137_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-142_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_31935e26a0bd05d0f9bf5285154cb25e_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[19.242, 0.017], [36.607, 0.709], [51.274, 1.374], [65.515, 0.632], [80.202, -0.352]]",
      "bundle_frame_names": [
        "31935e26a0bd05d0f9bf5285154cb25e-127",
        "31935e26a0bd05d0f9bf5285154cb25e-132",
        "31935e26a0bd05d0f9bf5285154cb25e-137",
        "31935e26a0bd05d0f9bf5285154cb25e-142",
        "31935e26a0bd05d0f9bf5285154cb25e-147"
      ],
      "ground_truth_text": "[[19.242, 0.017], [36.607, 0.709], [51.274, 1.374], [65.515, 0.632], [80.202, -0.352]]",
      "frame_name": "31935e26a0bd05d0f9bf5285154cb25e-147",
      "anchor_image_path": "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "31935e26a0bd05d0f9bf5285154cb25e",
      "scenario_cluster": "Cyclist",
      "bundle_id": "31935e26a0bd05d0f9bf5285154cb25e:group_147",
      "image_paths": [
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-127_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-132_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-137_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-142_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg"
      ],
      "image_path": "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-128_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-133_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-138_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-143_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_349479e335692cf038b24348f274d575_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[4.735, 0.014], [9.772, 0.042], [14.929, 0.067], [19.921, 0.069], [24.583, 0.082]]",
      "bundle_frame_names": [
        "349479e335692cf038b24348f274d575-128",
        "349479e335692cf038b24348f274d575-133",
        "349479e335692cf038b24348f274d575-138",
        "349479e335692cf038b24348f274d575-143",
        "349479e335692cf038b24348f274d575-148"
      ],
      "ground_truth_text": "[[4.735, 0.014], [9.772, 0.042], [14.929, 0.067], [19.921, 0.069], [24.583, 0.082]]",
      "frame_name": "349479e335692cf038b24348f274d575-148",
      "anchor_image_path": "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "349479e335692cf038b24348f274d575",
      "scenario_cluster": "Intersection",
      "bundle_id": "349479e335692cf038b24348f274d575:group_148",
      "image_paths": [
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-128_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-133_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-138_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-143_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg"
      ],
      "image_path": "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "4d279c1e6f3220a3b594bd085b91e7cf/CAM_FRONT/4d279c1e6f3220a3b594bd085b91e7cf-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_4d279c1e6f3220a3b594bd085b91e7cf_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[2.123, 0.004], [3.442, 0.007], [4.602, 0.009], [7.513, 0.014], [12.907, 0.067]]",
      "bundle_frame_names": [
        "4d279c1e6f3220a3b594bd085b91e7cf-129",
        "4d279c1e6f3220a3b594bd085b91e7cf-134",
        "4d279c1e6f3220a3b594bd085b91e7cf-139",
        "4d279c1e6f3220a3b594bd085b91e7cf-144",
        "4d279c1e6f3220a3b594bd085b91e7cf-149"
      ],
      "ground_truth_text": "[[2.123, 0.004], [3.442, 0.007], [4.602, 0.009], [7.513, 0.014], [12.907, 0.067]]",
      "frame_name": "4d279c1e6f3220a3b594bd085b91e7cf-149",
      "anchor_image_path": "4d279c1e6f3220a3b594bd085b91e7cf/CAM_FRONT/4d279c1e6f3220a3b594bd085b91e7cf-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "4d279c1e6f3220a3b594bd085b91e7cf",
      "scenario_cluster": "Cyclist",
      "bundle_id": "4d279c1e6f3220a3b594bd085b91e7cf:group_149",
      "image_paths": [
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      ],
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      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_57e3f4d557f856f3b40d35329670f58f_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[15.08, 0.176], [31.01, 0.846], [47.559, 1.919], [64.643, 3.129], [82.219, 4.353]]",
      "bundle_frame_names": [
        "57e3f4d557f856f3b40d35329670f58f-129",
        "57e3f4d557f856f3b40d35329670f58f-134",
        "57e3f4d557f856f3b40d35329670f58f-139",
        "57e3f4d557f856f3b40d35329670f58f-144",
        "57e3f4d557f856f3b40d35329670f58f-149"
      ],
      "ground_truth_text": "[[15.08, 0.176], [31.01, 0.846], [47.559, 1.919], [64.643, 3.129], [82.219, 4.353]]",
      "frame_name": "57e3f4d557f856f3b40d35329670f58f-149",
      "anchor_image_path": "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "57e3f4d557f856f3b40d35329670f58f",
      "scenario_cluster": "Construction",
      "bundle_id": "57e3f4d557f856f3b40d35329670f58f:group_149",
      "image_paths": [
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      ],
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      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_5a71aee52b4764ceb8c0bef7f3e3a6c9_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[24.846, -0.404], [49.711, -1.32], [74.519, -2.463], [98.986, -3.745], [123.222, -5.132]]",
      "bundle_frame_names": [
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        "5a71aee52b4764ceb8c0bef7f3e3a6c9-133",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-138",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-143",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-148"
      ],
      "ground_truth_text": "[[24.846, -0.404], [49.711, -1.32], [74.519, -2.463], [98.986, -3.745], [123.222, -5.132]]",
      "frame_name": "5a71aee52b4764ceb8c0bef7f3e3a6c9-148",
      "anchor_image_path": "5a71aee52b4764ceb8c0bef7f3e3a6c9/CAM_FRONT/5a71aee52b4764ceb8c0bef7f3e3a6c9-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9:group_148",
      "image_paths": [
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      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_5f0d91d9bebc6af381629682682edcf8_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[21.197, 0.072], [41.777, 0.375], [61.985, 0.958], [81.954, 1.622], [101.588, 2.208]]",
      "bundle_frame_names": [
        "5f0d91d9bebc6af381629682682edcf8-128",
        "5f0d91d9bebc6af381629682682edcf8-133",
        "5f0d91d9bebc6af381629682682edcf8-138",
        "5f0d91d9bebc6af381629682682edcf8-143",
        "5f0d91d9bebc6af381629682682edcf8-148"
      ],
      "ground_truth_text": "[[21.197, 0.072], [41.777, 0.375], [61.985, 0.958], [81.954, 1.622], [101.588, 2.208]]",
      "frame_name": "5f0d91d9bebc6af381629682682edcf8-148",
      "anchor_image_path": "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5f0d91d9bebc6af381629682682edcf8",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "5f0d91d9bebc6af381629682682edcf8:group_148",
      "image_paths": [
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      ],
      "image_path": "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_60d8cd7bcba63512079dd96f6b8fdfab_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[0.247, -0.0], [2.089, -0.002], [5.831, 0.008], [11.225, 0.082], [18.236, 0.317]]",
      "bundle_frame_names": [
        "60d8cd7bcba63512079dd96f6b8fdfab-129",
        "60d8cd7bcba63512079dd96f6b8fdfab-134",
        "60d8cd7bcba63512079dd96f6b8fdfab-139",
        "60d8cd7bcba63512079dd96f6b8fdfab-144",
        "60d8cd7bcba63512079dd96f6b8fdfab-149"
      ],
      "ground_truth_text": "[[0.247, -0.0], [2.089, -0.002], [5.831, 0.008], [11.225, 0.082], [18.236, 0.317]]",
      "frame_name": "60d8cd7bcba63512079dd96f6b8fdfab-149",
      "anchor_image_path": "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "60d8cd7bcba63512079dd96f6b8fdfab",
      "scenario_cluster": "Cyclist",
      "bundle_id": "60d8cd7bcba63512079dd96f6b8fdfab:group_149",
      "image_paths": [
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      ],
      "image_path": "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "6567b0c95aae8192daea6207ca30f5bd/CAM_FRONT/6567b0c95aae8192daea6207ca30f5bd-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_6567b0c95aae8192daea6207ca30f5bd_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[28.03, -0.116], [53.561, -0.216], [76.775, -0.295], [99.501, -0.184], [122.557, 0.03]]",
      "bundle_frame_names": [
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        "6567b0c95aae8192daea6207ca30f5bd-132",
        "6567b0c95aae8192daea6207ca30f5bd-137",
        "6567b0c95aae8192daea6207ca30f5bd-142",
        "6567b0c95aae8192daea6207ca30f5bd-147"
      ],
      "ground_truth_text": "[[28.03, -0.116], [53.561, -0.216], [76.775, -0.295], [99.501, -0.184], [122.557, 0.03]]",
      "frame_name": "6567b0c95aae8192daea6207ca30f5bd-147",
      "anchor_image_path": "6567b0c95aae8192daea6207ca30f5bd/CAM_FRONT/6567b0c95aae8192daea6207ca30f5bd-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "6567b0c95aae8192daea6207ca30f5bd",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "6567b0c95aae8192daea6207ca30f5bd:group_147",
      "image_paths": [
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        "6567b0c95aae8192daea6207ca30f5bd/CAM_FRONT/6567b0c95aae8192daea6207ca30f5bd-132_FRONT.jpg",
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        "6567b0c95aae8192daea6207ca30f5bd/CAM_FRONT/6567b0c95aae8192daea6207ca30f5bd-147_FRONT.jpg"
      ],
      "image_path": "6567b0c95aae8192daea6207ca30f5bd/CAM_FRONT/6567b0c95aae8192daea6207ca30f5bd-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-129_FRONT.jpg",
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-134_FRONT.jpg",
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-139_FRONT.jpg",
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-144_FRONT.jpg",
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_6e73c2f589c607a008e4fd91932d36de_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[12.145, -0.04], [24.077, -0.056], [35.877, -0.021], [47.916, -0.021], [60.276, -0.03]]",
      "bundle_frame_names": [
        "6e73c2f589c607a008e4fd91932d36de-129",
        "6e73c2f589c607a008e4fd91932d36de-134",
        "6e73c2f589c607a008e4fd91932d36de-139",
        "6e73c2f589c607a008e4fd91932d36de-144",
        "6e73c2f589c607a008e4fd91932d36de-149"
      ],
      "ground_truth_text": "[[12.145, -0.04], [24.077, -0.056], [35.877, -0.021], [47.916, -0.021], [60.276, -0.03]]",
      "frame_name": "6e73c2f589c607a008e4fd91932d36de-149",
      "anchor_image_path": "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "6e73c2f589c607a008e4fd91932d36de",
      "scenario_cluster": "Construction",
      "bundle_id": "6e73c2f589c607a008e4fd91932d36de:group_149",
      "image_paths": [
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        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-134_FRONT.jpg",
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-139_FRONT.jpg",
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-144_FRONT.jpg",
        "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-149_FRONT.jpg"
      ],
      "image_path": "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-128_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-133_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-138_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-143_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_7deba62759cbbac95e741a5c527ef932_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.288, 0.017], [4.753, 0.022], [5.137, 0.023], [5.567, 0.024], [6.967, 0.026]]",
      "bundle_frame_names": [
        "7deba62759cbbac95e741a5c527ef932-128",
        "7deba62759cbbac95e741a5c527ef932-133",
        "7deba62759cbbac95e741a5c527ef932-138",
        "7deba62759cbbac95e741a5c527ef932-143",
        "7deba62759cbbac95e741a5c527ef932-148"
      ],
      "ground_truth_text": "[[3.288, 0.017], [4.753, 0.022], [5.137, 0.023], [5.567, 0.024], [6.967, 0.026]]",
      "frame_name": "7deba62759cbbac95e741a5c527ef932-148",
      "anchor_image_path": "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7deba62759cbbac95e741a5c527ef932",
      "scenario_cluster": "Cyclist",
      "bundle_id": "7deba62759cbbac95e741a5c527ef932:group_148",
      "image_paths": [
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        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-133_FRONT.jpg",
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        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg"
      ],
      "image_path": "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-133_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-138_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-143_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_8b6660a0653c8339753d8db89299e308_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[0.136, 0.001], [1.193, -0.0], [3.47, 0.014], [6.724, 0.39], [10.442, 1.94]]",
      "bundle_frame_names": [
        "8b6660a0653c8339753d8db89299e308-128",
        "8b6660a0653c8339753d8db89299e308-133",
        "8b6660a0653c8339753d8db89299e308-138",
        "8b6660a0653c8339753d8db89299e308-143",
        "8b6660a0653c8339753d8db89299e308-148"
      ],
      "ground_truth_text": "[[0.136, 0.001], [1.193, -0.0], [3.47, 0.014], [6.724, 0.39], [10.442, 1.94]]",
      "frame_name": "8b6660a0653c8339753d8db89299e308-148",
      "anchor_image_path": "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "8b6660a0653c8339753d8db89299e308",
      "scenario_cluster": "Cyclist",
      "bundle_id": "8b6660a0653c8339753d8db89299e308:group_148",
      "image_paths": [
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        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-133_FRONT.jpg",
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        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg"
      ],
      "image_path": "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-133_FRONT.jpg",
        "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-138_FRONT.jpg",
        "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-143_FRONT.jpg",
        "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_93b2364898ed9f011c8be36992189132_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[25.352, -0.003], [50.662, 0.074], [76.05, 0.327], [101.384, 0.644], [126.506, 0.944]]",
      "bundle_frame_names": [
        "93b2364898ed9f011c8be36992189132-128",
        "93b2364898ed9f011c8be36992189132-133",
        "93b2364898ed9f011c8be36992189132-138",
        "93b2364898ed9f011c8be36992189132-143",
        "93b2364898ed9f011c8be36992189132-148"
      ],
      "ground_truth_text": "[[25.352, -0.003], [50.662, 0.074], [76.05, 0.327], [101.384, 0.644], [126.506, 0.944]]",
      "frame_name": "93b2364898ed9f011c8be36992189132-148",
      "anchor_image_path": "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "93b2364898ed9f011c8be36992189132",
      "scenario_cluster": "Others",
      "bundle_id": "93b2364898ed9f011c8be36992189132:group_148",
      "image_paths": [
        "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-128_FRONT.jpg",
        "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-133_FRONT.jpg",
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      ],
      "image_path": "93b2364898ed9f011c8be36992189132/CAM_FRONT/93b2364898ed9f011c8be36992189132-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-134_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-139_FRONT.jpg",
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        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_95aad68ad5531c1764066a71c4093238_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.168, -0.929], [6.319, -2.646], [9.599, -4.648], [13.059, -6.746], [16.712, -8.964]]",
      "bundle_frame_names": [
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        "95aad68ad5531c1764066a71c4093238-134",
        "95aad68ad5531c1764066a71c4093238-139",
        "95aad68ad5531c1764066a71c4093238-144",
        "95aad68ad5531c1764066a71c4093238-149"
      ],
      "ground_truth_text": "[[3.168, -0.929], [6.319, -2.646], [9.599, -4.648], [13.059, -6.746], [16.712, -8.964]]",
      "frame_name": "95aad68ad5531c1764066a71c4093238-149",
      "anchor_image_path": "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "95aad68ad5531c1764066a71c4093238",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "95aad68ad5531c1764066a71c4093238:group_149",
      "image_paths": [
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        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-134_FRONT.jpg",
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      ],
      "image_path": "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-128_FRONT.jpg",
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      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_9d8cce1b82aeaec6173978bf27e1ff9a_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.748, -0.055], [6.911, -0.109], [9.133, -0.147], [10.663, -0.17], [11.136, -0.177]]",
      "bundle_frame_names": [
        "9d8cce1b82aeaec6173978bf27e1ff9a-128",
        "9d8cce1b82aeaec6173978bf27e1ff9a-133",
        "9d8cce1b82aeaec6173978bf27e1ff9a-138",
        "9d8cce1b82aeaec6173978bf27e1ff9a-143",
        "9d8cce1b82aeaec6173978bf27e1ff9a-148"
      ],
      "ground_truth_text": "[[3.748, -0.055], [6.911, -0.109], [9.133, -0.147], [10.663, -0.17], [11.136, -0.177]]",
      "frame_name": "9d8cce1b82aeaec6173978bf27e1ff9a-148",
      "anchor_image_path": "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "9d8cce1b82aeaec6173978bf27e1ff9a",
      "scenario_cluster": "Intersection",
      "bundle_id": "9d8cce1b82aeaec6173978bf27e1ff9a:group_148",
      "image_paths": [
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        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg"
      ],
      "image_path": "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-133_FRONT.jpg",
        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-138_FRONT.jpg",
        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-143_FRONT.jpg",
        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a37a7047090039e5d9ad368e07aee00b_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[9.486, 0.086], [14.741, 0.484], [16.349, 0.735], [17.973, 1.04], [21.159, 1.608]]",
      "bundle_frame_names": [
        "a37a7047090039e5d9ad368e07aee00b-128",
        "a37a7047090039e5d9ad368e07aee00b-133",
        "a37a7047090039e5d9ad368e07aee00b-138",
        "a37a7047090039e5d9ad368e07aee00b-143",
        "a37a7047090039e5d9ad368e07aee00b-148"
      ],
      "ground_truth_text": "[[9.486, 0.086], [14.741, 0.484], [16.349, 0.735], [17.973, 1.04], [21.159, 1.608]]",
      "frame_name": "a37a7047090039e5d9ad368e07aee00b-148",
      "anchor_image_path": "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a37a7047090039e5d9ad368e07aee00b",
      "scenario_cluster": "Cyclist",
      "bundle_id": "a37a7047090039e5d9ad368e07aee00b:group_148",
      "image_paths": [
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        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-133_FRONT.jpg",
        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-138_FRONT.jpg",
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        "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-148_FRONT.jpg"
      ],
      "image_path": "a37a7047090039e5d9ad368e07aee00b/CAM_FRONT/a37a7047090039e5d9ad368e07aee00b-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-133_FRONT.jpg",
        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-138_FRONT.jpg",
        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-143_FRONT.jpg",
        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a42ade86866dc0cd19ee95eafc1b91c8_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.037, -0.005], [4.075, -0.008], [4.709, -0.007], [5.459, -0.003], [6.69, 0.01]]",
      "bundle_frame_names": [
        "a42ade86866dc0cd19ee95eafc1b91c8-128",
        "a42ade86866dc0cd19ee95eafc1b91c8-133",
        "a42ade86866dc0cd19ee95eafc1b91c8-138",
        "a42ade86866dc0cd19ee95eafc1b91c8-143",
        "a42ade86866dc0cd19ee95eafc1b91c8-148"
      ],
      "ground_truth_text": "[[3.037, -0.005], [4.075, -0.008], [4.709, -0.007], [5.459, -0.003], [6.69, 0.01]]",
      "frame_name": "a42ade86866dc0cd19ee95eafc1b91c8-148",
      "anchor_image_path": "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a42ade86866dc0cd19ee95eafc1b91c8",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "a42ade86866dc0cd19ee95eafc1b91c8:group_148",
      "image_paths": [
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        "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg"
      ],
      "image_path": "a42ade86866dc0cd19ee95eafc1b91c8/CAM_FRONT/a42ade86866dc0cd19ee95eafc1b91c8-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-132_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-137_FRONT.jpg",
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        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a54a6cab3409c4c47afbe4c2ac48ac90_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[12.537, 0.101], [24.93, 0.291], [36.795, 0.872], [47.642, 1.638], [57.108, 1.914]]",
      "bundle_frame_names": [
        "a54a6cab3409c4c47afbe4c2ac48ac90-127",
        "a54a6cab3409c4c47afbe4c2ac48ac90-132",
        "a54a6cab3409c4c47afbe4c2ac48ac90-137",
        "a54a6cab3409c4c47afbe4c2ac48ac90-142",
        "a54a6cab3409c4c47afbe4c2ac48ac90-147"
      ],
      "ground_truth_text": "[[12.537, 0.101], [24.93, 0.291], [36.795, 0.872], [47.642, 1.638], [57.108, 1.914]]",
      "frame_name": "a54a6cab3409c4c47afbe4c2ac48ac90-147",
      "anchor_image_path": "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a54a6cab3409c4c47afbe4c2ac48ac90",
      "scenario_cluster": "Cyclist",
      "bundle_id": "a54a6cab3409c4c47afbe4c2ac48ac90:group_147",
      "image_paths": [
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        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg"
      ],
      "image_path": "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "a72707e8b188cea7d5724aaf76dda5f3/CAM_FRONT/a72707e8b188cea7d5724aaf76dda5f3-132_FRONT.jpg",
        "a72707e8b188cea7d5724aaf76dda5f3/CAM_FRONT/a72707e8b188cea7d5724aaf76dda5f3-137_FRONT.jpg",
        "a72707e8b188cea7d5724aaf76dda5f3/CAM_FRONT/a72707e8b188cea7d5724aaf76dda5f3-142_FRONT.jpg",
        "a72707e8b188cea7d5724aaf76dda5f3/CAM_FRONT/a72707e8b188cea7d5724aaf76dda5f3-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a72707e8b188cea7d5724aaf76dda5f3_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[0.663, -0.004], [0.671, -0.002], [1.659, 0.008], [5.201, 0.453], [9.666, 2.809]]",
      "bundle_frame_names": [
        "a72707e8b188cea7d5724aaf76dda5f3-127",
        "a72707e8b188cea7d5724aaf76dda5f3-132",
        "a72707e8b188cea7d5724aaf76dda5f3-137",
        "a72707e8b188cea7d5724aaf76dda5f3-142",
        "a72707e8b188cea7d5724aaf76dda5f3-147"
      ],
      "ground_truth_text": "[[0.663, -0.004], [0.671, -0.002], [1.659, 0.008], [5.201, 0.453], [9.666, 2.809]]",
      "frame_name": "a72707e8b188cea7d5724aaf76dda5f3-147",
      "anchor_image_path": "a72707e8b188cea7d5724aaf76dda5f3/CAM_FRONT/a72707e8b188cea7d5724aaf76dda5f3-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a72707e8b188cea7d5724aaf76dda5f3",
      "scenario_cluster": "Intersection",
      "bundle_id": "a72707e8b188cea7d5724aaf76dda5f3:group_147",
      "image_paths": [
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      ],
      "image_path": "a72707e8b188cea7d5724aaf76dda5f3/CAM_FRONT/a72707e8b188cea7d5724aaf76dda5f3-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-133_FRONT.jpg",
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        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a9d28c43ea1d48e94fce413ecf10d17c_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[1.474, -0.007], [1.664, -0.008], [1.649, -0.008], [1.755, -0.01], [3.734, -0.007]]",
      "bundle_frame_names": [
        "a9d28c43ea1d48e94fce413ecf10d17c-128",
        "a9d28c43ea1d48e94fce413ecf10d17c-133",
        "a9d28c43ea1d48e94fce413ecf10d17c-138",
        "a9d28c43ea1d48e94fce413ecf10d17c-143",
        "a9d28c43ea1d48e94fce413ecf10d17c-148"
      ],
      "ground_truth_text": "[[1.474, -0.007], [1.664, -0.008], [1.649, -0.008], [1.755, -0.01], [3.734, -0.007]]",
      "frame_name": "a9d28c43ea1d48e94fce413ecf10d17c-148",
      "anchor_image_path": "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a9d28c43ea1d48e94fce413ecf10d17c",
      "scenario_cluster": "Intersection",
      "bundle_id": "a9d28c43ea1d48e94fce413ecf10d17c:group_148",
      "image_paths": [
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      ],
      "image_path": "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-128_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-133_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-138_FRONT.jpg",
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        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_ae776e971249029cab5f6b069a677897_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[0.88, 0.013], [0.874, 0.013], [0.873, 0.013], [0.873, 0.013], [0.873, 0.013]]",
      "bundle_frame_names": [
        "ae776e971249029cab5f6b069a677897-128",
        "ae776e971249029cab5f6b069a677897-133",
        "ae776e971249029cab5f6b069a677897-138",
        "ae776e971249029cab5f6b069a677897-143",
        "ae776e971249029cab5f6b069a677897-148"
      ],
      "ground_truth_text": "[[0.88, 0.013], [0.874, 0.013], [0.873, 0.013], [0.873, 0.013], [0.873, 0.013]]",
      "frame_name": "ae776e971249029cab5f6b069a677897-148",
      "anchor_image_path": "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "ae776e971249029cab5f6b069a677897",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "ae776e971249029cab5f6b069a677897:group_148",
      "image_paths": [
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      ],
      "image_path": "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-133_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-138_FRONT.jpg",
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        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_b3c53b14746cce13a1ade988378276b7_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[11.19, -0.014], [16.82, -0.031], [20.122, -0.033], [24.569, -0.026], [29.948, 0.01]]",
      "bundle_frame_names": [
        "b3c53b14746cce13a1ade988378276b7-128",
        "b3c53b14746cce13a1ade988378276b7-133",
        "b3c53b14746cce13a1ade988378276b7-138",
        "b3c53b14746cce13a1ade988378276b7-143",
        "b3c53b14746cce13a1ade988378276b7-148"
      ],
      "ground_truth_text": "[[11.19, -0.014], [16.82, -0.031], [20.122, -0.033], [24.569, -0.026], [29.948, 0.01]]",
      "frame_name": "b3c53b14746cce13a1ade988378276b7-148",
      "anchor_image_path": "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "b3c53b14746cce13a1ade988378276b7",
      "scenario_cluster": "Cyclist",
      "bundle_id": "b3c53b14746cce13a1ade988378276b7:group_148",
      "image_paths": [
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        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-133_FRONT.jpg",
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        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg"
      ],
      "image_path": "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-132_FRONT.jpg",
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        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_bd09cff83130f018440c580cf71e54dc_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[2.91, 0.084], [3.743, 0.193], [4.347, 0.332], [5.565, 0.654], [6.816, 1.002]]",
      "bundle_frame_names": [
        "bd09cff83130f018440c580cf71e54dc-127",
        "bd09cff83130f018440c580cf71e54dc-132",
        "bd09cff83130f018440c580cf71e54dc-137",
        "bd09cff83130f018440c580cf71e54dc-142",
        "bd09cff83130f018440c580cf71e54dc-147"
      ],
      "ground_truth_text": "[[2.91, 0.084], [3.743, 0.193], [4.347, 0.332], [5.565, 0.654], [6.816, 1.002]]",
      "frame_name": "bd09cff83130f018440c580cf71e54dc-147",
      "anchor_image_path": "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "bd09cff83130f018440c580cf71e54dc",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "bd09cff83130f018440c580cf71e54dc:group_147",
      "image_paths": [
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        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-132_FRONT.jpg",
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        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg"
      ],
      "image_path": "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "be13ab572a2f5b5ef155a3e2bbbf9aa0/CAM_FRONT/be13ab572a2f5b5ef155a3e2bbbf9aa0-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_be13ab572a2f5b5ef155a3e2bbbf9aa0_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[10.165, 0.434], [15.002, 0.876], [17.484, 1.159], [20.424, 1.444], [24.935, 1.871]]",
      "bundle_frame_names": [
        "be13ab572a2f5b5ef155a3e2bbbf9aa0-127",
        "be13ab572a2f5b5ef155a3e2bbbf9aa0-132",
        "be13ab572a2f5b5ef155a3e2bbbf9aa0-137",
        "be13ab572a2f5b5ef155a3e2bbbf9aa0-142",
        "be13ab572a2f5b5ef155a3e2bbbf9aa0-147"
      ],
      "ground_truth_text": "[[10.165, 0.434], [15.002, 0.876], [17.484, 1.159], [20.424, 1.444], [24.935, 1.871]]",
      "frame_name": "be13ab572a2f5b5ef155a3e2bbbf9aa0-147",
      "anchor_image_path": "be13ab572a2f5b5ef155a3e2bbbf9aa0/CAM_FRONT/be13ab572a2f5b5ef155a3e2bbbf9aa0-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "be13ab572a2f5b5ef155a3e2bbbf9aa0",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "be13ab572a2f5b5ef155a3e2bbbf9aa0:group_147",
      "image_paths": [
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      "image_path": "be13ab572a2f5b5ef155a3e2bbbf9aa0/CAM_FRONT/be13ab572a2f5b5ef155a3e2bbbf9aa0-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "bf5ce21bed408c229595eea25846db55/CAM_FRONT/bf5ce21bed408c229595eea25846db55-133_FRONT.jpg",
        "bf5ce21bed408c229595eea25846db55/CAM_FRONT/bf5ce21bed408c229595eea25846db55-138_FRONT.jpg",
        "bf5ce21bed408c229595eea25846db55/CAM_FRONT/bf5ce21bed408c229595eea25846db55-143_FRONT.jpg",
        "bf5ce21bed408c229595eea25846db55/CAM_FRONT/bf5ce21bed408c229595eea25846db55-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_bf5ce21bed408c229595eea25846db55_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[10.395, 0.045], [20.086, 0.162], [29.02, 0.332], [37.069, 0.554], [44.447, 0.796]]",
      "bundle_frame_names": [
        "bf5ce21bed408c229595eea25846db55-128",
        "bf5ce21bed408c229595eea25846db55-133",
        "bf5ce21bed408c229595eea25846db55-138",
        "bf5ce21bed408c229595eea25846db55-143",
        "bf5ce21bed408c229595eea25846db55-148"
      ],
      "ground_truth_text": "[[10.395, 0.045], [20.086, 0.162], [29.02, 0.332], [37.069, 0.554], [44.447, 0.796]]",
      "frame_name": "bf5ce21bed408c229595eea25846db55-148",
      "anchor_image_path": "bf5ce21bed408c229595eea25846db55/CAM_FRONT/bf5ce21bed408c229595eea25846db55-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "bf5ce21bed408c229595eea25846db55",
      "scenario_cluster": "Intersection",
      "bundle_id": "bf5ce21bed408c229595eea25846db55:group_148",
      "image_paths": [
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        "bf5ce21bed408c229595eea25846db55/CAM_FRONT/bf5ce21bed408c229595eea25846db55-133_FRONT.jpg",
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      ],
      "image_path": "bf5ce21bed408c229595eea25846db55/CAM_FRONT/bf5ce21bed408c229595eea25846db55-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_c1b025ee95af51ea66623e5d17e94fab_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[7.725, -0.032], [15.545, -0.12], [23.461, -0.289], [31.159, -0.543], [38.621, -0.822]]",
      "bundle_frame_names": [
        "c1b025ee95af51ea66623e5d17e94fab-129",
        "c1b025ee95af51ea66623e5d17e94fab-134",
        "c1b025ee95af51ea66623e5d17e94fab-139",
        "c1b025ee95af51ea66623e5d17e94fab-144",
        "c1b025ee95af51ea66623e5d17e94fab-149"
      ],
      "ground_truth_text": "[[7.725, -0.032], [15.545, -0.12], [23.461, -0.289], [31.159, -0.543], [38.621, -0.822]]",
      "frame_name": "c1b025ee95af51ea66623e5d17e94fab-149",
      "anchor_image_path": "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "c1b025ee95af51ea66623e5d17e94fab",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "c1b025ee95af51ea66623e5d17e94fab:group_149",
      "image_paths": [
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      "image_path": "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "c915f39617f62ec72eda769e337731bd/CAM_FRONT/c915f39617f62ec72eda769e337731bd-134_FRONT.jpg",
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        "c915f39617f62ec72eda769e337731bd/CAM_FRONT/c915f39617f62ec72eda769e337731bd-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_c915f39617f62ec72eda769e337731bd_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[1.836, 0.001], [4.635, 0.022], [7.387, 0.048], [11.307, 0.082], [17.391, 0.161]]",
      "bundle_frame_names": [
        "c915f39617f62ec72eda769e337731bd-129",
        "c915f39617f62ec72eda769e337731bd-134",
        "c915f39617f62ec72eda769e337731bd-139",
        "c915f39617f62ec72eda769e337731bd-144",
        "c915f39617f62ec72eda769e337731bd-149"
      ],
      "ground_truth_text": "[[1.836, 0.001], [4.635, 0.022], [7.387, 0.048], [11.307, 0.082], [17.391, 0.161]]",
      "frame_name": "c915f39617f62ec72eda769e337731bd-149",
      "anchor_image_path": "c915f39617f62ec72eda769e337731bd/CAM_FRONT/c915f39617f62ec72eda769e337731bd-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "c915f39617f62ec72eda769e337731bd",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "c915f39617f62ec72eda769e337731bd:group_149",
      "image_paths": [
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        "c915f39617f62ec72eda769e337731bd/CAM_FRONT/c915f39617f62ec72eda769e337731bd-149_FRONT.jpg"
      ],
      "image_path": "c915f39617f62ec72eda769e337731bd/CAM_FRONT/c915f39617f62ec72eda769e337731bd-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-132_FRONT.jpg",
        "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-137_FRONT.jpg",
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        "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d007146fded6c0160d4c6f8ca5b60306_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[5.415, 0.048], [7.4, 0.182], [8.181, 0.275], [9.768, 0.171], [12.115, -0.404]]",
      "bundle_frame_names": [
        "d007146fded6c0160d4c6f8ca5b60306-127",
        "d007146fded6c0160d4c6f8ca5b60306-132",
        "d007146fded6c0160d4c6f8ca5b60306-137",
        "d007146fded6c0160d4c6f8ca5b60306-142",
        "d007146fded6c0160d4c6f8ca5b60306-147"
      ],
      "ground_truth_text": "[[5.415, 0.048], [7.4, 0.182], [8.181, 0.275], [9.768, 0.171], [12.115, -0.404]]",
      "frame_name": "d007146fded6c0160d4c6f8ca5b60306-147",
      "anchor_image_path": "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d007146fded6c0160d4c6f8ca5b60306",
      "scenario_cluster": "Cyclist",
      "bundle_id": "d007146fded6c0160d4c6f8ca5b60306:group_147",
      "image_paths": [
        "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-127_FRONT.jpg",
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        "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-137_FRONT.jpg",
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        "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-147_FRONT.jpg"
      ],
      "image_path": "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-127_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-132_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-137_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-142_FRONT.jpg",
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d1d943b7f0e8036803e262b8e0f9681c_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.71, -0.497], [3.828, -0.53], [3.82, -0.53], [3.82, -0.53], [3.82, -0.53]]",
      "bundle_frame_names": [
        "d1d943b7f0e8036803e262b8e0f9681c-127",
        "d1d943b7f0e8036803e262b8e0f9681c-132",
        "d1d943b7f0e8036803e262b8e0f9681c-137",
        "d1d943b7f0e8036803e262b8e0f9681c-142",
        "d1d943b7f0e8036803e262b8e0f9681c-147"
      ],
      "ground_truth_text": "[[3.71, -0.497], [3.828, -0.53], [3.82, -0.53], [3.82, -0.53], [3.82, -0.53]]",
      "frame_name": "d1d943b7f0e8036803e262b8e0f9681c-147",
      "anchor_image_path": "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d1d943b7f0e8036803e262b8e0f9681c",
      "scenario_cluster": "Construction",
      "bundle_id": "d1d943b7f0e8036803e262b8e0f9681c:group_147",
      "image_paths": [
        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-127_FRONT.jpg",
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        "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-142_FRONT.jpg",
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      ],
      "image_path": "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-133_FRONT.jpg",
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        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-143_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d5a542df464004d76568e3ea694133bf_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[8.003, -0.025], [10.901, -0.044], [11.545, -0.05], [13.237, -0.068], [17.167, -0.12]]",
      "bundle_frame_names": [
        "d5a542df464004d76568e3ea694133bf-128",
        "d5a542df464004d76568e3ea694133bf-133",
        "d5a542df464004d76568e3ea694133bf-138",
        "d5a542df464004d76568e3ea694133bf-143",
        "d5a542df464004d76568e3ea694133bf-148"
      ],
      "ground_truth_text": "[[8.003, -0.025], [10.901, -0.044], [11.545, -0.05], [13.237, -0.068], [17.167, -0.12]]",
      "frame_name": "d5a542df464004d76568e3ea694133bf-148",
      "anchor_image_path": "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d5a542df464004d76568e3ea694133bf",
      "scenario_cluster": "Cyclist",
      "bundle_id": "d5a542df464004d76568e3ea694133bf:group_148",
      "image_paths": [
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-128_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-133_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-138_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-143_FRONT.jpg",
        "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg"
      ],
      "image_path": "d5a542df464004d76568e3ea694133bf/CAM_FRONT/d5a542df464004d76568e3ea694133bf-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-128_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-133_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-138_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-143_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d6ea4dab948d2d1ce93216f3285a38f5_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[1.462, 0.014], [4.696, 0.09], [9.648, 0.336], [16.22, 0.705], [23.983, 1.15]]",
      "bundle_frame_names": [
        "d6ea4dab948d2d1ce93216f3285a38f5-128",
        "d6ea4dab948d2d1ce93216f3285a38f5-133",
        "d6ea4dab948d2d1ce93216f3285a38f5-138",
        "d6ea4dab948d2d1ce93216f3285a38f5-143",
        "d6ea4dab948d2d1ce93216f3285a38f5-148"
      ],
      "ground_truth_text": "[[1.462, 0.014], [4.696, 0.09], [9.648, 0.336], [16.22, 0.705], [23.983, 1.15]]",
      "frame_name": "d6ea4dab948d2d1ce93216f3285a38f5-148",
      "anchor_image_path": "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d6ea4dab948d2d1ce93216f3285a38f5",
      "scenario_cluster": "Intersection",
      "bundle_id": "d6ea4dab948d2d1ce93216f3285a38f5:group_148",
      "image_paths": [
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-128_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-133_FRONT.jpg",
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        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-143_FRONT.jpg",
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      ],
      "image_path": "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-127_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-132_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-137_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-142_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d7a169aa0b0b8247631ead8c0dad382f_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[7.486, -0.018], [11.118, 0.01], [11.931, 0.02], [12.745, 0.031], [15.647, 0.041]]",
      "bundle_frame_names": [
        "d7a169aa0b0b8247631ead8c0dad382f-127",
        "d7a169aa0b0b8247631ead8c0dad382f-132",
        "d7a169aa0b0b8247631ead8c0dad382f-137",
        "d7a169aa0b0b8247631ead8c0dad382f-142",
        "d7a169aa0b0b8247631ead8c0dad382f-147"
      ],
      "ground_truth_text": "[[7.486, -0.018], [11.118, 0.01], [11.931, 0.02], [12.745, 0.031], [15.647, 0.041]]",
      "frame_name": "d7a169aa0b0b8247631ead8c0dad382f-147",
      "anchor_image_path": "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d7a169aa0b0b8247631ead8c0dad382f",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "d7a169aa0b0b8247631ead8c0dad382f:group_147",
      "image_paths": [
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-127_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-132_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-137_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-142_FRONT.jpg",
        "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-147_FRONT.jpg"
      ],
      "image_path": "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-129_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-134_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-139_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-144_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d810a02521d2fdb70bd0b2e8fd706ab9_group_149_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[6.995, 0.15], [13.741, 0.494], [20.368, 1.082], [26.982, 1.799], [33.647, 2.312]]",
      "bundle_frame_names": [
        "d810a02521d2fdb70bd0b2e8fd706ab9-129",
        "d810a02521d2fdb70bd0b2e8fd706ab9-134",
        "d810a02521d2fdb70bd0b2e8fd706ab9-139",
        "d810a02521d2fdb70bd0b2e8fd706ab9-144",
        "d810a02521d2fdb70bd0b2e8fd706ab9-149"
      ],
      "ground_truth_text": "[[6.995, 0.15], [13.741, 0.494], [20.368, 1.082], [26.982, 1.799], [33.647, 2.312]]",
      "frame_name": "d810a02521d2fdb70bd0b2e8fd706ab9-149",
      "anchor_image_path": "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d810a02521d2fdb70bd0b2e8fd706ab9",
      "scenario_cluster": "Intersection",
      "bundle_id": "d810a02521d2fdb70bd0b2e8fd706ab9:group_149",
      "image_paths": [
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-129_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-134_FRONT.jpg",
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        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg"
      ],
      "image_path": "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-127_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-132_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-137_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-142_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_dab72f2dc046264172fbb26b39147bf7_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.984, -0.026], [6.604, -0.018], [8.362, -0.004], [9.045, 0.004], [9.199, 0.007]]",
      "bundle_frame_names": [
        "dab72f2dc046264172fbb26b39147bf7-127",
        "dab72f2dc046264172fbb26b39147bf7-132",
        "dab72f2dc046264172fbb26b39147bf7-137",
        "dab72f2dc046264172fbb26b39147bf7-142",
        "dab72f2dc046264172fbb26b39147bf7-147"
      ],
      "ground_truth_text": "[[3.984, -0.026], [6.604, -0.018], [8.362, -0.004], [9.045, 0.004], [9.199, 0.007]]",
      "frame_name": "dab72f2dc046264172fbb26b39147bf7-147",
      "anchor_image_path": "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "dab72f2dc046264172fbb26b39147bf7",
      "scenario_cluster": "Intersection",
      "bundle_id": "dab72f2dc046264172fbb26b39147bf7:group_147",
      "image_paths": [
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-127_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-132_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-137_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-142_FRONT.jpg",
        "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-147_FRONT.jpg"
      ],
      "image_path": "dab72f2dc046264172fbb26b39147bf7/CAM_FRONT/dab72f2dc046264172fbb26b39147bf7-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-128_FRONT.jpg",
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-133_FRONT.jpg",
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-138_FRONT.jpg",
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-143_FRONT.jpg",
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_dbdafa374e2756a14a982015ea6d512e_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.97, -0.21], [8.904, -1.042], [14.69, -2.416], [20.949, -4.193], [27.5, -6.287]]",
      "bundle_frame_names": [
        "dbdafa374e2756a14a982015ea6d512e-128",
        "dbdafa374e2756a14a982015ea6d512e-133",
        "dbdafa374e2756a14a982015ea6d512e-138",
        "dbdafa374e2756a14a982015ea6d512e-143",
        "dbdafa374e2756a14a982015ea6d512e-148"
      ],
      "ground_truth_text": "[[3.97, -0.21], [8.904, -1.042], [14.69, -2.416], [20.949, -4.193], [27.5, -6.287]]",
      "frame_name": "dbdafa374e2756a14a982015ea6d512e-148",
      "anchor_image_path": "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "dbdafa374e2756a14a982015ea6d512e",
      "scenario_cluster": "Intersection",
      "bundle_id": "dbdafa374e2756a14a982015ea6d512e:group_148",
      "image_paths": [
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        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-133_FRONT.jpg",
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-138_FRONT.jpg",
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-143_FRONT.jpg",
        "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-148_FRONT.jpg"
      ],
      "image_path": "dbdafa374e2756a14a982015ea6d512e/CAM_FRONT/dbdafa374e2756a14a982015ea6d512e-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-128_FRONT.jpg",
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-133_FRONT.jpg",
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-138_FRONT.jpg",
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-143_FRONT.jpg",
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_dfa20c65dcc57c6ccabc47a7cc96d13f_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.799, -0.229], [5.816, -0.431], [8.569, -0.744], [12.612, -1.278], [17.962, -2.12]]",
      "bundle_frame_names": [
        "dfa20c65dcc57c6ccabc47a7cc96d13f-128",
        "dfa20c65dcc57c6ccabc47a7cc96d13f-133",
        "dfa20c65dcc57c6ccabc47a7cc96d13f-138",
        "dfa20c65dcc57c6ccabc47a7cc96d13f-143",
        "dfa20c65dcc57c6ccabc47a7cc96d13f-148"
      ],
      "ground_truth_text": "[[3.799, -0.229], [5.816, -0.431], [8.569, -0.744], [12.612, -1.278], [17.962, -2.12]]",
      "frame_name": "dfa20c65dcc57c6ccabc47a7cc96d13f-148",
      "anchor_image_path": "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "dfa20c65dcc57c6ccabc47a7cc96d13f",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "dfa20c65dcc57c6ccabc47a7cc96d13f:group_148",
      "image_paths": [
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-128_FRONT.jpg",
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-133_FRONT.jpg",
        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-138_FRONT.jpg",
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        "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-148_FRONT.jpg"
      ],
      "image_path": "dfa20c65dcc57c6ccabc47a7cc96d13f/CAM_FRONT/dfa20c65dcc57c6ccabc47a7cc96d13f-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-128_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-133_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-138_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-143_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_ea9000098840a70220698bd12b167f97_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[5.082, -0.255], [10.466, -0.856], [16.088, -1.858], [21.516, -3.238], [26.591, -4.799]]",
      "bundle_frame_names": [
        "ea9000098840a70220698bd12b167f97-128",
        "ea9000098840a70220698bd12b167f97-133",
        "ea9000098840a70220698bd12b167f97-138",
        "ea9000098840a70220698bd12b167f97-143",
        "ea9000098840a70220698bd12b167f97-148"
      ],
      "ground_truth_text": "[[5.082, -0.255], [10.466, -0.856], [16.088, -1.858], [21.516, -3.238], [26.591, -4.799]]",
      "frame_name": "ea9000098840a70220698bd12b167f97-148",
      "anchor_image_path": "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "ea9000098840a70220698bd12b167f97",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "ea9000098840a70220698bd12b167f97:group_148",
      "image_paths": [
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-128_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-133_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-138_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-143_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg"
      ],
      "image_path": "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-127_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-132_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-137_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-142_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_f0236dc2f2fb6612f8d6abda39369974_group_147_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[1.311, -0.002], [3.959, 0.092], [7.374, 0.92], [10.679, 3.263], [12.989, 7.237]]",
      "bundle_frame_names": [
        "f0236dc2f2fb6612f8d6abda39369974-127",
        "f0236dc2f2fb6612f8d6abda39369974-132",
        "f0236dc2f2fb6612f8d6abda39369974-137",
        "f0236dc2f2fb6612f8d6abda39369974-142",
        "f0236dc2f2fb6612f8d6abda39369974-147"
      ],
      "ground_truth_text": "[[1.311, -0.002], [3.959, 0.092], [7.374, 0.92], [10.679, 3.263], [12.989, 7.237]]",
      "frame_name": "f0236dc2f2fb6612f8d6abda39369974-147",
      "anchor_image_path": "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f0236dc2f2fb6612f8d6abda39369974",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "f0236dc2f2fb6612f8d6abda39369974:group_147",
      "image_paths": [
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        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-132_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-137_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-142_FRONT.jpg",
        "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg"
      ],
      "image_path": "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-128_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-133_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-138_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-143_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_f1dd0e328596edd3c4cd977a0d09aef5_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[21.928, -0.769], [43.733, -2.966], [65.416, -6.573], [86.949, -11.594], [108.278, -18.012]]",
      "bundle_frame_names": [
        "f1dd0e328596edd3c4cd977a0d09aef5-128",
        "f1dd0e328596edd3c4cd977a0d09aef5-133",
        "f1dd0e328596edd3c4cd977a0d09aef5-138",
        "f1dd0e328596edd3c4cd977a0d09aef5-143",
        "f1dd0e328596edd3c4cd977a0d09aef5-148"
      ],
      "ground_truth_text": "[[21.928, -0.769], [43.733, -2.966], [65.416, -6.573], [86.949, -11.594], [108.278, -18.012]]",
      "frame_name": "f1dd0e328596edd3c4cd977a0d09aef5-148",
      "anchor_image_path": "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f1dd0e328596edd3c4cd977a0d09aef5",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "f1dd0e328596edd3c4cd977a0d09aef5:group_148",
      "image_paths": [
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      ],
      "image_path": "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-133_FRONT.jpg",
        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-138_FRONT.jpg",
        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-143_FRONT.jpg",
        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_f5c835e8c74b712a68a09e9a776885e4_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[6.195, -0.002], [11.366, -0.014], [16.564, -0.045], [22.57, -0.096], [29.032, -0.105]]",
      "bundle_frame_names": [
        "f5c835e8c74b712a68a09e9a776885e4-128",
        "f5c835e8c74b712a68a09e9a776885e4-133",
        "f5c835e8c74b712a68a09e9a776885e4-138",
        "f5c835e8c74b712a68a09e9a776885e4-143",
        "f5c835e8c74b712a68a09e9a776885e4-148"
      ],
      "ground_truth_text": "[[6.195, -0.002], [11.366, -0.014], [16.564, -0.045], [22.57, -0.096], [29.032, -0.105]]",
      "frame_name": "f5c835e8c74b712a68a09e9a776885e4-148",
      "anchor_image_path": "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f5c835e8c74b712a68a09e9a776885e4",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "f5c835e8c74b712a68a09e9a776885e4:group_148",
      "image_paths": [
        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-128_FRONT.jpg",
        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-133_FRONT.jpg",
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        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-143_FRONT.jpg",
        "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-148_FRONT.jpg"
      ],
      "image_path": "f5c835e8c74b712a68a09e9a776885e4/CAM_FRONT/f5c835e8c74b712a68a09e9a776885e4-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-128_FRONT.jpg",
        "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-133_FRONT.jpg",
        "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-138_FRONT.jpg",
        "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-143_FRONT.jpg",
        "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_fa3deaf57d0df0e3057722177a5e27fc_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[3.811, -0.533], [7.217, -1.551], [11.62, -3.719], [17.02, -7.665], [22.743, -12.927]]",
      "bundle_frame_names": [
        "fa3deaf57d0df0e3057722177a5e27fc-128",
        "fa3deaf57d0df0e3057722177a5e27fc-133",
        "fa3deaf57d0df0e3057722177a5e27fc-138",
        "fa3deaf57d0df0e3057722177a5e27fc-143",
        "fa3deaf57d0df0e3057722177a5e27fc-148"
      ],
      "ground_truth_text": "[[3.811, -0.533], [7.217, -1.551], [11.62, -3.719], [17.02, -7.665], [22.743, -12.927]]",
      "frame_name": "fa3deaf57d0df0e3057722177a5e27fc-148",
      "anchor_image_path": "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "fa3deaf57d0df0e3057722177a5e27fc",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "fa3deaf57d0df0e3057722177a5e27fc:group_148",
      "image_paths": [
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        "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-148_FRONT.jpg"
      ],
      "image_path": "fa3deaf57d0df0e3057722177a5e27fc/CAM_FRONT/fa3deaf57d0df0e3057722177a5e27fc-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-133_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-138_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-143_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, predict the ego vehicle trajectory for the next 5 future frames sampled over the full future horizon in the current ego-centric coordinate system anchored at the last observed frame. The current frame is [0.0, 0.0]. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_fd7cce36d47f456b4811d3ca4807517e_group_148_TRJ-5",
      "id": "TRJ-5",
      "ground_truth": "[[6.093, -0.47], [13.109, -1.035], [20.62, -1.571], [28.376, -2.048], [36.642, -2.595]]",
      "bundle_frame_names": [
        "fd7cce36d47f456b4811d3ca4807517e-128",
        "fd7cce36d47f456b4811d3ca4807517e-133",
        "fd7cce36d47f456b4811d3ca4807517e-138",
        "fd7cce36d47f456b4811d3ca4807517e-143",
        "fd7cce36d47f456b4811d3ca4807517e-148"
      ],
      "ground_truth_text": "[[6.093, -0.47], [13.109, -1.035], [20.62, -1.571], [28.376, -2.048], [36.642, -2.595]]",
      "frame_name": "fd7cce36d47f456b4811d3ca4807517e-148",
      "anchor_image_path": "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "fd7cce36d47f456b4811d3ca4807517e",
      "scenario_cluster": "Intersection",
      "bundle_id": "fd7cce36d47f456b4811d3ca4807517e:group_148",
      "image_paths": [
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-128_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-133_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-138_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-143_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-148_FRONT.jpg"
      ],
      "image_path": "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-128_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-133_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-138_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-143_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [3.488, 0.091]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_06a63756c3dc03b0a1778312c6468d58_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[6.963, 0.116], [10.263, 0.352], [13.3, 1.064], [15.822, 2.086]]",
      "bundle_frame_names": [
        "06a63756c3dc03b0a1778312c6468d58-128",
        "06a63756c3dc03b0a1778312c6468d58-133",
        "06a63756c3dc03b0a1778312c6468d58-138",
        "06a63756c3dc03b0a1778312c6468d58-143",
        "06a63756c3dc03b0a1778312c6468d58-148"
      ],
      "ground_truth_text": "[[6.963, 0.116], [10.263, 0.352], [13.3, 1.064], [15.822, 2.086]]",
      "frame_name": "06a63756c3dc03b0a1778312c6468d58-148",
      "anchor_image_path": "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "06a63756c3dc03b0a1778312c6468d58",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "06a63756c3dc03b0a1778312c6468d58:group_148",
      "image_paths": [
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-128_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-133_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-138_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-143_FRONT.jpg",
        "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg"
      ],
      "image_path": "06a63756c3dc03b0a1778312c6468d58/CAM_FRONT/06a63756c3dc03b0a1778312c6468d58-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-129_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-134_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-139_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-144_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [9.523, 0.152]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_0cdf4bedea5f52bfb54edea6cbdbb15e_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[18.071, 0.475], [25.782, 0.816], [32.45, 1.086], [37.702, 1.285]]",
      "bundle_frame_names": [
        "0cdf4bedea5f52bfb54edea6cbdbb15e-129",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-134",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-139",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-144",
        "0cdf4bedea5f52bfb54edea6cbdbb15e-149"
      ],
      "ground_truth_text": "[[18.071, 0.475], [25.782, 0.816], [32.45, 1.086], [37.702, 1.285]]",
      "frame_name": "0cdf4bedea5f52bfb54edea6cbdbb15e-149",
      "anchor_image_path": "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0cdf4bedea5f52bfb54edea6cbdbb15e",
      "scenario_cluster": "Intersection",
      "bundle_id": "0cdf4bedea5f52bfb54edea6cbdbb15e:group_149",
      "image_paths": [
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-129_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-134_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-139_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-144_FRONT.jpg",
        "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-149_FRONT.jpg"
      ],
      "image_path": "0cdf4bedea5f52bfb54edea6cbdbb15e/CAM_FRONT/0cdf4bedea5f52bfb54edea6cbdbb15e-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-129_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-134_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-139_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-144_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [8.311, -0.047]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_0d94d2a36405c21b1fc19601c41e8d23_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[15.499, -0.134], [20.323, -0.202], [22.583, -0.212], [23.172, -0.211]]",
      "bundle_frame_names": [
        "0d94d2a36405c21b1fc19601c41e8d23-129",
        "0d94d2a36405c21b1fc19601c41e8d23-134",
        "0d94d2a36405c21b1fc19601c41e8d23-139",
        "0d94d2a36405c21b1fc19601c41e8d23-144",
        "0d94d2a36405c21b1fc19601c41e8d23-149"
      ],
      "ground_truth_text": "[[15.499, -0.134], [20.323, -0.202], [22.583, -0.212], [23.172, -0.211]]",
      "frame_name": "0d94d2a36405c21b1fc19601c41e8d23-149",
      "anchor_image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0d94d2a36405c21b1fc19601c41e8d23",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "0d94d2a36405c21b1fc19601c41e8d23:group_149",
      "image_paths": [
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-129_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-134_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-139_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-144_FRONT.jpg",
        "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg"
      ],
      "image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-128_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-133_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-138_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-143_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [6.139, -0.006]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_0e871c9836cb94ce7adaf502567b2b35_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[8.886, -0.018], [11.739, -0.032], [15.543, -0.068], [20.25, -0.095]]",
      "bundle_frame_names": [
        "0e871c9836cb94ce7adaf502567b2b35-128",
        "0e871c9836cb94ce7adaf502567b2b35-133",
        "0e871c9836cb94ce7adaf502567b2b35-138",
        "0e871c9836cb94ce7adaf502567b2b35-143",
        "0e871c9836cb94ce7adaf502567b2b35-148"
      ],
      "ground_truth_text": "[[8.886, -0.018], [11.739, -0.032], [15.543, -0.068], [20.25, -0.095]]",
      "frame_name": "0e871c9836cb94ce7adaf502567b2b35-148",
      "anchor_image_path": "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0e871c9836cb94ce7adaf502567b2b35",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "0e871c9836cb94ce7adaf502567b2b35:group_148",
      "image_paths": [
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-128_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-133_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-138_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-143_FRONT.jpg",
        "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-148_FRONT.jpg"
      ],
      "image_path": "0e871c9836cb94ce7adaf502567b2b35/CAM_FRONT/0e871c9836cb94ce7adaf502567b2b35-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-127_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-132_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-137_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-142_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [8.97, 0.178]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_1a13b64b69b568c4ccbb572bc3f0257b_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[18.443, 0.403], [27.604, 0.423], [35.298, 0.258], [41.962, -0.064]]",
      "bundle_frame_names": [
        "1a13b64b69b568c4ccbb572bc3f0257b-127",
        "1a13b64b69b568c4ccbb572bc3f0257b-132",
        "1a13b64b69b568c4ccbb572bc3f0257b-137",
        "1a13b64b69b568c4ccbb572bc3f0257b-142",
        "1a13b64b69b568c4ccbb572bc3f0257b-147"
      ],
      "ground_truth_text": "[[18.443, 0.403], [27.604, 0.423], [35.298, 0.258], [41.962, -0.064]]",
      "frame_name": "1a13b64b69b568c4ccbb572bc3f0257b-147",
      "anchor_image_path": "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "1a13b64b69b568c4ccbb572bc3f0257b",
      "scenario_cluster": "Cyclist",
      "bundle_id": "1a13b64b69b568c4ccbb572bc3f0257b:group_147",
      "image_paths": [
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-127_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-132_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-137_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-142_FRONT.jpg",
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg"
      ],
      "image_path": "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-127_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-132_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-137_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-142_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.292, 0.01]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_241e6e041eacfe34b21cde7b630c270c_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[0.289, 0.009], [0.851, 0.027], [2.626, 0.075], [5.364, 0.115]]",
      "bundle_frame_names": [
        "241e6e041eacfe34b21cde7b630c270c-127",
        "241e6e041eacfe34b21cde7b630c270c-132",
        "241e6e041eacfe34b21cde7b630c270c-137",
        "241e6e041eacfe34b21cde7b630c270c-142",
        "241e6e041eacfe34b21cde7b630c270c-147"
      ],
      "ground_truth_text": "[[0.289, 0.009], [0.851, 0.027], [2.626, 0.075], [5.364, 0.115]]",
      "frame_name": "241e6e041eacfe34b21cde7b630c270c-147",
      "anchor_image_path": "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "241e6e041eacfe34b21cde7b630c270c",
      "scenario_cluster": "Cyclist",
      "bundle_id": "241e6e041eacfe34b21cde7b630c270c:group_147",
      "image_paths": [
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-127_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-132_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-137_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-142_FRONT.jpg",
        "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg"
      ],
      "image_path": "241e6e041eacfe34b21cde7b630c270c/CAM_FRONT/241e6e041eacfe34b21cde7b630c270c-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-128_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-133_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-138_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-143_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.422, 0.001]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_262e00554925cca1cf924ed5ea430d66_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[2.281, 0.005], [5.215, -0.338], [8.136, -1.632], [10.483, -4.308]]",
      "bundle_frame_names": [
        "262e00554925cca1cf924ed5ea430d66-128",
        "262e00554925cca1cf924ed5ea430d66-133",
        "262e00554925cca1cf924ed5ea430d66-138",
        "262e00554925cca1cf924ed5ea430d66-143",
        "262e00554925cca1cf924ed5ea430d66-148"
      ],
      "ground_truth_text": "[[2.281, 0.005], [5.215, -0.338], [8.136, -1.632], [10.483, -4.308]]",
      "frame_name": "262e00554925cca1cf924ed5ea430d66-148",
      "anchor_image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "262e00554925cca1cf924ed5ea430d66",
      "scenario_cluster": "Intersection",
      "bundle_id": "262e00554925cca1cf924ed5ea430d66:group_148",
      "image_paths": [
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-128_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-133_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-138_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-143_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg"
      ],
      "image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-127_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-132_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-137_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-142_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [3.122, -0.011]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_291acc4578630f6231a35e718de21523_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[3.728, -0.011], [4.947, -0.01], [7.489, 0.0], [11.549, 0.036]]",
      "bundle_frame_names": [
        "291acc4578630f6231a35e718de21523-127",
        "291acc4578630f6231a35e718de21523-132",
        "291acc4578630f6231a35e718de21523-137",
        "291acc4578630f6231a35e718de21523-142",
        "291acc4578630f6231a35e718de21523-147"
      ],
      "ground_truth_text": "[[3.728, -0.011], [4.947, -0.01], [7.489, 0.0], [11.549, 0.036]]",
      "frame_name": "291acc4578630f6231a35e718de21523-147",
      "anchor_image_path": "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "291acc4578630f6231a35e718de21523",
      "scenario_cluster": "Cyclist",
      "bundle_id": "291acc4578630f6231a35e718de21523:group_147",
      "image_paths": [
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-127_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-132_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-137_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-142_FRONT.jpg",
        "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg"
      ],
      "image_path": "291acc4578630f6231a35e718de21523/CAM_FRONT/291acc4578630f6231a35e718de21523-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-128_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-133_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-138_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-143_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [8.265, -0.021]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_29b60d9832de56b7b0047692136bf918_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[15.121, -0.08], [21.483, -0.181], [26.51, -0.248], [30.51, -0.248]]",
      "bundle_frame_names": [
        "29b60d9832de56b7b0047692136bf918-128",
        "29b60d9832de56b7b0047692136bf918-133",
        "29b60d9832de56b7b0047692136bf918-138",
        "29b60d9832de56b7b0047692136bf918-143",
        "29b60d9832de56b7b0047692136bf918-148"
      ],
      "ground_truth_text": "[[15.121, -0.08], [21.483, -0.181], [26.51, -0.248], [30.51, -0.248]]",
      "frame_name": "29b60d9832de56b7b0047692136bf918-148",
      "anchor_image_path": "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "29b60d9832de56b7b0047692136bf918",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "29b60d9832de56b7b0047692136bf918:group_148",
      "image_paths": [
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-128_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-133_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-138_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-143_FRONT.jpg",
        "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg"
      ],
      "image_path": "29b60d9832de56b7b0047692136bf918/CAM_FRONT/29b60d9832de56b7b0047692136bf918-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-128_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-133_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-138_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-143_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [6.382, -0.027]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_2fe32b2cddf4f6e328e294fca52e482f_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[9.571, -0.259], [9.622, -0.266], [9.801, -0.286], [9.817, -0.288]]",
      "bundle_frame_names": [
        "2fe32b2cddf4f6e328e294fca52e482f-128",
        "2fe32b2cddf4f6e328e294fca52e482f-133",
        "2fe32b2cddf4f6e328e294fca52e482f-138",
        "2fe32b2cddf4f6e328e294fca52e482f-143",
        "2fe32b2cddf4f6e328e294fca52e482f-148"
      ],
      "ground_truth_text": "[[9.571, -0.259], [9.622, -0.266], [9.801, -0.286], [9.817, -0.288]]",
      "frame_name": "2fe32b2cddf4f6e328e294fca52e482f-148",
      "anchor_image_path": "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "2fe32b2cddf4f6e328e294fca52e482f",
      "scenario_cluster": "Cyclist",
      "bundle_id": "2fe32b2cddf4f6e328e294fca52e482f:group_148",
      "image_paths": [
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-128_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-133_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-138_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-143_FRONT.jpg",
        "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg"
      ],
      "image_path": "2fe32b2cddf4f6e328e294fca52e482f/CAM_FRONT/2fe32b2cddf4f6e328e294fca52e482f-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-129_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-134_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-139_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-144_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [20.271, 0.04]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_398703cb3d52176faf9da2a53aac4b69_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[40.509, 0.256], [60.862, 0.51], [81.263, 0.751], [101.864, 1.024]]",
      "bundle_frame_names": [
        "398703cb3d52176faf9da2a53aac4b69-129",
        "398703cb3d52176faf9da2a53aac4b69-134",
        "398703cb3d52176faf9da2a53aac4b69-139",
        "398703cb3d52176faf9da2a53aac4b69-144",
        "398703cb3d52176faf9da2a53aac4b69-149"
      ],
      "ground_truth_text": "[[40.509, 0.256], [60.862, 0.51], [81.263, 0.751], [101.864, 1.024]]",
      "frame_name": "398703cb3d52176faf9da2a53aac4b69-149",
      "anchor_image_path": "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "398703cb3d52176faf9da2a53aac4b69",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "398703cb3d52176faf9da2a53aac4b69:group_149",
      "image_paths": [
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-129_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-134_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-139_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-144_FRONT.jpg",
        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-149_FRONT.jpg"
      ],
      "image_path": "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-129_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-134_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-139_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-144_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.819, 0.009]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_4758699520985474f05538b7856de1b7_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[2.997, 0.02], [3.783, 0.03], [4.229, 0.038], [4.405, 0.04]]",
      "bundle_frame_names": [
        "4758699520985474f05538b7856de1b7-129",
        "4758699520985474f05538b7856de1b7-134",
        "4758699520985474f05538b7856de1b7-139",
        "4758699520985474f05538b7856de1b7-144",
        "4758699520985474f05538b7856de1b7-149"
      ],
      "ground_truth_text": "[[2.997, 0.02], [3.783, 0.03], [4.229, 0.038], [4.405, 0.04]]",
      "frame_name": "4758699520985474f05538b7856de1b7-149",
      "anchor_image_path": "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "4758699520985474f05538b7856de1b7",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "4758699520985474f05538b7856de1b7:group_149",
      "image_paths": [
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-129_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-134_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-139_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-144_FRONT.jpg",
        "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg"
      ],
      "image_path": "4758699520985474f05538b7856de1b7/CAM_FRONT/4758699520985474f05538b7856de1b7-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-127_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-132_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-137_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-142_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.256, 0.0]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_57e34daab2df82dfd78edad9dfc8d56d_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[4.776, 0.057], [13.741, 0.283], [25.726, 0.75], [40.266, 1.451]]",
      "bundle_frame_names": [
        "57e34daab2df82dfd78edad9dfc8d56d-127",
        "57e34daab2df82dfd78edad9dfc8d56d-132",
        "57e34daab2df82dfd78edad9dfc8d56d-137",
        "57e34daab2df82dfd78edad9dfc8d56d-142",
        "57e34daab2df82dfd78edad9dfc8d56d-147"
      ],
      "ground_truth_text": "[[4.776, 0.057], [13.741, 0.283], [25.726, 0.75], [40.266, 1.451]]",
      "frame_name": "57e34daab2df82dfd78edad9dfc8d56d-147",
      "anchor_image_path": "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "57e34daab2df82dfd78edad9dfc8d56d",
      "scenario_cluster": "Intersection",
      "bundle_id": "57e34daab2df82dfd78edad9dfc8d56d:group_147",
      "image_paths": [
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-127_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-132_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-137_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-142_FRONT.jpg",
        "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg"
      ],
      "image_path": "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-129_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-134_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-139_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-144_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [15.08, 0.176]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_57e3f4d557f856f3b40d35329670f58f_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[31.01, 0.846], [47.559, 1.919], [64.643, 3.129], [82.219, 4.353]]",
      "bundle_frame_names": [
        "57e3f4d557f856f3b40d35329670f58f-129",
        "57e3f4d557f856f3b40d35329670f58f-134",
        "57e3f4d557f856f3b40d35329670f58f-139",
        "57e3f4d557f856f3b40d35329670f58f-144",
        "57e3f4d557f856f3b40d35329670f58f-149"
      ],
      "ground_truth_text": "[[31.01, 0.846], [47.559, 1.919], [64.643, 3.129], [82.219, 4.353]]",
      "frame_name": "57e3f4d557f856f3b40d35329670f58f-149",
      "anchor_image_path": "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "57e3f4d557f856f3b40d35329670f58f",
      "scenario_cluster": "Construction",
      "bundle_id": "57e3f4d557f856f3b40d35329670f58f:group_149",
      "image_paths": [
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-129_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-134_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-139_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-144_FRONT.jpg",
        "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg"
      ],
      "image_path": "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-129_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-134_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-139_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-144_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [3.281, -0.073]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_5934e42d9ea6a90094eff972eb6d7e4d_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[6.44, -0.204], [9.503, -0.338], [13.08, -0.436], [17.454, -0.524]]",
      "bundle_frame_names": [
        "5934e42d9ea6a90094eff972eb6d7e4d-129",
        "5934e42d9ea6a90094eff972eb6d7e4d-134",
        "5934e42d9ea6a90094eff972eb6d7e4d-139",
        "5934e42d9ea6a90094eff972eb6d7e4d-144",
        "5934e42d9ea6a90094eff972eb6d7e4d-149"
      ],
      "ground_truth_text": "[[6.44, -0.204], [9.503, -0.338], [13.08, -0.436], [17.454, -0.524]]",
      "frame_name": "5934e42d9ea6a90094eff972eb6d7e4d-149",
      "anchor_image_path": "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5934e42d9ea6a90094eff972eb6d7e4d",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "5934e42d9ea6a90094eff972eb6d7e4d:group_149",
      "image_paths": [
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-129_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-134_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-139_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-144_FRONT.jpg",
        "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-149_FRONT.jpg"
      ],
      "image_path": "5934e42d9ea6a90094eff972eb6d7e4d/CAM_FRONT/5934e42d9ea6a90094eff972eb6d7e4d-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-129_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-134_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-139_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-144_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.179, 0.0]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_5ca4bdec47a8ba33a309cb4329b1ac1c_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[1.008, 0.003], [2.09, 0.008], [3.346, 0.008], [5.0, 0.007]]",
      "bundle_frame_names": [
        "5ca4bdec47a8ba33a309cb4329b1ac1c-129",
        "5ca4bdec47a8ba33a309cb4329b1ac1c-134",
        "5ca4bdec47a8ba33a309cb4329b1ac1c-139",
        "5ca4bdec47a8ba33a309cb4329b1ac1c-144",
        "5ca4bdec47a8ba33a309cb4329b1ac1c-149"
      ],
      "ground_truth_text": "[[1.008, 0.003], [2.09, 0.008], [3.346, 0.008], [5.0, 0.007]]",
      "frame_name": "5ca4bdec47a8ba33a309cb4329b1ac1c-149",
      "anchor_image_path": "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5ca4bdec47a8ba33a309cb4329b1ac1c",
      "scenario_cluster": "Cyclist",
      "bundle_id": "5ca4bdec47a8ba33a309cb4329b1ac1c:group_149",
      "image_paths": [
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-129_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-134_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-139_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-144_FRONT.jpg",
        "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-149_FRONT.jpg"
      ],
      "image_path": "5ca4bdec47a8ba33a309cb4329b1ac1c/CAM_FRONT/5ca4bdec47a8ba33a309cb4329b1ac1c-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-128_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-133_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-138_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-143_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [21.197, 0.072]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_5f0d91d9bebc6af381629682682edcf8_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[41.777, 0.375], [61.985, 0.958], [81.954, 1.622], [101.588, 2.208]]",
      "bundle_frame_names": [
        "5f0d91d9bebc6af381629682682edcf8-128",
        "5f0d91d9bebc6af381629682682edcf8-133",
        "5f0d91d9bebc6af381629682682edcf8-138",
        "5f0d91d9bebc6af381629682682edcf8-143",
        "5f0d91d9bebc6af381629682682edcf8-148"
      ],
      "ground_truth_text": "[[41.777, 0.375], [61.985, 0.958], [81.954, 1.622], [101.588, 2.208]]",
      "frame_name": "5f0d91d9bebc6af381629682682edcf8-148",
      "anchor_image_path": "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5f0d91d9bebc6af381629682682edcf8",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "5f0d91d9bebc6af381629682682edcf8:group_148",
      "image_paths": [
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-128_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-133_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-138_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-143_FRONT.jpg",
        "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg"
      ],
      "image_path": "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-129_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-134_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-139_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-144_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.247, -0.0]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_60d8cd7bcba63512079dd96f6b8fdfab_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[2.089, -0.002], [5.831, 0.008], [11.225, 0.082], [18.236, 0.317]]",
      "bundle_frame_names": [
        "60d8cd7bcba63512079dd96f6b8fdfab-129",
        "60d8cd7bcba63512079dd96f6b8fdfab-134",
        "60d8cd7bcba63512079dd96f6b8fdfab-139",
        "60d8cd7bcba63512079dd96f6b8fdfab-144",
        "60d8cd7bcba63512079dd96f6b8fdfab-149"
      ],
      "ground_truth_text": "[[2.089, -0.002], [5.831, 0.008], [11.225, 0.082], [18.236, 0.317]]",
      "frame_name": "60d8cd7bcba63512079dd96f6b8fdfab-149",
      "anchor_image_path": "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "60d8cd7bcba63512079dd96f6b8fdfab",
      "scenario_cluster": "Cyclist",
      "bundle_id": "60d8cd7bcba63512079dd96f6b8fdfab:group_149",
      "image_paths": [
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-129_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-134_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-139_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-144_FRONT.jpg",
        "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-149_FRONT.jpg"
      ],
      "image_path": "60d8cd7bcba63512079dd96f6b8fdfab/CAM_FRONT/60d8cd7bcba63512079dd96f6b8fdfab-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-132_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-137_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-142_FRONT.jpg",
        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [8.694, -1.031]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_7331710f59abdde8b10bce1c48d49b3f_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[19.568, -2.879], [32.119, -4.75], [45.9, -6.506], [60.562, -8.291]]",
      "bundle_frame_names": [
        "7331710f59abdde8b10bce1c48d49b3f-127",
        "7331710f59abdde8b10bce1c48d49b3f-132",
        "7331710f59abdde8b10bce1c48d49b3f-137",
        "7331710f59abdde8b10bce1c48d49b3f-142",
        "7331710f59abdde8b10bce1c48d49b3f-147"
      ],
      "ground_truth_text": "[[19.568, -2.879], [32.119, -4.75], [45.9, -6.506], [60.562, -8.291]]",
      "frame_name": "7331710f59abdde8b10bce1c48d49b3f-147",
      "anchor_image_path": "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7331710f59abdde8b10bce1c48d49b3f",
      "scenario_cluster": "Intersection",
      "bundle_id": "7331710f59abdde8b10bce1c48d49b3f:group_147",
      "image_paths": [
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        "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg"
      ],
      "image_path": "7331710f59abdde8b10bce1c48d49b3f/CAM_FRONT/7331710f59abdde8b10bce1c48d49b3f-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-128_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-133_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-138_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-143_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.39, -0.132]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_774c537b284a58c77de4106b1c190117_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[0.78, -0.275], [1.169, -0.399], [1.214, -0.409], [1.223, -0.407]]",
      "bundle_frame_names": [
        "774c537b284a58c77de4106b1c190117-128",
        "774c537b284a58c77de4106b1c190117-133",
        "774c537b284a58c77de4106b1c190117-138",
        "774c537b284a58c77de4106b1c190117-143",
        "774c537b284a58c77de4106b1c190117-148"
      ],
      "ground_truth_text": "[[0.78, -0.275], [1.169, -0.399], [1.214, -0.409], [1.223, -0.407]]",
      "frame_name": "774c537b284a58c77de4106b1c190117-148",
      "anchor_image_path": "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "774c537b284a58c77de4106b1c190117",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "774c537b284a58c77de4106b1c190117:group_148",
      "image_paths": [
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-128_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-133_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-138_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-143_FRONT.jpg",
        "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg"
      ],
      "image_path": "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-134_FRONT.jpg",
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        "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.396, 0.0]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_7854e250a1043af72519707e793680b1_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[0.844, 0.0], [1.379, 0.003], [2.52, 0.014], [5.486, 0.063]]",
      "bundle_frame_names": [
        "7854e250a1043af72519707e793680b1-129",
        "7854e250a1043af72519707e793680b1-134",
        "7854e250a1043af72519707e793680b1-139",
        "7854e250a1043af72519707e793680b1-144",
        "7854e250a1043af72519707e793680b1-149"
      ],
      "ground_truth_text": "[[0.844, 0.0], [1.379, 0.003], [2.52, 0.014], [5.486, 0.063]]",
      "frame_name": "7854e250a1043af72519707e793680b1-149",
      "anchor_image_path": "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7854e250a1043af72519707e793680b1",
      "scenario_cluster": "Cyclist",
      "bundle_id": "7854e250a1043af72519707e793680b1:group_149",
      "image_paths": [
        "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-129_FRONT.jpg",
        "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-134_FRONT.jpg",
        "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-139_FRONT.jpg",
        "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-144_FRONT.jpg",
        "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-149_FRONT.jpg"
      ],
      "image_path": "7854e250a1043af72519707e793680b1/CAM_FRONT/7854e250a1043af72519707e793680b1-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-128_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-133_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-138_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-143_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [3.288, 0.017]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_7deba62759cbbac95e741a5c527ef932_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[4.753, 0.022], [5.137, 0.023], [5.567, 0.024], [6.967, 0.026]]",
      "bundle_frame_names": [
        "7deba62759cbbac95e741a5c527ef932-128",
        "7deba62759cbbac95e741a5c527ef932-133",
        "7deba62759cbbac95e741a5c527ef932-138",
        "7deba62759cbbac95e741a5c527ef932-143",
        "7deba62759cbbac95e741a5c527ef932-148"
      ],
      "ground_truth_text": "[[4.753, 0.022], [5.137, 0.023], [5.567, 0.024], [6.967, 0.026]]",
      "frame_name": "7deba62759cbbac95e741a5c527ef932-148",
      "anchor_image_path": "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7deba62759cbbac95e741a5c527ef932",
      "scenario_cluster": "Cyclist",
      "bundle_id": "7deba62759cbbac95e741a5c527ef932:group_148",
      "image_paths": [
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-128_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-133_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-138_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-143_FRONT.jpg",
        "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg"
      ],
      "image_path": "7deba62759cbbac95e741a5c527ef932/CAM_FRONT/7deba62759cbbac95e741a5c527ef932-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-127_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-132_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-137_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-142_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.939, 0.312]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_800692ff67909e7d79fadbb2849c71ce_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[1.336, 0.494], [1.346, 0.499], [1.346, 0.499], [1.346, 0.499]]",
      "bundle_frame_names": [
        "800692ff67909e7d79fadbb2849c71ce-127",
        "800692ff67909e7d79fadbb2849c71ce-132",
        "800692ff67909e7d79fadbb2849c71ce-137",
        "800692ff67909e7d79fadbb2849c71ce-142",
        "800692ff67909e7d79fadbb2849c71ce-147"
      ],
      "ground_truth_text": "[[1.336, 0.494], [1.346, 0.499], [1.346, 0.499], [1.346, 0.499]]",
      "frame_name": "800692ff67909e7d79fadbb2849c71ce-147",
      "anchor_image_path": "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "800692ff67909e7d79fadbb2849c71ce",
      "scenario_cluster": "Intersection",
      "bundle_id": "800692ff67909e7d79fadbb2849c71ce:group_147",
      "image_paths": [
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-127_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-132_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-137_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-142_FRONT.jpg",
        "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-147_FRONT.jpg"
      ],
      "image_path": "800692ff67909e7d79fadbb2849c71ce/CAM_FRONT/800692ff67909e7d79fadbb2849c71ce-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-128_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-133_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-138_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-143_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.136, 0.001]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_8b6660a0653c8339753d8db89299e308_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[1.193, -0.0], [3.47, 0.014], [6.724, 0.39], [10.442, 1.94]]",
      "bundle_frame_names": [
        "8b6660a0653c8339753d8db89299e308-128",
        "8b6660a0653c8339753d8db89299e308-133",
        "8b6660a0653c8339753d8db89299e308-138",
        "8b6660a0653c8339753d8db89299e308-143",
        "8b6660a0653c8339753d8db89299e308-148"
      ],
      "ground_truth_text": "[[1.193, -0.0], [3.47, 0.014], [6.724, 0.39], [10.442, 1.94]]",
      "frame_name": "8b6660a0653c8339753d8db89299e308-148",
      "anchor_image_path": "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "8b6660a0653c8339753d8db89299e308",
      "scenario_cluster": "Cyclist",
      "bundle_id": "8b6660a0653c8339753d8db89299e308:group_148",
      "image_paths": [
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-128_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-133_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-138_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-143_FRONT.jpg",
        "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg"
      ],
      "image_path": "8b6660a0653c8339753d8db89299e308/CAM_FRONT/8b6660a0653c8339753d8db89299e308-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-128_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-133_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-138_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-143_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.516, -0.106]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_9153c9087daf1a90427913fc5b34c886_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[3.605, -0.406], [5.086, -0.806], [5.167, -0.834], [5.161, -0.83]]",
      "bundle_frame_names": [
        "9153c9087daf1a90427913fc5b34c886-128",
        "9153c9087daf1a90427913fc5b34c886-133",
        "9153c9087daf1a90427913fc5b34c886-138",
        "9153c9087daf1a90427913fc5b34c886-143",
        "9153c9087daf1a90427913fc5b34c886-148"
      ],
      "ground_truth_text": "[[3.605, -0.406], [5.086, -0.806], [5.167, -0.834], [5.161, -0.83]]",
      "frame_name": "9153c9087daf1a90427913fc5b34c886-148",
      "anchor_image_path": "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "9153c9087daf1a90427913fc5b34c886",
      "scenario_cluster": "Intersection",
      "bundle_id": "9153c9087daf1a90427913fc5b34c886:group_148",
      "image_paths": [
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-128_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-133_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-138_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-143_FRONT.jpg",
        "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-148_FRONT.jpg"
      ],
      "image_path": "9153c9087daf1a90427913fc5b34c886/CAM_FRONT/9153c9087daf1a90427913fc5b34c886-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-129_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-134_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-139_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-144_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [3.168, -0.929]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_95aad68ad5531c1764066a71c4093238_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[6.319, -2.646], [9.599, -4.648], [13.059, -6.746], [16.712, -8.964]]",
      "bundle_frame_names": [
        "95aad68ad5531c1764066a71c4093238-129",
        "95aad68ad5531c1764066a71c4093238-134",
        "95aad68ad5531c1764066a71c4093238-139",
        "95aad68ad5531c1764066a71c4093238-144",
        "95aad68ad5531c1764066a71c4093238-149"
      ],
      "ground_truth_text": "[[6.319, -2.646], [9.599, -4.648], [13.059, -6.746], [16.712, -8.964]]",
      "frame_name": "95aad68ad5531c1764066a71c4093238-149",
      "anchor_image_path": "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "95aad68ad5531c1764066a71c4093238",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "95aad68ad5531c1764066a71c4093238:group_149",
      "image_paths": [
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-129_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-134_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-139_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-144_FRONT.jpg",
        "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg"
      ],
      "image_path": "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-128_FRONT.jpg",
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        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [3.748, -0.055]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_9d8cce1b82aeaec6173978bf27e1ff9a_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[6.911, -0.109], [9.133, -0.147], [10.663, -0.17], [11.136, -0.177]]",
      "bundle_frame_names": [
        "9d8cce1b82aeaec6173978bf27e1ff9a-128",
        "9d8cce1b82aeaec6173978bf27e1ff9a-133",
        "9d8cce1b82aeaec6173978bf27e1ff9a-138",
        "9d8cce1b82aeaec6173978bf27e1ff9a-143",
        "9d8cce1b82aeaec6173978bf27e1ff9a-148"
      ],
      "ground_truth_text": "[[6.911, -0.109], [9.133, -0.147], [10.663, -0.17], [11.136, -0.177]]",
      "frame_name": "9d8cce1b82aeaec6173978bf27e1ff9a-148",
      "anchor_image_path": "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "9d8cce1b82aeaec6173978bf27e1ff9a",
      "scenario_cluster": "Intersection",
      "bundle_id": "9d8cce1b82aeaec6173978bf27e1ff9a:group_148",
      "image_paths": [
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-128_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-133_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-138_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-143_FRONT.jpg",
        "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg"
      ],
      "image_path": "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-128_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-133_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-138_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-143_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.748, 0.038]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a089800aa672a22a6dcfd0404e82e92b_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[2.478, 0.064], [2.615, 0.066], [2.635, 0.067], [3.36, 0.107]]",
      "bundle_frame_names": [
        "a089800aa672a22a6dcfd0404e82e92b-128",
        "a089800aa672a22a6dcfd0404e82e92b-133",
        "a089800aa672a22a6dcfd0404e82e92b-138",
        "a089800aa672a22a6dcfd0404e82e92b-143",
        "a089800aa672a22a6dcfd0404e82e92b-148"
      ],
      "ground_truth_text": "[[2.478, 0.064], [2.615, 0.066], [2.635, 0.067], [3.36, 0.107]]",
      "frame_name": "a089800aa672a22a6dcfd0404e82e92b-148",
      "anchor_image_path": "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a089800aa672a22a6dcfd0404e82e92b",
      "scenario_cluster": "Cyclist",
      "bundle_id": "a089800aa672a22a6dcfd0404e82e92b:group_148",
      "image_paths": [
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-128_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-133_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-138_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-143_FRONT.jpg",
        "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-148_FRONT.jpg"
      ],
      "image_path": "a089800aa672a22a6dcfd0404e82e92b/CAM_FRONT/a089800aa672a22a6dcfd0404e82e92b-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-127_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-132_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-137_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-142_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [12.537, 0.101]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a54a6cab3409c4c47afbe4c2ac48ac90_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[24.93, 0.291], [36.795, 0.872], [47.642, 1.638], [57.108, 1.914]]",
      "bundle_frame_names": [
        "a54a6cab3409c4c47afbe4c2ac48ac90-127",
        "a54a6cab3409c4c47afbe4c2ac48ac90-132",
        "a54a6cab3409c4c47afbe4c2ac48ac90-137",
        "a54a6cab3409c4c47afbe4c2ac48ac90-142",
        "a54a6cab3409c4c47afbe4c2ac48ac90-147"
      ],
      "ground_truth_text": "[[24.93, 0.291], [36.795, 0.872], [47.642, 1.638], [57.108, 1.914]]",
      "frame_name": "a54a6cab3409c4c47afbe4c2ac48ac90-147",
      "anchor_image_path": "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a54a6cab3409c4c47afbe4c2ac48ac90",
      "scenario_cluster": "Cyclist",
      "bundle_id": "a54a6cab3409c4c47afbe4c2ac48ac90:group_147",
      "image_paths": [
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-127_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-132_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-137_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-142_FRONT.jpg",
        "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg"
      ],
      "image_path": "a54a6cab3409c4c47afbe4c2ac48ac90/CAM_FRONT/a54a6cab3409c4c47afbe4c2ac48ac90-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-128_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-133_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-138_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-143_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.474, -0.007]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_a9d28c43ea1d48e94fce413ecf10d17c_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[1.664, -0.008], [1.649, -0.008], [1.755, -0.01], [3.734, -0.007]]",
      "bundle_frame_names": [
        "a9d28c43ea1d48e94fce413ecf10d17c-128",
        "a9d28c43ea1d48e94fce413ecf10d17c-133",
        "a9d28c43ea1d48e94fce413ecf10d17c-138",
        "a9d28c43ea1d48e94fce413ecf10d17c-143",
        "a9d28c43ea1d48e94fce413ecf10d17c-148"
      ],
      "ground_truth_text": "[[1.664, -0.008], [1.649, -0.008], [1.755, -0.01], [3.734, -0.007]]",
      "frame_name": "a9d28c43ea1d48e94fce413ecf10d17c-148",
      "anchor_image_path": "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a9d28c43ea1d48e94fce413ecf10d17c",
      "scenario_cluster": "Intersection",
      "bundle_id": "a9d28c43ea1d48e94fce413ecf10d17c:group_148",
      "image_paths": [
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-128_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-133_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-138_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-143_FRONT.jpg",
        "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg"
      ],
      "image_path": "a9d28c43ea1d48e94fce413ecf10d17c/CAM_FRONT/a9d28c43ea1d48e94fce413ecf10d17c-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-128_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-133_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-138_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-143_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.078, 0.003]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_ac60422fa8ff502073e4d149e34dbfe7_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[1.836, 0.005], [2.759, -0.027], [4.036, -0.391], [5.895, -1.68]]",
      "bundle_frame_names": [
        "ac60422fa8ff502073e4d149e34dbfe7-128",
        "ac60422fa8ff502073e4d149e34dbfe7-133",
        "ac60422fa8ff502073e4d149e34dbfe7-138",
        "ac60422fa8ff502073e4d149e34dbfe7-143",
        "ac60422fa8ff502073e4d149e34dbfe7-148"
      ],
      "ground_truth_text": "[[1.836, 0.005], [2.759, -0.027], [4.036, -0.391], [5.895, -1.68]]",
      "frame_name": "ac60422fa8ff502073e4d149e34dbfe7-148",
      "anchor_image_path": "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "ac60422fa8ff502073e4d149e34dbfe7",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "ac60422fa8ff502073e4d149e34dbfe7:group_148",
      "image_paths": [
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-128_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-133_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-138_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-143_FRONT.jpg",
        "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-148_FRONT.jpg"
      ],
      "image_path": "ac60422fa8ff502073e4d149e34dbfe7/CAM_FRONT/ac60422fa8ff502073e4d149e34dbfe7-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-127_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-132_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-137_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-142_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [11.352, -0.348]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_ae122d63639893bfee253372882f8e8c_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[18.821, -0.709], [22.398, -0.809], [23.234, -0.818], [23.25, -0.815]]",
      "bundle_frame_names": [
        "ae122d63639893bfee253372882f8e8c-127",
        "ae122d63639893bfee253372882f8e8c-132",
        "ae122d63639893bfee253372882f8e8c-137",
        "ae122d63639893bfee253372882f8e8c-142",
        "ae122d63639893bfee253372882f8e8c-147"
      ],
      "ground_truth_text": "[[18.821, -0.709], [22.398, -0.809], [23.234, -0.818], [23.25, -0.815]]",
      "frame_name": "ae122d63639893bfee253372882f8e8c-147",
      "anchor_image_path": "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "ae122d63639893bfee253372882f8e8c",
      "scenario_cluster": "Intersection",
      "bundle_id": "ae122d63639893bfee253372882f8e8c:group_147",
      "image_paths": [
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-127_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-132_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-137_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-142_FRONT.jpg",
        "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-147_FRONT.jpg"
      ],
      "image_path": "ae122d63639893bfee253372882f8e8c/CAM_FRONT/ae122d63639893bfee253372882f8e8c-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-128_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-133_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-138_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-143_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.88, 0.013]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_ae776e971249029cab5f6b069a677897_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[0.874, 0.013], [0.873, 0.013], [0.873, 0.013], [0.873, 0.013]]",
      "bundle_frame_names": [
        "ae776e971249029cab5f6b069a677897-128",
        "ae776e971249029cab5f6b069a677897-133",
        "ae776e971249029cab5f6b069a677897-138",
        "ae776e971249029cab5f6b069a677897-143",
        "ae776e971249029cab5f6b069a677897-148"
      ],
      "ground_truth_text": "[[0.874, 0.013], [0.873, 0.013], [0.873, 0.013], [0.873, 0.013]]",
      "frame_name": "ae776e971249029cab5f6b069a677897-148",
      "anchor_image_path": "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "ae776e971249029cab5f6b069a677897",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "ae776e971249029cab5f6b069a677897:group_148",
      "image_paths": [
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-128_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-133_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-138_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-143_FRONT.jpg",
        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg"
      ],
      "image_path": "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-128_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-133_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-138_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-143_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [11.19, -0.014]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_b3c53b14746cce13a1ade988378276b7_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[16.82, -0.031], [20.122, -0.033], [24.569, -0.026], [29.948, 0.01]]",
      "bundle_frame_names": [
        "b3c53b14746cce13a1ade988378276b7-128",
        "b3c53b14746cce13a1ade988378276b7-133",
        "b3c53b14746cce13a1ade988378276b7-138",
        "b3c53b14746cce13a1ade988378276b7-143",
        "b3c53b14746cce13a1ade988378276b7-148"
      ],
      "ground_truth_text": "[[16.82, -0.031], [20.122, -0.033], [24.569, -0.026], [29.948, 0.01]]",
      "frame_name": "b3c53b14746cce13a1ade988378276b7-148",
      "anchor_image_path": "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "b3c53b14746cce13a1ade988378276b7",
      "scenario_cluster": "Cyclist",
      "bundle_id": "b3c53b14746cce13a1ade988378276b7:group_148",
      "image_paths": [
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-128_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-133_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-138_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-143_FRONT.jpg",
        "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg"
      ],
      "image_path": "b3c53b14746cce13a1ade988378276b7/CAM_FRONT/b3c53b14746cce13a1ade988378276b7-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-127_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-132_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-137_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-142_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [2.91, 0.084]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_bd09cff83130f018440c580cf71e54dc_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[3.743, 0.193], [4.347, 0.332], [5.565, 0.654], [6.816, 1.002]]",
      "bundle_frame_names": [
        "bd09cff83130f018440c580cf71e54dc-127",
        "bd09cff83130f018440c580cf71e54dc-132",
        "bd09cff83130f018440c580cf71e54dc-137",
        "bd09cff83130f018440c580cf71e54dc-142",
        "bd09cff83130f018440c580cf71e54dc-147"
      ],
      "ground_truth_text": "[[3.743, 0.193], [4.347, 0.332], [5.565, 0.654], [6.816, 1.002]]",
      "frame_name": "bd09cff83130f018440c580cf71e54dc-147",
      "anchor_image_path": "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "bd09cff83130f018440c580cf71e54dc",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "bd09cff83130f018440c580cf71e54dc:group_147",
      "image_paths": [
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-127_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-132_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-137_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-142_FRONT.jpg",
        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg"
      ],
      "image_path": "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-128_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-133_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-138_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-143_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.986, 0.003]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_bdcdf85c5e29f04d86d31a395ff01219_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[2.284, 0.004], [3.502, 0.023], [5.979, 0.079], [9.47, 0.173]]",
      "bundle_frame_names": [
        "bdcdf85c5e29f04d86d31a395ff01219-128",
        "bdcdf85c5e29f04d86d31a395ff01219-133",
        "bdcdf85c5e29f04d86d31a395ff01219-138",
        "bdcdf85c5e29f04d86d31a395ff01219-143",
        "bdcdf85c5e29f04d86d31a395ff01219-148"
      ],
      "ground_truth_text": "[[2.284, 0.004], [3.502, 0.023], [5.979, 0.079], [9.47, 0.173]]",
      "frame_name": "bdcdf85c5e29f04d86d31a395ff01219-148",
      "anchor_image_path": "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "bdcdf85c5e29f04d86d31a395ff01219",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "bdcdf85c5e29f04d86d31a395ff01219:group_148",
      "image_paths": [
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-128_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-133_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-138_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-143_FRONT.jpg",
        "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-148_FRONT.jpg"
      ],
      "image_path": "bdcdf85c5e29f04d86d31a395ff01219/CAM_FRONT/bdcdf85c5e29f04d86d31a395ff01219-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-129_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-134_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-139_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-144_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [6.325, -0.008]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_beb80cd63303bb6251e25af2c6802d68_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[13.807, 0.002], [22.548, 0.05], [32.501, 0.164], [43.013, 0.298]]",
      "bundle_frame_names": [
        "beb80cd63303bb6251e25af2c6802d68-129",
        "beb80cd63303bb6251e25af2c6802d68-134",
        "beb80cd63303bb6251e25af2c6802d68-139",
        "beb80cd63303bb6251e25af2c6802d68-144",
        "beb80cd63303bb6251e25af2c6802d68-149"
      ],
      "ground_truth_text": "[[13.807, 0.002], [22.548, 0.05], [32.501, 0.164], [43.013, 0.298]]",
      "frame_name": "beb80cd63303bb6251e25af2c6802d68-149",
      "anchor_image_path": "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "beb80cd63303bb6251e25af2c6802d68",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "beb80cd63303bb6251e25af2c6802d68:group_149",
      "image_paths": [
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-129_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-134_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-139_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-144_FRONT.jpg",
        "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-149_FRONT.jpg"
      ],
      "image_path": "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_203",
      "frame_index": 203,
      "history_image_paths": [
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-183_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-188_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-193_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-198_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-203_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [4.851, -0.123]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_bff795a5e9d1f1eefa247769cccb0175_group_203_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[10.471, -0.436], [16.629, -0.896], [23.105, -1.391], [29.766, -1.859]]",
      "bundle_frame_names": [
        "bff795a5e9d1f1eefa247769cccb0175-183",
        "bff795a5e9d1f1eefa247769cccb0175-188",
        "bff795a5e9d1f1eefa247769cccb0175-193",
        "bff795a5e9d1f1eefa247769cccb0175-198",
        "bff795a5e9d1f1eefa247769cccb0175-203"
      ],
      "ground_truth_text": "[[10.471, -0.436], [16.629, -0.896], [23.105, -1.391], [29.766, -1.859]]",
      "frame_name": "bff795a5e9d1f1eefa247769cccb0175-203",
      "anchor_image_path": "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-203_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "bff795a5e9d1f1eefa247769cccb0175",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "bff795a5e9d1f1eefa247769cccb0175:group_203",
      "image_paths": [
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-183_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-188_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-193_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-198_FRONT.jpg",
        "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-203_FRONT.jpg"
      ],
      "image_path": "bff795a5e9d1f1eefa247769cccb0175/CAM_FRONT/bff795a5e9d1f1eefa247769cccb0175-203_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-129_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-134_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-139_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-144_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [7.725, -0.032]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_c1b025ee95af51ea66623e5d17e94fab_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[15.545, -0.12], [23.461, -0.289], [31.159, -0.543], [38.621, -0.822]]",
      "bundle_frame_names": [
        "c1b025ee95af51ea66623e5d17e94fab-129",
        "c1b025ee95af51ea66623e5d17e94fab-134",
        "c1b025ee95af51ea66623e5d17e94fab-139",
        "c1b025ee95af51ea66623e5d17e94fab-144",
        "c1b025ee95af51ea66623e5d17e94fab-149"
      ],
      "ground_truth_text": "[[15.545, -0.12], [23.461, -0.289], [31.159, -0.543], [38.621, -0.822]]",
      "frame_name": "c1b025ee95af51ea66623e5d17e94fab-149",
      "anchor_image_path": "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "c1b025ee95af51ea66623e5d17e94fab",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "c1b025ee95af51ea66623e5d17e94fab:group_149",
      "image_paths": [
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-129_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-134_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-139_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-144_FRONT.jpg",
        "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-149_FRONT.jpg"
      ],
      "image_path": "c1b025ee95af51ea66623e5d17e94fab/CAM_FRONT/c1b025ee95af51ea66623e5d17e94fab-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-127_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-132_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-137_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-142_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [2.901, 0.085]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_c62ca0c1ed7cb3d79aae63deaeb86ca0_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[3.527, 0.129], [3.553, 0.126], [4.737, 0.22], [7.336, 0.383]]",
      "bundle_frame_names": [
        "c62ca0c1ed7cb3d79aae63deaeb86ca0-127",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0-132",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0-137",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0-142",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0-147"
      ],
      "ground_truth_text": "[[3.527, 0.129], [3.553, 0.126], [4.737, 0.22], [7.336, 0.383]]",
      "frame_name": "c62ca0c1ed7cb3d79aae63deaeb86ca0-147",
      "anchor_image_path": "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "c62ca0c1ed7cb3d79aae63deaeb86ca0",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "c62ca0c1ed7cb3d79aae63deaeb86ca0:group_147",
      "image_paths": [
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-127_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-132_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-137_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-142_FRONT.jpg",
        "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-147_FRONT.jpg"
      ],
      "image_path": "c62ca0c1ed7cb3d79aae63deaeb86ca0/CAM_FRONT/c62ca0c1ed7cb3d79aae63deaeb86ca0-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-128_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-133_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-138_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-143_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [3.23, 0.065]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_c81e3bed93363d5449da909ca4c46ca5_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[6.311, 0.254], [8.917, 0.907], [11.091, 2.253], [13.164, 4.564]]",
      "bundle_frame_names": [
        "c81e3bed93363d5449da909ca4c46ca5-128",
        "c81e3bed93363d5449da909ca4c46ca5-133",
        "c81e3bed93363d5449da909ca4c46ca5-138",
        "c81e3bed93363d5449da909ca4c46ca5-143",
        "c81e3bed93363d5449da909ca4c46ca5-148"
      ],
      "ground_truth_text": "[[6.311, 0.254], [8.917, 0.907], [11.091, 2.253], [13.164, 4.564]]",
      "frame_name": "c81e3bed93363d5449da909ca4c46ca5-148",
      "anchor_image_path": "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "c81e3bed93363d5449da909ca4c46ca5",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "c81e3bed93363d5449da909ca4c46ca5:group_148",
      "image_paths": [
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-128_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-133_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-138_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-143_FRONT.jpg",
        "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-148_FRONT.jpg"
      ],
      "image_path": "c81e3bed93363d5449da909ca4c46ca5/CAM_FRONT/c81e3bed93363d5449da909ca4c46ca5-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-127_FRONT.jpg",
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-132_FRONT.jpg",
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-137_FRONT.jpg",
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-142_FRONT.jpg",
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.73, -0.086]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_c90a0552eff17d47f1fb4151e2c6334f_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[3.534, -0.285], [5.459, -0.673], [7.404, -1.257], [9.368, -2.185]]",
      "bundle_frame_names": [
        "c90a0552eff17d47f1fb4151e2c6334f-127",
        "c90a0552eff17d47f1fb4151e2c6334f-132",
        "c90a0552eff17d47f1fb4151e2c6334f-137",
        "c90a0552eff17d47f1fb4151e2c6334f-142",
        "c90a0552eff17d47f1fb4151e2c6334f-147"
      ],
      "ground_truth_text": "[[3.534, -0.285], [5.459, -0.673], [7.404, -1.257], [9.368, -2.185]]",
      "frame_name": "c90a0552eff17d47f1fb4151e2c6334f-147",
      "anchor_image_path": "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "c90a0552eff17d47f1fb4151e2c6334f",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "c90a0552eff17d47f1fb4151e2c6334f:group_147",
      "image_paths": [
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-127_FRONT.jpg",
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-132_FRONT.jpg",
        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-137_FRONT.jpg",
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        "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-147_FRONT.jpg"
      ],
      "image_path": "c90a0552eff17d47f1fb4151e2c6334f/CAM_FRONT/c90a0552eff17d47f1fb4151e2c6334f-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-128_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-133_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-138_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-143_FRONT.jpg",
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.462, 0.014]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d6ea4dab948d2d1ce93216f3285a38f5_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[4.696, 0.09], [9.648, 0.336], [16.22, 0.705], [23.983, 1.15]]",
      "bundle_frame_names": [
        "d6ea4dab948d2d1ce93216f3285a38f5-128",
        "d6ea4dab948d2d1ce93216f3285a38f5-133",
        "d6ea4dab948d2d1ce93216f3285a38f5-138",
        "d6ea4dab948d2d1ce93216f3285a38f5-143",
        "d6ea4dab948d2d1ce93216f3285a38f5-148"
      ],
      "ground_truth_text": "[[4.696, 0.09], [9.648, 0.336], [16.22, 0.705], [23.983, 1.15]]",
      "frame_name": "d6ea4dab948d2d1ce93216f3285a38f5-148",
      "anchor_image_path": "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d6ea4dab948d2d1ce93216f3285a38f5",
      "scenario_cluster": "Intersection",
      "bundle_id": "d6ea4dab948d2d1ce93216f3285a38f5:group_148",
      "image_paths": [
        "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-128_FRONT.jpg",
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      ],
      "image_path": "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-129_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-134_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-139_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-144_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.531, 0.071]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d7029947c6c42936fd5b9c5ee47bcd8a_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[0.533, 0.068], [0.532, 0.068], [0.532, 0.068], [0.628, 0.082]]",
      "bundle_frame_names": [
        "d7029947c6c42936fd5b9c5ee47bcd8a-129",
        "d7029947c6c42936fd5b9c5ee47bcd8a-134",
        "d7029947c6c42936fd5b9c5ee47bcd8a-139",
        "d7029947c6c42936fd5b9c5ee47bcd8a-144",
        "d7029947c6c42936fd5b9c5ee47bcd8a-149"
      ],
      "ground_truth_text": "[[0.533, 0.068], [0.532, 0.068], [0.532, 0.068], [0.628, 0.082]]",
      "frame_name": "d7029947c6c42936fd5b9c5ee47bcd8a-149",
      "anchor_image_path": "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d7029947c6c42936fd5b9c5ee47bcd8a",
      "scenario_cluster": "Intersection",
      "bundle_id": "d7029947c6c42936fd5b9c5ee47bcd8a:group_149",
      "image_paths": [
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-129_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-134_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-139_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-144_FRONT.jpg",
        "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg"
      ],
      "image_path": "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-129_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-134_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-139_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-144_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [6.995, 0.15]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_d810a02521d2fdb70bd0b2e8fd706ab9_group_149_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[13.741, 0.494], [20.368, 1.082], [26.982, 1.799], [33.647, 2.312]]",
      "bundle_frame_names": [
        "d810a02521d2fdb70bd0b2e8fd706ab9-129",
        "d810a02521d2fdb70bd0b2e8fd706ab9-134",
        "d810a02521d2fdb70bd0b2e8fd706ab9-139",
        "d810a02521d2fdb70bd0b2e8fd706ab9-144",
        "d810a02521d2fdb70bd0b2e8fd706ab9-149"
      ],
      "ground_truth_text": "[[13.741, 0.494], [20.368, 1.082], [26.982, 1.799], [33.647, 2.312]]",
      "frame_name": "d810a02521d2fdb70bd0b2e8fd706ab9-149",
      "anchor_image_path": "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d810a02521d2fdb70bd0b2e8fd706ab9",
      "scenario_cluster": "Intersection",
      "bundle_id": "d810a02521d2fdb70bd0b2e8fd706ab9:group_149",
      "image_paths": [
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-129_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-134_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-139_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-144_FRONT.jpg",
        "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg"
      ],
      "image_path": "d810a02521d2fdb70bd0b2e8fd706ab9/CAM_FRONT/d810a02521d2fdb70bd0b2e8fd706ab9-149_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-128_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-133_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-138_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-143_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [0.708, 0.027]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_dabe56f068d94714e5181d3607498d1f_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[1.039, 0.081], [1.033, 0.08], [1.033, 0.08], [1.033, 0.08]]",
      "bundle_frame_names": [
        "dabe56f068d94714e5181d3607498d1f-128",
        "dabe56f068d94714e5181d3607498d1f-133",
        "dabe56f068d94714e5181d3607498d1f-138",
        "dabe56f068d94714e5181d3607498d1f-143",
        "dabe56f068d94714e5181d3607498d1f-148"
      ],
      "ground_truth_text": "[[1.039, 0.081], [1.033, 0.08], [1.033, 0.08], [1.033, 0.08]]",
      "frame_name": "dabe56f068d94714e5181d3607498d1f-148",
      "anchor_image_path": "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "dabe56f068d94714e5181d3607498d1f",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "dabe56f068d94714e5181d3607498d1f:group_148",
      "image_paths": [
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-128_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-133_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-138_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-143_FRONT.jpg",
        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-148_FRONT.jpg"
      ],
      "image_path": "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-127_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-132_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-137_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-142_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [1.68, -0.014]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_e350da1e9a0a9a8a5a0f2665ae0c4bdc_group_147_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[1.667, -0.013], [1.936, -0.018], [2.533, -0.034], [4.375, -0.082]]",
      "bundle_frame_names": [
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-127",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-132",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-137",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-142",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-147"
      ],
      "ground_truth_text": "[[1.667, -0.013], [1.936, -0.018], [2.533, -0.034], [4.375, -0.082]]",
      "frame_name": "e350da1e9a0a9a8a5a0f2665ae0c4bdc-147",
      "anchor_image_path": "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "e350da1e9a0a9a8a5a0f2665ae0c4bdc",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "e350da1e9a0a9a8a5a0f2665ae0c4bdc:group_147",
      "image_paths": [
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-127_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-132_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-137_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-142_FRONT.jpg",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg"
      ],
      "image_path": "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-128_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-133_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-138_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-143_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [5.082, -0.255]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_ea9000098840a70220698bd12b167f97_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[10.466, -0.856], [16.088, -1.858], [21.516, -3.238], [26.591, -4.799]]",
      "bundle_frame_names": [
        "ea9000098840a70220698bd12b167f97-128",
        "ea9000098840a70220698bd12b167f97-133",
        "ea9000098840a70220698bd12b167f97-138",
        "ea9000098840a70220698bd12b167f97-143",
        "ea9000098840a70220698bd12b167f97-148"
      ],
      "ground_truth_text": "[[10.466, -0.856], [16.088, -1.858], [21.516, -3.238], [26.591, -4.799]]",
      "frame_name": "ea9000098840a70220698bd12b167f97-148",
      "anchor_image_path": "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "ea9000098840a70220698bd12b167f97",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "ea9000098840a70220698bd12b167f97:group_148",
      "image_paths": [
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-128_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-133_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-138_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-143_FRONT.jpg",
        "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg"
      ],
      "image_path": "ea9000098840a70220698bd12b167f97/CAM_FRONT/ea9000098840a70220698bd12b167f97-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-128_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-133_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-138_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-143_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [21.928, -0.769]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_f1dd0e328596edd3c4cd977a0d09aef5_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[43.733, -2.966], [65.416, -6.573], [86.949, -11.594], [108.278, -18.012]]",
      "bundle_frame_names": [
        "f1dd0e328596edd3c4cd977a0d09aef5-128",
        "f1dd0e328596edd3c4cd977a0d09aef5-133",
        "f1dd0e328596edd3c4cd977a0d09aef5-138",
        "f1dd0e328596edd3c4cd977a0d09aef5-143",
        "f1dd0e328596edd3c4cd977a0d09aef5-148"
      ],
      "ground_truth_text": "[[43.733, -2.966], [65.416, -6.573], [86.949, -11.594], [108.278, -18.012]]",
      "frame_name": "f1dd0e328596edd3c4cd977a0d09aef5-148",
      "anchor_image_path": "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f1dd0e328596edd3c4cd977a0d09aef5",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "f1dd0e328596edd3c4cd977a0d09aef5:group_148",
      "image_paths": [
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-128_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-133_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-138_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-143_FRONT.jpg",
        "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg"
      ],
      "image_path": "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg",
      "scoring_type": "trajectory_waypoints"
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-128_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-133_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-138_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-143_FRONT.jpg",
        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-148_FRONT.jpg"
      ],
      "question": "Based on the past 5 observed front-view frames sampled over approximately 2.0 seconds, the ego vehicle position at the next future frame is already known in the current ego-centric coordinate system anchored at the last observed frame as [x1, y1] = [6.093, -0.47]. The current frame is [0.0, 0.0]. Given that first future point, predict the trajectory for the following 4 future frames sampled over the same full future horizon. Do not repeat the known first future position. Output only a JSON array of exactly 4 points formatted as [[x2, y2], [x3, y3], [x4, y4], [x5, y5]].",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "question_id": "waymo_val_fd7cce36d47f456b4811d3ca4807517e_group_148_TRJ-6",
      "id": "TRJ-6",
      "ground_truth": "[[13.109, -1.035], [20.62, -1.571], [28.376, -2.048], [36.642, -2.595]]",
      "bundle_frame_names": [
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      "choices": {
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        "C": "Candidate trajectory C"
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            496.23,
            916.18
          ],
          [
            501.81,
            823.59
          ],
          [
            505.46,
            791.81
          ],
          [
            504.64,
            769.03
          ]
        ]
      }
    },
    {
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          [
            6.435,
            -1.006
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          [
            9.43,
            -2.545
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          [
            11.124,
            -4.144
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          [
            12.397,
            -6.607
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            2.19,
            -0.095
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          [
            5.681,
            -0.637
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          [
            7.984,
            -1.289
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          [
            10.236,
            -2.151
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          [
            14.111,
            -4.154
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        ],
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            2.404,
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          [
            9.436,
            -2.017
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          [
            18.735,
            -7.948
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          [
            27.169,
            -15.828
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          [
            38.972,
            -27.543
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        ]
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        "fa3deaf57d0df0e3057722177a5e27fc-143",
        "fa3deaf57d0df0e3057722177a5e27fc-148"
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      "scoring_type": "mcq",
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          [
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          [
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            666.93,
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          [
            660.77,
            1200.6
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          [
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            1029.27
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          [
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          [
            854.77,
            879.18
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          [
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          [
            1000.79,
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          [
            1173.07,
            800.1
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          [
            1304.24,
            776.3
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        ]
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    {
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      ],
      "question": "Given the current scene and three candidate ego future trajectories, which trajectory is the best driving choice?",
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          [
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          [
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          [
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          [
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          [
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          [
            28.693,
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          [
            35.006,
            -1.178
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          [
            36.011,
            -0.388
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          [
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        "fb0ed944efebd34d756103188d59da85-137",
        "fb0ed944efebd34d756103188d59da85-142",
        "fb0ed944efebd34d756103188d59da85-147"
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          [
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          [
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      "question_id": "waymo_val_fd52a15b681dc3920df93665305231f5_group_148_TRJ-8",
      "id": "TRJ-8",
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            0.089,
            0.0
          ],
          [
            0.71,
            0.0
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          [
            3.43,
            -0.045
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          [
            7.934,
            -0.221
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          [
            16.512,
            -0.532
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        ],
        "B": [
          [
            0.019,
            -0.0
          ],
          [
            0.958,
            0.0
          ],
          [
            5.28,
            -0.082
          ],
          [
            11.614,
            -0.462
          ],
          [
            22.286,
            -1.467
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        ],
        "C": [
          [
            0.031,
            -0.0
          ],
          [
            0.279,
            -0.0
          ],
          [
            0.593,
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          ],
          [
            0.796,
            -0.0
          ],
          [
            0.98,
            0.0
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        ]
      },
      "question_id": "waymo_val_0824d90db4395e53f693374259e5b1d8_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the most appropriate trajectory for a cyclist scenario as it shows a smooth, continuous forward motion with increasing speed and a gradual lateral drift, consistent with natural cycling behavior on a straight path. In contrast, Candidate A is less consistent in progression and Candidate C remains nearly stationary, both of which are unrealistic for a moving cyclist.",
      "bundle_frame_names": [
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        "0824d90db4395e53f693374259e5b1d8-134",
        "0824d90db4395e53f693374259e5b1d8-139",
        "0824d90db4395e53f693374259e5b1d8-144",
        "0824d90db4395e53f693374259e5b1d8-149"
      ],
      "ground_truth_text": "Candidate B is the most appropriate trajectory for a cyclist scenario as it shows a smooth, continuous forward motion with increasing speed and a gradual lateral drift, consistent with natural cycling behavior on a straight path. In contrast, Candidate A is less consistent in progression and Candidate C remains nearly stationary, both of which are unrealistic for a moving cyclist.",
      "frame_name": "0824d90db4395e53f693374259e5b1d8-149",
      "anchor_image_path": "0824d90db4395e53f693374259e5b1d8/CAM_FRONT/0824d90db4395e53f693374259e5b1d8-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0824d90db4395e53f693374259e5b1d8",
      "scenario_cluster": "Cyclist",
      "bundle_id": "0824d90db4395e53f693374259e5b1d8:group_149",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
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          [
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          [
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        "B": [
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            523.29,
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          [
            544.14,
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          [
            570.05,
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    {
      "group_id": "group_148",
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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            -0.004
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          [
            13.108,
            0.078
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          [
            27.183,
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          [
            32.276,
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          [
            3.807,
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          [
            12.622,
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          [
            19.59,
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          [
            23.702,
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          [
            26.874,
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      "question_id": "waymo_val_0c1bdcfac7be564a0c75d3bb4234e47d_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it maintains a straight and stable path with minimal lateral deviation, avoiding unnecessary swerving that could risk collision with the foreign object debris. Compared to B, which veers significantly to the right, and C, which cuts off the path too early, A provides a safe, continuous trajectory that preserves forward momentum while staying clear of the hazard.",
      "bundle_frame_names": [
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        "0c1bdcfac7be564a0c75d3bb4234e47d-133",
        "0c1bdcfac7be564a0c75d3bb4234e47d-138",
        "0c1bdcfac7be564a0c75d3bb4234e47d-143",
        "0c1bdcfac7be564a0c75d3bb4234e47d-148"
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      "ground_truth_text": "Candidate A is the best choice because it maintains a straight and stable path with minimal lateral deviation, avoiding unnecessary swerving that could risk collision with the foreign object debris. Compared to B, which veers significantly to the right, and C, which cuts off the path too early, A provides a safe, continuous trajectory that preserves forward momentum while staying clear of the hazard.",
      "frame_name": "0c1bdcfac7be564a0c75d3bb4234e47d-148",
      "anchor_image_path": "0c1bdcfac7be564a0c75d3bb4234e47d/CAM_FRONT/0c1bdcfac7be564a0c75d3bb4234e47d-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "0c1bdcfac7be564a0c75d3bb4234e47d",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "0c1bdcfac7be564a0c75d3bb4234e47d:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
            530.03,
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
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          [
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            23.595,
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      "question_id": "waymo_val_0d94d2a36405c21b1fc19601c41e8d23_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it maintains a consistent and safe lateral offset while continuing forward at a steady pace, allowing the vehicle to smoothly navigate past the cut-in scenario without abrupt deceleration or lateral instability. In contrast, Candidate B slows significantly and ends earlier, potentially causing a collision or unsafe gap, while Candidate C abruptly stops and remains too close to the cut-in vehicle, failing to maintain safe following distance.",
      "bundle_frame_names": [
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        "0d94d2a36405c21b1fc19601c41e8d23-149"
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      "ground_truth_text": "Candidate A is the best choice because it maintains a consistent and safe lateral offset while continuing forward at a steady pace, allowing the vehicle to smoothly navigate past the cut-in scenario without abrupt deceleration or lateral instability. In contrast, Candidate B slows significantly and ends earlier, potentially causing a collision or unsafe gap, while Candidate C abruptly stops and remains too close to the cut-in vehicle, failing to maintain safe following distance.",
      "frame_name": "0d94d2a36405c21b1fc19601c41e8d23-149",
      "anchor_image_path": "0d94d2a36405c21b1fc19601c41e8d23/CAM_FRONT/0d94d2a36405c21b1fc19601c41e8d23-149_FRONT.jpg",
      "task": "trajectory-prediction",
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      "scenario_cluster": "Cut-ins",
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      "ground_truth": "In a construction scenario, Candidate A is the best choice because it maintains a consistent and safe trajectory along the intended path, with a longer, smoother progression that avoids abrupt changes in speed or direction. Compared to Candidate B, which ends prematurely and shows erratic step sizes, and Candidate C, which deviates further laterally and extends beyond the necessary path, Candidate A provides a more stable and predictable route through the construction zone.",
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      "anchor_image_path": "14f0b68cf50bad87e90a490a70109cad/CAM_FRONT/14f0b68cf50bad87e90a490a70109cad-149_FRONT.jpg",
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        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            4.557,
            0.038
          ],
          [
            16.306,
            0.266
          ],
          [
            28.423,
            0.211
          ],
          [
            38.045,
            -0.098
          ],
          [
            49.767,
            -0.646
          ]
        ],
        "B": [
          [
            4.521,
            0.049
          ],
          [
            15.72,
            0.41
          ],
          [
            25.807,
            0.647
          ],
          [
            32.385,
            0.612
          ],
          [
            38.746,
            0.434
          ]
        ],
        "C": [
          [
            4.568,
            0.041
          ],
          [
            16.546,
            0.304
          ],
          [
            29.329,
            0.252
          ],
          [
            39.797,
            -0.174
          ],
          [
            52.824,
            -1.029
          ]
        ]
      },
      "question_id": "waymo_val_1a13b64b69b568c4ccbb572bc3f0257b_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it maintains a consistent and natural trajectory with smooth acceleration and a longer path that safely navigates the scene, avoiding abrupt changes in speed or lateral movement. Compared to A and B, it extends further along the road with a more stable step progression and a slightly wider but controlled lateral deviation, indicating a more confident and continuous motion consistent with typical cyclist behavior.",
      "bundle_frame_names": [
        "1a13b64b69b568c4ccbb572bc3f0257b-127",
        "1a13b64b69b568c4ccbb572bc3f0257b-132",
        "1a13b64b69b568c4ccbb572bc3f0257b-137",
        "1a13b64b69b568c4ccbb572bc3f0257b-142",
        "1a13b64b69b568c4ccbb572bc3f0257b-147"
      ],
      "ground_truth_text": "Candidate C is the best choice because it maintains a consistent and natural trajectory with smooth acceleration and a longer path that safely navigates the scene, avoiding abrupt changes in speed or lateral movement. Compared to A and B, it extends further along the road with a more stable step progression and a slightly wider but controlled lateral deviation, indicating a more confident and continuous motion consistent with typical cyclist behavior.",
      "frame_name": "1a13b64b69b568c4ccbb572bc3f0257b-147",
      "anchor_image_path": "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "1a13b64b69b568c4ccbb572bc3f0257b",
      "scenario_cluster": "Cyclist",
      "bundle_id": "1a13b64b69b568c4ccbb572bc3f0257b:group_147",
      "image_paths": [
        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-127_FRONT.jpg",
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        "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg"
      ],
      "image_path": "1a13b64b69b568c4ccbb572bc3f0257b/CAM_FRONT/1a13b64b69b568c4ccbb572bc3f0257b-147_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            490.89,
            1376.54
          ],
          [
            475.93,
            850.69
          ],
          [
            486.22,
            789.29
          ],
          [
            497.64,
            769.53
          ],
          [
            509.37,
            756.09
          ]
        ],
        "B": [
          [
            486.77,
            1384.46
          ],
          [
            463.88,
            856.34
          ],
          [
            465.38,
            797.41
          ],
          [
            472.68,
            779.71
          ],
          [
            481.61,
            768.53
          ]
        ],
        "C": [
          [
            489.8,
            1374.16
          ],
          [
            473.39,
            848.52
          ],
          [
            484.82,
            786.86
          ],
          [
            499.68,
            767.0
          ],
          [
            516.77,
            753.59
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-128_FRONT.jpg",
        "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-133_FRONT.jpg",
        "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-138_FRONT.jpg",
        "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-143_FRONT.jpg",
        "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            0.491,
            0.0
          ],
          [
            3.673,
            -0.014
          ],
          [
            8.58,
            -0.012
          ],
          [
            12.261,
            0.219
          ],
          [
            15.598,
            0.868
          ]
        ],
        "B": [
          [
            0.409,
            0.001
          ],
          [
            2.407,
            0.005
          ],
          [
            4.765,
            0.031
          ],
          [
            6.383,
            0.069
          ],
          [
            7.998,
            0.142
          ]
        ],
        "C": [
          [
            0.367,
            0.0
          ],
          [
            1.916,
            -0.001
          ],
          [
            3.464,
            -0.003
          ],
          [
            4.427,
            -0.003
          ],
          [
            5.416,
            0.001
          ]
        ]
      },
      "question_id": "waymo_val_1bbb0008698212719802e03983c052d7_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "At an intersection, the safest and most appropriate path is one that proceeds straight through with minimal lateral deviation and maintains a consistent, controlled speed. Candidate C follows a direct, low-lateral trajectory with smooth, steady steps, making it the most reasonable choice compared to the longer, more erratic paths of A and B.",
      "bundle_frame_names": [
        "1bbb0008698212719802e03983c052d7-128",
        "1bbb0008698212719802e03983c052d7-133",
        "1bbb0008698212719802e03983c052d7-138",
        "1bbb0008698212719802e03983c052d7-143",
        "1bbb0008698212719802e03983c052d7-148"
      ],
      "ground_truth_text": "At an intersection, the safest and most appropriate path is one that proceeds straight through with minimal lateral deviation and maintains a consistent, controlled speed. Candidate C follows a direct, low-lateral trajectory with smooth, steady steps, making it the most reasonable choice compared to the longer, more erratic paths of A and B.",
      "frame_name": "1bbb0008698212719802e03983c052d7-148",
      "anchor_image_path": "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "1bbb0008698212719802e03983c052d7",
      "scenario_cluster": "Intersection",
      "bundle_id": "1bbb0008698212719802e03983c052d7:group_148",
      "image_paths": [
        "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-128_FRONT.jpg",
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      ],
      "image_path": "1bbb0008698212719802e03983c052d7/CAM_FRONT/1bbb0008698212719802e03983c052d7-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            513.12,
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          ],
          [
            500.46,
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          [
            474.78,
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          ],
          [
            428.38,
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        ],
        "B": [
          [
            514.63,
            2975.69
          ],
          [
            491.72,
            1336.46
          ],
          [
            484.24,
            1130.65
          ],
          [
            474.47,
            1027.67
          ]
        ],
        "C": [
          [
            555.06,
            5752.62
          ],
          [
            508.74,
            1750.05
          ],
          [
            504.31,
            1408.42
          ],
          [
            500.89,
            1233.19
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        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-128_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-133_FRONT.jpg",
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        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            0.182,
            0.0
          ],
          [
            3.01,
            -0.077
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          [
            8.588,
            -1.683
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          [
            12.207,
            -6.133
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          [
            13.618,
            -15.926
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        ],
        "B": [
          [
            0.053,
            -0.0
          ],
          [
            1.222,
            0.002
          ],
          [
            4.579,
            -0.075
          ],
          [
            7.639,
            -0.724
          ],
          [
            11.146,
            -3.26
          ]
        ],
        "C": [
          [
            0.056,
            -0.0
          ],
          [
            0.338,
            0.001
          ],
          [
            0.852,
            0.001
          ],
          [
            1.376,
            -0.003
          ],
          [
            2.357,
            -0.012
          ]
        ]
      },
      "question_id": "waymo_val_262e00554925cca1cf924ed5ea430d66_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "At an intersection, the safest and most appropriate action is to proceed with minimal movement, maintaining a stable and controlled path. Candidate C reflects a short, steady trajectory with negligible lateral deviation, indicating a cautious stop or slow yield, which is appropriate for an intersection scenario. In contrast, Candidates A and B show significantly longer and more aggressive paths, suggesting unnecessary acceleration or turning, which would be unsafe and inappropriate for typical intersection behavior.",
      "bundle_frame_names": [
        "262e00554925cca1cf924ed5ea430d66-128",
        "262e00554925cca1cf924ed5ea430d66-133",
        "262e00554925cca1cf924ed5ea430d66-138",
        "262e00554925cca1cf924ed5ea430d66-143",
        "262e00554925cca1cf924ed5ea430d66-148"
      ],
      "ground_truth_text": "At an intersection, the safest and most appropriate action is to proceed with minimal movement, maintaining a stable and controlled path. Candidate C reflects a short, steady trajectory with negligible lateral deviation, indicating a cautious stop or slow yield, which is appropriate for an intersection scenario. In contrast, Candidates A and B show significantly longer and more aggressive paths, suggesting unnecessary acceleration or turning, which would be unsafe and inappropriate for typical intersection behavior.",
      "frame_name": "262e00554925cca1cf924ed5ea430d66-148",
      "anchor_image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "262e00554925cca1cf924ed5ea430d66",
      "scenario_cluster": "Intersection",
      "bundle_id": "262e00554925cca1cf924ed5ea430d66:group_148",
      "image_paths": [
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-128_FRONT.jpg",
        "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-133_FRONT.jpg",
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      ],
      "image_path": "262e00554925cca1cf924ed5ea430d66/CAM_FRONT/262e00554925cca1cf924ed5ea430d66-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            559.33,
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          ],
          [
            757.0,
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          [
            1130.59,
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          ],
          [
            1959.14,
            887.51
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        ],
        "B": [
          [
            522.59,
            1371.21
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          [
            623.92,
            1043.72
          ],
          [
            868.45,
            925.17
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        ],
        "C": [
          [
            527.97,
            3096.66
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        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-132_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-137_FRONT.jpg",
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        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            10.01,
            0.001
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          [
            32.009,
            0.025
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          [
            52.83,
            -0.184
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          [
            69.495,
            -0.483
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          [
            90.154,
            -0.86
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        ],
        "B": [
          [
            9.976,
            0.03
          ],
          [
            31.729,
            0.592
          ],
          [
            52.029,
            1.155
          ],
          [
            68.331,
            1.069
          ],
          [
            88.804,
            0.367
          ]
        ],
        "C": [
          [
            9.555,
            -0.005
          ],
          [
            27.22,
            0.215
          ],
          [
            37.096,
            0.501
          ],
          [
            41.735,
            0.633
          ],
          [
            46.717,
            0.66
          ]
        ]
      },
      "question_id": "waymo_val_31935e26a0bd05d0f9bf5285154cb25e_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the most plausible trajectory for a cyclist, as it maintains a consistent forward progression with smooth lateral movement and realistic step lengths, avoiding abrupt changes in speed or direction. Compared to A, which veers too far left and shows erratic lateral deviation, and C, which ends prematurely and decelerates sharply, B follows a natural path that aligns with typical cyclist behavior in the scene.",
      "bundle_frame_names": [
        "31935e26a0bd05d0f9bf5285154cb25e-127",
        "31935e26a0bd05d0f9bf5285154cb25e-132",
        "31935e26a0bd05d0f9bf5285154cb25e-137",
        "31935e26a0bd05d0f9bf5285154cb25e-142",
        "31935e26a0bd05d0f9bf5285154cb25e-147"
      ],
      "ground_truth_text": "Candidate B is the most plausible trajectory for a cyclist, as it maintains a consistent forward progression with smooth lateral movement and realistic step lengths, avoiding abrupt changes in speed or direction. Compared to A, which veers too far left and shows erratic lateral deviation, and C, which ends prematurely and decelerates sharply, B follows a natural path that aligns with typical cyclist behavior in the scene.",
      "frame_name": "31935e26a0bd05d0f9bf5285154cb25e-147",
      "anchor_image_path": "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "31935e26a0bd05d0f9bf5285154cb25e",
      "scenario_cluster": "Cyclist",
      "bundle_id": "31935e26a0bd05d0f9bf5285154cb25e:group_147",
      "image_paths": [
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-127_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-132_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-137_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-142_FRONT.jpg",
        "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg"
      ],
      "image_path": "31935e26a0bd05d0f9bf5285154cb25e/CAM_FRONT/31935e26a0bd05d0f9bf5285154cb25e-147_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            492.58,
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          ],
          [
            491.1,
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          ],
          [
            495.88,
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          ],
          [
            499.75,
            744.28
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          [
            502.61,
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          ]
        ],
        "B": [
          [
            488.78,
            951.64
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          [
            470.26,
            780.85
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          [
            466.52,
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          ],
          [
            474.1,
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          ],
          [
            487.17,
            737.63
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        ],
        "C": [
          [
            493.46,
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          [
            482.78,
            792.62
          ],
          [
            476.34,
            770.9
          ],
          [
            474.48,
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          ],
          [
            475.71,
            758.9
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-128_FRONT.jpg",
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-133_FRONT.jpg",
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        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            2.407,
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          [
            8.423,
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          [
            14.415,
            -0.01
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          [
            18.701,
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          [
            23.084,
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        ],
        "B": [
          [
            2.419,
            0.008
          ],
          [
            8.494,
            0.116
          ],
          [
            15.119,
            0.301
          ],
          [
            20.614,
            0.367
          ],
          [
            27.407,
            0.344
          ]
        ],
        "C": [
          [
            2.007,
            0.004
          ],
          [
            8.161,
            0.022
          ],
          [
            14.586,
            0.065
          ],
          [
            19.614,
            0.068
          ],
          [
            25.436,
            0.08
          ]
        ]
      },
      "question_id": "waymo_val_349479e335692cf038b24348f274d575_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice at the intersection because it maintains a straight, stable path with minimal lateral deviation, indicating a safe and efficient through movement. In contrast, Candidate A veers significantly to the left, suggesting a potential lane drift or unsafe maneuver, while Candidate B shows a wider, less direct trajectory that could imply unnecessary lateral movement or a less optimal path through the intersection.",
      "bundle_frame_names": [
        "349479e335692cf038b24348f274d575-128",
        "349479e335692cf038b24348f274d575-133",
        "349479e335692cf038b24348f274d575-138",
        "349479e335692cf038b24348f274d575-143",
        "349479e335692cf038b24348f274d575-148"
      ],
      "ground_truth_text": "Candidate C is the best choice at the intersection because it maintains a straight, stable path with minimal lateral deviation, indicating a safe and efficient through movement. In contrast, Candidate A veers significantly to the left, suggesting a potential lane drift or unsafe maneuver, while Candidate B shows a wider, less direct trajectory that could imply unnecessary lateral movement or a less optimal path through the intersection.",
      "frame_name": "349479e335692cf038b24348f274d575-148",
      "anchor_image_path": "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "349479e335692cf038b24348f274d575",
      "scenario_cluster": "Intersection",
      "bundle_id": "349479e335692cf038b24348f274d575:group_148",
      "image_paths": [
        "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-128_FRONT.jpg",
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      ],
      "image_path": "349479e335692cf038b24348f274d575/CAM_FRONT/349479e335692cf038b24348f274d575-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            502.4,
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          [
            488.32,
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          [
            490.28,
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          [
            505.04,
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          [
            539.72,
            811.57
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        ],
        "B": [
          [
            497.26,
            2949.81
          ],
          [
            472.02,
            1006.35
          ],
          [
            464.69,
            865.79
          ],
          [
            467.57,
            823.23
          ],
          [
            473.94,
            795.61
          ]
        ],
        "C": [
          [
            514.08,
            4826.97
          ],
          [
            486.98,
            1020.89
          ],
          [
            483.86,
            871.94
          ],
          [
            484.85,
            829.15
          ],
          [
            485.08,
            802.08
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "3721a68c3a5f6b637f192a2bc4c05824/CAM_FRONT/3721a68c3a5f6b637f192a2bc4c05824-128_FRONT.jpg",
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        "3721a68c3a5f6b637f192a2bc4c05824/CAM_FRONT/3721a68c3a5f6b637f192a2bc4c05824-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            -0.0,
            0.0
          ],
          [
            -0.0,
            0.0
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          [
            -0.0,
            0.0
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          [
            -0.0,
            0.0
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          [
            -0.0,
            0.0
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        ],
        "B": [
          [
            0.022,
            -0.002
          ],
          [
            0.911,
            -0.001
          ],
          [
            3.846,
            0.006
          ],
          [
            7.059,
            -0.197
          ],
          [
            11.372,
            -2.081
          ]
        ],
        "C": [
          [
            0.054,
            -0.001
          ],
          [
            1.632,
            -0.0
          ],
          [
            6.026,
            -0.196
          ],
          [
            10.195,
            -1.531
          ],
          [
            14.51,
            -6.87
          ]
        ]
      },
      "question_id": "waymo_val_3721a68c3a5f6b637f192a2bc4c05824_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "At an intersection, Candidate B provides a smooth and controlled trajectory that progresses forward while maintaining a reasonable lateral offset, avoiding abrupt movements or excessive lateral deviation. Compared to Candidate A, which shows no motion, and Candidate C, which exhibits a sharper turn and greater lateral drift, B offers a more natural and safe path through the intersection.",
      "bundle_frame_names": [
        "3721a68c3a5f6b637f192a2bc4c05824-128",
        "3721a68c3a5f6b637f192a2bc4c05824-133",
        "3721a68c3a5f6b637f192a2bc4c05824-138",
        "3721a68c3a5f6b637f192a2bc4c05824-143",
        "3721a68c3a5f6b637f192a2bc4c05824-148"
      ],
      "ground_truth_text": "At an intersection, Candidate B provides a smooth and controlled trajectory that progresses forward while maintaining a reasonable lateral offset, avoiding abrupt movements or excessive lateral deviation. Compared to Candidate A, which shows no motion, and Candidate C, which exhibits a sharper turn and greater lateral drift, B offers a more natural and safe path through the intersection.",
      "frame_name": "3721a68c3a5f6b637f192a2bc4c05824-148",
      "anchor_image_path": "3721a68c3a5f6b637f192a2bc4c05824/CAM_FRONT/3721a68c3a5f6b637f192a2bc4c05824-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "3721a68c3a5f6b637f192a2bc4c05824",
      "scenario_cluster": "Intersection",
      "bundle_id": "3721a68c3a5f6b637f192a2bc4c05824:group_148",
      "image_paths": [
        "3721a68c3a5f6b637f192a2bc4c05824/CAM_FRONT/3721a68c3a5f6b637f192a2bc4c05824-128_FRONT.jpg",
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        "3721a68c3a5f6b637f192a2bc4c05824/CAM_FRONT/3721a68c3a5f6b637f192a2bc4c05824-148_FRONT.jpg"
      ],
      "image_path": "3721a68c3a5f6b637f192a2bc4c05824/CAM_FRONT/3721a68c3a5f6b637f192a2bc4c05824-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [],
        "B": [
          [
            497.92,
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          ],
          [
            535.96,
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          ],
          [
            730.51,
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        ],
        "C": [
          [
            545.53,
            1164.18
          ],
          [
            691.96,
            950.59
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          [
            1085.24,
            875.84
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        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            4.305,
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          [
            13.193,
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          [
            17.618,
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          [
            18.953,
            1.014
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          [
            19.881,
            1.068
          ]
        ],
        "B": [
          [
            4.321,
            0.133
          ],
          [
            14.342,
            1.124
          ],
          [
            23.593,
            2.301
          ],
          [
            31.531,
            2.999
          ],
          [
            42.634,
            3.508
          ]
        ],
        "C": [
          [
            4.392,
            0.108
          ],
          [
            16.1,
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          ],
          [
            28.937,
            1.783
          ],
          [
            39.68,
            2.364
          ],
          [
            53.43,
            3.149
          ]
        ]
      },
      "question_id": "waymo_val_3863d816ff1b1aa7a40de54c4ba821eb_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the most appropriate trajectory as it maintains a smooth, consistent forward progression with a gradual increase in lateral offset, indicating a natural and safe lane-keeping maneuver. Compared to A, which ends abruptly and shows minimal lateral movement, and C, which extends too far with a higher lateral deviation, B offers a balanced and realistic path that aligns with typical cyclist behavior in a straight roadway context.",
      "bundle_frame_names": [
        "3863d816ff1b1aa7a40de54c4ba821eb-128",
        "3863d816ff1b1aa7a40de54c4ba821eb-133",
        "3863d816ff1b1aa7a40de54c4ba821eb-138",
        "3863d816ff1b1aa7a40de54c4ba821eb-143",
        "3863d816ff1b1aa7a40de54c4ba821eb-148"
      ],
      "ground_truth_text": "Candidate B is the most appropriate trajectory as it maintains a smooth, consistent forward progression with a gradual increase in lateral offset, indicating a natural and safe lane-keeping maneuver. Compared to A, which ends abruptly and shows minimal lateral movement, and C, which extends too far with a higher lateral deviation, B offers a balanced and realistic path that aligns with typical cyclist behavior in a straight roadway context.",
      "frame_name": "3863d816ff1b1aa7a40de54c4ba821eb-148",
      "anchor_image_path": "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "3863d816ff1b1aa7a40de54c4ba821eb",
      "scenario_cluster": "Cyclist",
      "bundle_id": "3863d816ff1b1aa7a40de54c4ba821eb:group_148",
      "image_paths": [
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      "image_path": "3863d816ff1b1aa7a40de54c4ba821eb/CAM_FRONT/3863d816ff1b1aa7a40de54c4ba821eb-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            452.06,
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          [
            430.21,
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          [
            426.59,
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          [
            426.21,
            833.92
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          [
            426.15,
            828.1
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        ],
        "B": [
          [
            443.15,
            1435.84
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          [
            393.75,
            875.17
          ],
          [
            374.76,
            809.39
          ],
          [
            379.35,
            785.34
          ],
          [
            395.44,
            767.36
          ]
        ],
        "C": [
          [
            453.98,
            1418.2
          ],
          [
            418.92,
            856.43
          ],
          [
            418.21,
            791.91
          ],
          [
            421.47,
            771.3
          ],
          [
            422.78,
            757.36
          ]
        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-149_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            8.852,
            -0.002
          ],
          [
            29.149,
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          ],
          [
            49.42,
            0.339
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          [
            74.902,
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          [
            95.395,
            0.906
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        ],
        "B": [
          [
            10.335,
            -0.012
          ],
          [
            35.43,
            0.0
          ],
          [
            59.971,
            0.123
          ],
          [
            79.393,
            0.314
          ],
          [
            103.769,
            0.64
          ]
        ],
        "C": [
          [
            10.358,
            -0.008
          ],
          [
            36.091,
            0.133
          ],
          [
            62.274,
            0.516
          ],
          [
            83.443,
            0.957
          ],
          [
            110.249,
            1.752
          ]
        ]
      },
      "question_id": "waymo_val_398703cb3d52176faf9da2a53aac4b69_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice for a cut-in scenario because it maintains a smooth, consistent lateral path with minimal lateral deviation (0.906 m) and a stable, predictable progression that safely accommodates the merging vehicle without excessive speed or abrupt maneuvers. Compared to B and C, it avoids oversteering or excessive lateral displacement, ensuring a controlled and efficient integration into the lane while preserving safe spacing and trajectory coherence.",
      "bundle_frame_names": [
        "398703cb3d52176faf9da2a53aac4b69-129",
        "398703cb3d52176faf9da2a53aac4b69-134",
        "398703cb3d52176faf9da2a53aac4b69-139",
        "398703cb3d52176faf9da2a53aac4b69-144",
        "398703cb3d52176faf9da2a53aac4b69-149"
      ],
      "ground_truth_text": "Candidate A is the best choice for a cut-in scenario because it maintains a smooth, consistent lateral path with minimal lateral deviation (0.906 m) and a stable, predictable progression that safely accommodates the merging vehicle without excessive speed or abrupt maneuvers. Compared to B and C, it avoids oversteering or excessive lateral displacement, ensuring a controlled and efficient integration into the lane while preserving safe spacing and trajectory coherence.",
      "frame_name": "398703cb3d52176faf9da2a53aac4b69-149",
      "anchor_image_path": "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "398703cb3d52176faf9da2a53aac4b69",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "398703cb3d52176faf9da2a53aac4b69:group_149",
      "image_paths": [
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      "image_path": "398703cb3d52176faf9da2a53aac4b69/CAM_FRONT/398703cb3d52176faf9da2a53aac4b69-149_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            496.5,
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          [
            488.21,
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          [
            483.95,
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          [
            481.53,
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          [
            480.7,
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        ],
        "B": [
          [
            496.84,
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          [
            492.14,
            775.72
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          [
            489.33,
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          [
            487.03,
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          [
            484.44,
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        ],
        "C": [
          [
            496.32,
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          [
            487.85,
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          ],
          [
            482.21,
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          ],
          [
            478.5,
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          ],
          [
            473.46,
            735.07
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        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            0.029,
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          [
            1.451,
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          [
            5.158,
            0.031
          ],
          [
            8.136,
            -0.176
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          [
            10.929,
            -1.341
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        ],
        "B": [
          [
            0.057,
            0.001
          ],
          [
            2.184,
            0.02
          ],
          [
            7.48,
            -0.442
          ],
          [
            11.64,
            -2.811
          ],
          [
            14.56,
            -9.947
          ]
        ],
        "C": [
          [
            0.018,
            -0.002
          ],
          [
            1.131,
            0.004
          ],
          [
            4.231,
            0.048
          ],
          [
            6.749,
            0.029
          ],
          [
            8.98,
            -0.347
          ]
        ]
      },
      "question_id": "waymo_val_517edb9d2be14368685cc9f6184e9afe_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the most reasonable trajectory for a cyclist, as it maintains a smooth, gradual path with minimal lateral deviation and consistent forward progression, avoiding abrupt changes in speed or direction. Compared to B, which shows a sharp, erratic turn and excessive lateral movement, and C, which is shorter and less stable, A better reflects a natural and safe cycling motion.",
      "bundle_frame_names": [
        "517edb9d2be14368685cc9f6184e9afe-128",
        "517edb9d2be14368685cc9f6184e9afe-133",
        "517edb9d2be14368685cc9f6184e9afe-138",
        "517edb9d2be14368685cc9f6184e9afe-143",
        "517edb9d2be14368685cc9f6184e9afe-148"
      ],
      "ground_truth_text": "Candidate A is the most reasonable trajectory for a cyclist, as it maintains a smooth, gradual path with minimal lateral deviation and consistent forward progression, avoiding abrupt changes in speed or direction. Compared to B, which shows a sharp, erratic turn and excessive lateral movement, and C, which is shorter and less stable, A better reflects a natural and safe cycling motion.",
      "frame_name": "517edb9d2be14368685cc9f6184e9afe-148",
      "anchor_image_path": "517edb9d2be14368685cc9f6184e9afe/CAM_FRONT/517edb9d2be14368685cc9f6184e9afe-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "517edb9d2be14368685cc9f6184e9afe",
      "scenario_cluster": "Cyclist",
      "bundle_id": "517edb9d2be14368685cc9f6184e9afe:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            484.91,
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          [
            521.52,
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          [
            649.67,
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        ],
        "B": [
          [
            485.48,
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          [
            574.77,
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          [
            800.09,
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          [
            1339.99,
            874.7
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        ],
        "C": [
          [
            476.58,
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          [
            486.55,
            1101.03
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          [
            543.34,
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      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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        "B": [
          [
            0.405,
            -0.001
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          [
            4.618,
            0.04
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          [
            14.381,
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          [
            25.713,
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          [
            42.875,
            2.462
          ]
        ],
        "C": [
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            -0.001
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          [
            0.457,
            -0.002
          ],
          [
            2.618,
            -0.01
          ],
          [
            8.101,
            0.131
          ],
          [
            20.285,
            0.569
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        ]
      },
      "question_id": "waymo_val_57e34daab2df82dfd78edad9dfc8d56d_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "At an intersection, Candidate B is the most appropriate trajectory as it continues straight through the intersection with a long, consistent path that maintains forward momentum and proper lane positioning, unlike Candidate A which stops abruptly and Candidate C which turns early, both of which are inconsistent with a through movement.",
      "bundle_frame_names": [
        "57e34daab2df82dfd78edad9dfc8d56d-127",
        "57e34daab2df82dfd78edad9dfc8d56d-132",
        "57e34daab2df82dfd78edad9dfc8d56d-137",
        "57e34daab2df82dfd78edad9dfc8d56d-142",
        "57e34daab2df82dfd78edad9dfc8d56d-147"
      ],
      "ground_truth_text": "At an intersection, Candidate B is the most appropriate trajectory as it continues straight through the intersection with a long, consistent path that maintains forward momentum and proper lane positioning, unlike Candidate A which stops abruptly and Candidate C which turns early, both of which are inconsistent with a through movement.",
      "frame_name": "57e34daab2df82dfd78edad9dfc8d56d-147",
      "anchor_image_path": "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "57e34daab2df82dfd78edad9dfc8d56d",
      "scenario_cluster": "Intersection",
      "bundle_id": "57e34daab2df82dfd78edad9dfc8d56d:group_147",
      "image_paths": [
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      "image_path": "57e34daab2df82dfd78edad9dfc8d56d/CAM_FRONT/57e34daab2df82dfd78edad9dfc8d56d-147_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [],
        "B": [
          [
            479.35,
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          [
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          [
            429.75,
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          [
            422.6,
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        "C": [
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            513.24,
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          [
            468.79,
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          [
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    },
    {
      "group_id": "group_149",
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      "history_image_paths": [
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      "question_format": "OEQ",
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          [
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      "question_id": "waymo_val_57e3f4d557f856f3b40d35329670f58f_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "In a construction scenario, Candidate B is the best choice because it maintains a smoother, more consistent path with a lower lateral deviation and a longer, more stable trajectory that safely navigates around the work zone. Compared to Candidate A, which veers further laterally and ends at a higher offset, and Candidate C, which is shorter and less consistent, B provides a more controlled and continuous progression through the area.",
      "bundle_frame_names": [
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        "57e3f4d557f856f3b40d35329670f58f-134",
        "57e3f4d557f856f3b40d35329670f58f-139",
        "57e3f4d557f856f3b40d35329670f58f-144",
        "57e3f4d557f856f3b40d35329670f58f-149"
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      "ground_truth_text": "In a construction scenario, Candidate B is the best choice because it maintains a smoother, more consistent path with a lower lateral deviation and a longer, more stable trajectory that safely navigates around the work zone. Compared to Candidate A, which veers further laterally and ends at a higher offset, and Candidate C, which is shorter and less consistent, B provides a more controlled and continuous progression through the area.",
      "frame_name": "57e3f4d557f856f3b40d35329670f58f-149",
      "anchor_image_path": "57e3f4d557f856f3b40d35329670f58f/CAM_FRONT/57e3f4d557f856f3b40d35329670f58f-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "57e3f4d557f856f3b40d35329670f58f",
      "scenario_cluster": "Construction",
      "bundle_id": "57e3f4d557f856f3b40d35329670f58f:group_149",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
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    },
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      "frame_index": 148,
      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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        ],
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          [
            101.141,
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          [
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        ]
      },
      "question_id": "waymo_val_5a71aee52b4764ceb8c0bef7f3e3a6c9_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it maintains a consistent and smooth trajectory with the longest path length and highest endpoint distance, indicating a stable and efficient lane change that safely clears the adjacent lane. Compared to B and C, it shows greater lateral progression and a more natural continuation of motion, avoiding abrupt changes in step size or lateral deviation.",
      "bundle_frame_names": [
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        "5a71aee52b4764ceb8c0bef7f3e3a6c9-133",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-138",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-143",
        "5a71aee52b4764ceb8c0bef7f3e3a6c9-148"
      ],
      "ground_truth_text": "Candidate A is the best choice because it maintains a consistent and smooth trajectory with the longest path length and highest endpoint distance, indicating a stable and efficient lane change that safely clears the adjacent lane. Compared to B and C, it shows greater lateral progression and a more natural continuation of motion, avoiding abrupt changes in step size or lateral deviation.",
      "frame_name": "5a71aee52b4764ceb8c0bef7f3e3a6c9-148",
      "anchor_image_path": "5a71aee52b4764ceb8c0bef7f3e3a6c9/CAM_FRONT/5a71aee52b4764ceb8c0bef7f3e3a6c9-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "5a71aee52b4764ceb8c0bef7f3e3a6c9:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
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      "question_format": "OEQ",
      "candidate_trajectories": {
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        "C": [
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            9.392,
            -0.026
          ],
          [
            35.254,
            -0.082
          ],
          [
            58.747,
            -0.371
          ],
          [
            74.853,
            -0.778
          ],
          [
            90.966,
            -1.241
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        ]
      },
      "question_id": "waymo_val_5f0d91d9bebc6af381629682682edcf8_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it maintains a smooth, consistent trajectory with minimal lateral deviation and stable step lengths, safely navigating past the foreign object debris while preserving a controlled path. In contrast, Candidate B exhibits excessive lateral movement and higher speed variability, increasing risk, while Candidate C cuts too close to the debris with erratic step sizes and a shorter, less stable path.",
      "bundle_frame_names": [
        "5f0d91d9bebc6af381629682682edcf8-128",
        "5f0d91d9bebc6af381629682682edcf8-133",
        "5f0d91d9bebc6af381629682682edcf8-138",
        "5f0d91d9bebc6af381629682682edcf8-143",
        "5f0d91d9bebc6af381629682682edcf8-148"
      ],
      "ground_truth_text": "Candidate A is the best choice because it maintains a smooth, consistent trajectory with minimal lateral deviation and stable step lengths, safely navigating past the foreign object debris while preserving a controlled path. In contrast, Candidate B exhibits excessive lateral movement and higher speed variability, increasing risk, while Candidate C cuts too close to the debris with erratic step sizes and a shorter, less stable path.",
      "frame_name": "5f0d91d9bebc6af381629682682edcf8-148",
      "anchor_image_path": "5f0d91d9bebc6af381629682682edcf8/CAM_FRONT/5f0d91d9bebc6af381629682682edcf8-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "5f0d91d9bebc6af381629682682edcf8",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "5f0d91d9bebc6af381629682682edcf8:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
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          [
            430.87,
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        "C": [
          [
            494.86,
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          [
            491.73,
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          [
            495.97,
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          [
            500.48,
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          [
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      }
    },
    {
      "group_id": "group_147",
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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        "C": [
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          [
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          [
            15.634,
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          [
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          [
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      "question_id": "waymo_val_605a964cc30b61fedf8a41bdf130f505_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it follows a smooth, consistent path with minimal lateral deviation and appropriate speed, staying within a reasonable distance from the observed path while maintaining a stable trajectory. In contrast, Candidates B and C show excessive lateral drift and longer, less controlled paths, which suggest less predictable or potentially unsafe behavior for a cyclist.",
      "bundle_frame_names": [
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        "605a964cc30b61fedf8a41bdf130f505-142",
        "605a964cc30b61fedf8a41bdf130f505-147"
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      "ground_truth_text": "Candidate A is the best choice because it follows a smooth, consistent path with minimal lateral deviation and appropriate speed, staying within a reasonable distance from the observed path while maintaining a stable trajectory. In contrast, Candidates B and C show excessive lateral drift and longer, less controlled paths, which suggest less predictable or potentially unsafe behavior for a cyclist.",
      "frame_name": "605a964cc30b61fedf8a41bdf130f505-147",
      "anchor_image_path": "605a964cc30b61fedf8a41bdf130f505/CAM_FRONT/605a964cc30b61fedf8a41bdf130f505-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "605a964cc30b61fedf8a41bdf130f505",
      "scenario_cluster": "Cyclist",
      "bundle_id": "605a964cc30b61fedf8a41bdf130f505:group_147",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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      "question_id": "waymo_val_61a2063cc014a36087e3031a2fc5cefc_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the best choice because it safely and smoothly merges into the lane while maintaining a consistent and appropriate speed, avoiding abrupt maneuvers. Unlike Candidate A, which cuts in too early and ends abruptly, and Candidate C, which remains too close to the original path and fails to complete the merge, B completes a natural and extended lane change that respects the surrounding traffic flow.",
      "bundle_frame_names": [
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        "61a2063cc014a36087e3031a2fc5cefc-144",
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      "ground_truth_text": "Candidate B is the best choice because it safely and smoothly merges into the lane while maintaining a consistent and appropriate speed, avoiding abrupt maneuvers. Unlike Candidate A, which cuts in too early and ends abruptly, and Candidate C, which remains too close to the original path and fails to complete the merge, B completes a natural and extended lane change that respects the surrounding traffic flow.",
      "frame_name": "61a2063cc014a36087e3031a2fc5cefc-149",
      "anchor_image_path": "61a2063cc014a36087e3031a2fc5cefc/CAM_FRONT/61a2063cc014a36087e3031a2fc5cefc-149_FRONT.jpg",
      "task": "trajectory-prediction",
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      "scenario_cluster": "Cut-ins",
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      "scoring_type": "open_oeq_text",
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          [
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          [
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      "question_id": "waymo_val_6b91e7d3471b70637f17cd8c0c953a09_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the best choice because it maintains a smooth, controlled lateral path with minimal deviation, avoiding excessive lateral movement while still clearing the foreign object debris. Compared to A, it has a more consistent step size and slightly shorter path length, and unlike C, it avoids an overly aggressive lateral shift that could risk instability or overshoot.",
      "bundle_frame_names": [
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      "ground_truth_text": "Candidate B is the best choice because it maintains a smooth, controlled lateral path with minimal deviation, avoiding excessive lateral movement while still clearing the foreign object debris. Compared to A, it has a more consistent step size and slightly shorter path length, and unlike C, it avoids an overly aggressive lateral shift that could risk instability or overshoot.",
      "frame_name": "6b91e7d3471b70637f17cd8c0c953a09-149",
      "anchor_image_path": "6b91e7d3471b70637f17cd8c0c953a09/CAM_FRONT/6b91e7d3471b70637f17cd8c0c953a09-149_FRONT.jpg",
      "task": "trajectory-prediction",
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      "scenario_cluster": "Foreign Object Debris",
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
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          ],
          [
            0.042,
            -0.005
          ]
        ],
        "C": [
          [
            0.077,
            0.0
          ],
          [
            2.463,
            0.002
          ],
          [
            9.781,
            -0.628
          ],
          [
            17.949,
            -2.019
          ],
          [
            29.699,
            -5.017
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        ]
      },
      "question_id": "waymo_val_6c5f2a71a41a720105b000ec3b1131e7_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it represents a smooth, gradual lane change with a reasonable path length and lateral displacement, maintaining safe and stable motion. In contrast, Candidate B shows minimal movement, suggesting no meaningful maneuver, while Candidate C exhibits excessive lateral drift and high speed, indicating an unsafe and unrealistic trajectory for a single-lane maneuver.",
      "bundle_frame_names": [
        "6c5f2a71a41a720105b000ec3b1131e7-128",
        "6c5f2a71a41a720105b000ec3b1131e7-133",
        "6c5f2a71a41a720105b000ec3b1131e7-138",
        "6c5f2a71a41a720105b000ec3b1131e7-143",
        "6c5f2a71a41a720105b000ec3b1131e7-148"
      ],
      "ground_truth_text": "Candidate A is the best choice because it represents a smooth, gradual lane change with a reasonable path length and lateral displacement, maintaining safe and stable motion. In contrast, Candidate B shows minimal movement, suggesting no meaningful maneuver, while Candidate C exhibits excessive lateral drift and high speed, indicating an unsafe and unrealistic trajectory for a single-lane maneuver.",
      "frame_name": "6c5f2a71a41a720105b000ec3b1131e7-148",
      "anchor_image_path": "6c5f2a71a41a720105b000ec3b1131e7/CAM_FRONT/6c5f2a71a41a720105b000ec3b1131e7-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "6c5f2a71a41a720105b000ec3b1131e7",
      "scenario_cluster": "Single-Lane Maneuvers",
      "bundle_id": "6c5f2a71a41a720105b000ec3b1131e7:group_148",
      "image_paths": [
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      "image_path": "6c5f2a71a41a720105b000ec3b1131e7/CAM_FRONT/6c5f2a71a41a720105b000ec3b1131e7-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
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          [
            560.9,
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          [
            614.88,
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        ],
        "B": [],
        "C": [
          [
            493.88,
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          [
            572.15,
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          [
            623.62,
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          [
            684.56,
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      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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        "6da6d10808633154702c238b489579f2/CAM_FRONT/6da6d10808633154702c238b489579f2-149_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
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            3.21,
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          [
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          [
            19.761,
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          [
            26.016,
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          [
            33.773,
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        ],
        "B": [
          [
            3.239,
            -0.196
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          [
            12.655,
            -1.658
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          [
            24.589,
            -3.589
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          [
            35.681,
            -5.198
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          [
            51.09,
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        ],
        "C": [
          [
            2.456,
            -0.101
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          [
            11.341,
            -1.61
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          [
            22.469,
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          [
            33.107,
            -6.246
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          [
            48.19,
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        ]
      },
      "question_id": "waymo_val_6da6d10808633154702c238b489579f2_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it maintains a controlled, consistent trajectory with minimal lateral deviation and stable step lengths, avoiding unnecessary acceleration or excessive path length that could risk the vehicle or the debris. In contrast, Candidates B and C exhibit higher lateral movement and longer, less predictable paths, increasing the risk of collision or instability when navigating around the foreign object debris.",
      "bundle_frame_names": [
        "6da6d10808633154702c238b489579f2-129",
        "6da6d10808633154702c238b489579f2-134",
        "6da6d10808633154702c238b489579f2-139",
        "6da6d10808633154702c238b489579f2-144",
        "6da6d10808633154702c238b489579f2-149"
      ],
      "ground_truth_text": "Candidate A is the best choice because it maintains a controlled, consistent trajectory with minimal lateral deviation and stable step lengths, avoiding unnecessary acceleration or excessive path length that could risk the vehicle or the debris. In contrast, Candidates B and C exhibit higher lateral movement and longer, less predictable paths, increasing the risk of collision or instability when navigating around the foreign object debris.",
      "frame_name": "6da6d10808633154702c238b489579f2-149",
      "anchor_image_path": "6da6d10808633154702c238b489579f2/CAM_FRONT/6da6d10808633154702c238b489579f2-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "6da6d10808633154702c238b489579f2",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "6da6d10808633154702c238b489579f2:group_149",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
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          [
            662.41,
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          [
            684.75,
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          [
            688.74,
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          [
            690.61,
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        "B": [
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            627.25,
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          [
            657.94,
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          [
            664.63,
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          [
            660.61,
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          [
            656.55,
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        ],
        "C": [
          [
            628.41,
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          [
            674.84,
            915.29
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          [
            701.04,
            807.03
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          [
            711.33,
            774.82
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          [
            712.93,
            754.29
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        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
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          [
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          [
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          [
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          [
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        ],
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          [
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          [
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          [
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          [
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          [
            20.928,
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          [
            35.588,
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          [
            46.544,
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          [
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      "question_id": "waymo_val_6e73c2f589c607a008e4fd91932d36de_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "In a construction scenario, Candidate A is the best choice because it maintains a consistent and safe lateral position with minimal deviation, follows a smooth and predictable path, and avoids the excessive lateral drift seen in Candidate B and the erratic speed changes in Candidate C, which could compromise stability near the work zone.",
      "bundle_frame_names": [
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        "6e73c2f589c607a008e4fd91932d36de-134",
        "6e73c2f589c607a008e4fd91932d36de-139",
        "6e73c2f589c607a008e4fd91932d36de-144",
        "6e73c2f589c607a008e4fd91932d36de-149"
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      "ground_truth_text": "In a construction scenario, Candidate A is the best choice because it maintains a consistent and safe lateral position with minimal deviation, follows a smooth and predictable path, and avoids the excessive lateral drift seen in Candidate B and the erratic speed changes in Candidate C, which could compromise stability near the work zone.",
      "frame_name": "6e73c2f589c607a008e4fd91932d36de-149",
      "anchor_image_path": "6e73c2f589c607a008e4fd91932d36de/CAM_FRONT/6e73c2f589c607a008e4fd91932d36de-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "6e73c2f589c607a008e4fd91932d36de",
      "scenario_cluster": "Construction",
      "bundle_id": "6e73c2f589c607a008e4fd91932d36de:group_149",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
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          [
            490.0,
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        "B": [
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            494.14,
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          [
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          [
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          [
            491.15,
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        "C": [
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            493.6,
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          [
            492.4,
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          [
            491.42,
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          [
            490.83,
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          [
            490.59,
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        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
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            0.215,
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          [
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          [
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          [
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          [
            3.326,
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        ],
        "B": [
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            0.221,
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          [
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          [
            2.162,
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          [
            3.305,
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          [
            4.629,
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        "C": [
          [
            0.127,
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          [
            0.106,
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          [
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          [
            -0.019,
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          [
            -0.047,
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        ]
      },
      "question_id": "waymo_val_774c537b284a58c77de4106b1c190117_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it maintains a very short path length and minimal lateral movement, indicating a cautious and controlled response that is appropriate when approaching a special vehicle. In contrast, Candidates A and B show significantly longer trajectories with greater lateral displacement, suggesting unnecessary deviation that could compromise safety or efficiency in this scenario.",
      "bundle_frame_names": [
        "774c537b284a58c77de4106b1c190117-128",
        "774c537b284a58c77de4106b1c190117-133",
        "774c537b284a58c77de4106b1c190117-138",
        "774c537b284a58c77de4106b1c190117-143",
        "774c537b284a58c77de4106b1c190117-148"
      ],
      "ground_truth_text": "Candidate C is the best choice because it maintains a very short path length and minimal lateral movement, indicating a cautious and controlled response that is appropriate when approaching a special vehicle. In contrast, Candidates A and B show significantly longer trajectories with greater lateral displacement, suggesting unnecessary deviation that could compromise safety or efficiency in this scenario.",
      "frame_name": "774c537b284a58c77de4106b1c190117-148",
      "anchor_image_path": "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "774c537b284a58c77de4106b1c190117",
      "scenario_cluster": "Special Vehicles",
      "bundle_id": "774c537b284a58c77de4106b1c190117:group_148",
      "image_paths": [
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      "image_path": "774c537b284a58c77de4106b1c190117/CAM_FRONT/774c537b284a58c77de4106b1c190117-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
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        ],
        "B": [
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            950.21,
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          [
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          [
            656.95,
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        "C": []
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    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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          [
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        ],
        "B": [
          [
            2.512,
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          [
            9.362,
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          ],
          [
            15.523,
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          [
            19.776,
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          [
            23.851,
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        ],
        "C": [
          [
            2.502,
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          ],
          [
            8.592,
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          ],
          [
            12.25,
            0.13
          ],
          [
            13.846,
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          ],
          [
            15.209,
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        ]
      },
      "question_id": "waymo_val_7ff16a5def1effbc5a017cc9b063b9de_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it maintains a smooth, consistent trajectory with minimal lateral deviation, avoiding abrupt changes in speed or direction while effectively navigating around the foreign object debris. Compared to B, which extends further and shows higher lateral stability but less direct path efficiency, and C, which ends prematurely and exhibits greater speed fluctuations, A provides a balanced and controlled path that prioritizes safety and stability.",
      "bundle_frame_names": [
        "7ff16a5def1effbc5a017cc9b063b9de-129",
        "7ff16a5def1effbc5a017cc9b063b9de-134",
        "7ff16a5def1effbc5a017cc9b063b9de-139",
        "7ff16a5def1effbc5a017cc9b063b9de-144",
        "7ff16a5def1effbc5a017cc9b063b9de-149"
      ],
      "ground_truth_text": "Candidate A is the best choice because it maintains a smooth, consistent trajectory with minimal lateral deviation, avoiding abrupt changes in speed or direction while effectively navigating around the foreign object debris. Compared to B, which extends further and shows higher lateral stability but less direct path efficiency, and C, which ends prematurely and exhibits greater speed fluctuations, A provides a balanced and controlled path that prioritizes safety and stability.",
      "frame_name": "7ff16a5def1effbc5a017cc9b063b9de-149",
      "anchor_image_path": "7ff16a5def1effbc5a017cc9b063b9de/CAM_FRONT/7ff16a5def1effbc5a017cc9b063b9de-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "7ff16a5def1effbc5a017cc9b063b9de",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "7ff16a5def1effbc5a017cc9b063b9de:group_149",
      "image_paths": [
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      "image_path": "7ff16a5def1effbc5a017cc9b063b9de/CAM_FRONT/7ff16a5def1effbc5a017cc9b063b9de-149_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            487.05,
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          [
            481.8,
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          [
            480.44,
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          [
            481.15,
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        ],
        "B": [
          [
            504.01,
            2744.81
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          [
            485.53,
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          [
            488.69,
            862.66
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          [
            490.52,
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          [
            489.88,
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        ],
        "C": [
          [
            504.15,
            2765.41
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          [
            481.26,
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          [
            478.54,
            906.43
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          [
            478.3,
            882.16
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          [
            478.98,
            865.92
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        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
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          [
            5.067,
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          [
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          [
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          [
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        ],
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            -0.001
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          [
            4.556,
            -0.009
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          [
            6.005,
            -0.05
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          [
            6.76,
            -0.098
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          [
            7.511,
            -0.122
          ]
        ],
        "C": [
          [
            1.665,
            0.003
          ],
          [
            4.947,
            0.017
          ],
          [
            8.086,
            0.026
          ],
          [
            11.114,
            0.054
          ],
          [
            15.563,
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        ]
      },
      "question_id": "waymo_val_82a788aa8d0e911d173468d7a5679e76_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the most plausible trajectory for a cyclist, as it maintains a consistent forward path with smooth acceleration and minimal lateral deviation, suggesting natural motion along the road. In contrast, Candidate A extends too far ahead with a high endpoint and significant lateral drift, while Candidate B shows abrupt deceleration and a sharp turn, indicating an unnatural or unsafe maneuver.",
      "bundle_frame_names": [
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        "82a788aa8d0e911d173468d7a5679e76-133",
        "82a788aa8d0e911d173468d7a5679e76-138",
        "82a788aa8d0e911d173468d7a5679e76-143",
        "82a788aa8d0e911d173468d7a5679e76-148"
      ],
      "ground_truth_text": "Candidate C is the most plausible trajectory for a cyclist, as it maintains a consistent forward path with smooth acceleration and minimal lateral deviation, suggesting natural motion along the road. In contrast, Candidate A extends too far ahead with a high endpoint and significant lateral drift, while Candidate B shows abrupt deceleration and a sharp turn, indicating an unnatural or unsafe maneuver.",
      "frame_name": "82a788aa8d0e911d173468d7a5679e76-148",
      "anchor_image_path": "82a788aa8d0e911d173468d7a5679e76/CAM_FRONT/82a788aa8d0e911d173468d7a5679e76-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "82a788aa8d0e911d173468d7a5679e76",
      "scenario_cluster": "Cyclist",
      "bundle_id": "82a788aa8d0e911d173468d7a5679e76:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
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            516.87,
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          [
            517.97,
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          [
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      }
    },
    {
      "group_id": "group_149",
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      "history_image_paths": [
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
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      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
            5.872,
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          [
            15.775,
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          [
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        ],
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          [
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            4.076,
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      "question_id": "waymo_val_95aad68ad5531c1764066a71c4093238_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it maintains a smooth, consistent trajectory that safely navigates around the foreign object debris while keeping lateral displacement within a reasonable range. In contrast, Candidate B extends too far laterally and increases speed unnecessarily, while Candidate C abruptly stops and reverses, indicating an overly cautious and inefficient response.",
      "bundle_frame_names": [
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        "95aad68ad5531c1764066a71c4093238-144",
        "95aad68ad5531c1764066a71c4093238-149"
      ],
      "ground_truth_text": "Candidate A is the best choice because it maintains a smooth, consistent trajectory that safely navigates around the foreign object debris while keeping lateral displacement within a reasonable range. In contrast, Candidate B extends too far laterally and increases speed unnecessarily, while Candidate C abruptly stops and reverses, indicating an overly cautious and inefficient response.",
      "frame_name": "95aad68ad5531c1764066a71c4093238-149",
      "anchor_image_path": "95aad68ad5531c1764066a71c4093238/CAM_FRONT/95aad68ad5531c1764066a71c4093238-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "95aad68ad5531c1764066a71c4093238",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "95aad68ad5531c1764066a71c4093238:group_149",
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
            1082.32,
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        "B": [
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            901.09,
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          [
            1039.41,
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          [
            1090.35,
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          [
            1116.6,
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        "C": [
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            808.0,
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          [
            817.73,
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          [
            801.9,
            1502.32
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          [
            725.81,
            1731.93
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        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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          [
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        ],
        "B": [
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          [
            5.314,
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          [
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          [
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          [
            7.803,
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        ],
        "C": [
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          [
            5.482,
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          [
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          [
            10.367,
            -0.144
          ],
          [
            11.137,
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        ]
      },
      "question_id": "waymo_val_9d8cce1b82aeaec6173978bf27e1ff9a_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "At an intersection, Candidate B is the most appropriate trajectory as it safely navigates through the intersection with minimal lateral deviation and a controlled, consistent path, avoiding unnecessary lateral movement or excessive speed. In contrast, Candidate A extends far beyond the intersection with significant lateral drift, while Candidate C shows erratic step sizes and an unstable path, both indicating less reliable and potentially unsafe behavior.",
      "bundle_frame_names": [
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        "9d8cce1b82aeaec6173978bf27e1ff9a-133",
        "9d8cce1b82aeaec6173978bf27e1ff9a-138",
        "9d8cce1b82aeaec6173978bf27e1ff9a-143",
        "9d8cce1b82aeaec6173978bf27e1ff9a-148"
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      "ground_truth_text": "At an intersection, Candidate B is the most appropriate trajectory as it safely navigates through the intersection with minimal lateral deviation and a controlled, consistent path, avoiding unnecessary lateral movement or excessive speed. In contrast, Candidate A extends far beyond the intersection with significant lateral drift, while Candidate C shows erratic step sizes and an unstable path, both indicating less reliable and potentially unsafe behavior.",
      "frame_name": "9d8cce1b82aeaec6173978bf27e1ff9a-148",
      "anchor_image_path": "9d8cce1b82aeaec6173978bf27e1ff9a/CAM_FRONT/9d8cce1b82aeaec6173978bf27e1ff9a-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "9d8cce1b82aeaec6173978bf27e1ff9a",
      "scenario_cluster": "Intersection",
      "bundle_id": "9d8cce1b82aeaec6173978bf27e1ff9a:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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        "B": [
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          [
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          [
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          [
            556.92,
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        "C": [
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          [
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          [
            507.99,
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          [
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      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
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      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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        ],
        "B": [
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          [
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          [
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          [
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        ],
        "C": [
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          [
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          [
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          [
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          [
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      },
      "question_id": "waymo_val_a30e34193a7e4cbe64d571417e590fc9_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the most reasonable trajectory for a cyclist, as it maintains a smooth and consistent path with moderate curvature and step sizes, avoiding the excessive lateral deviation seen in A and the abrupt, short path of B. It safely progresses forward with a natural progression in both distance and direction, aligning with typical cyclist behavior in an open environment.",
      "bundle_frame_names": [
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        "a30e34193a7e4cbe64d571417e590fc9-134",
        "a30e34193a7e4cbe64d571417e590fc9-139",
        "a30e34193a7e4cbe64d571417e590fc9-144",
        "a30e34193a7e4cbe64d571417e590fc9-149"
      ],
      "ground_truth_text": "Candidate C is the most reasonable trajectory for a cyclist, as it maintains a smooth and consistent path with moderate curvature and step sizes, avoiding the excessive lateral deviation seen in A and the abrupt, short path of B. It safely progresses forward with a natural progression in both distance and direction, aligning with typical cyclist behavior in an open environment.",
      "frame_name": "a30e34193a7e4cbe64d571417e590fc9-149",
      "anchor_image_path": "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "a30e34193a7e4cbe64d571417e590fc9",
      "scenario_cluster": "Cyclist",
      "bundle_id": "a30e34193a7e4cbe64d571417e590fc9:group_149",
      "image_paths": [
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        "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-149_FRONT.jpg"
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      "image_path": "a30e34193a7e4cbe64d571417e590fc9/CAM_FRONT/a30e34193a7e4cbe64d571417e590fc9-149_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            717.39,
            1710.24
          ],
          [
            1085.61,
            929.67
          ],
          [
            1690.98,
            846.74
          ],
          [
            2127.8,
            813.27
          ],
          [
            2498.95,
            784.59
          ]
        ],
        "B": [
          [
            704.93,
            1755.13
          ],
          [
            978.25,
            972.62
          ],
          [
            1322.06,
            898.53
          ],
          [
            1583.53,
            872.09
          ],
          [
            1867.58,
            847.47
          ]
        ],
        "C": [
          [
            699.17,
            1740.81
          ],
          [
            961.21,
            951.3
          ],
          [
            1357.64,
            867.96
          ],
          [
            1703.88,
            837.92
          ],
          [
            2080.9,
            811.37
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
        "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-128_FRONT.jpg",
        "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-133_FRONT.jpg",
        "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-138_FRONT.jpg",
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        "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            0.041,
            0.0
          ],
          [
            1.523,
            0.005
          ],
          [
            5.872,
            0.122
          ],
          [
            10.465,
            0.815
          ],
          [
            16.336,
            3.913
          ]
        ],
        "B": [
          [
            0.004,
            -0.001
          ],
          [
            0.138,
            -0.002
          ],
          [
            0.953,
            -0.003
          ],
          [
            2.078,
            -0.003
          ],
          [
            3.524,
            0.004
          ]
        ],
        "C": [
          [
            0.027,
            0.0
          ],
          [
            0.615,
            0.002
          ],
          [
            2.604,
            0.007
          ],
          [
            4.28,
            0.013
          ],
          [
            5.687,
            0.026
          ]
        ]
      },
      "question_id": "waymo_val_adba328ccd36b14cb2d1f2c777859861_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "At an intersection, Candidate A is the most appropriate trajectory as it shows a clear forward progression with a significant path length and lateral displacement, indicating a proper turn or through movement. In contrast, Candidates B and C exhibit minimal forward motion and remain nearly stationary, suggesting they do not properly navigate the intersection.",
      "bundle_frame_names": [
        "adba328ccd36b14cb2d1f2c777859861-128",
        "adba328ccd36b14cb2d1f2c777859861-133",
        "adba328ccd36b14cb2d1f2c777859861-138",
        "adba328ccd36b14cb2d1f2c777859861-143",
        "adba328ccd36b14cb2d1f2c777859861-148"
      ],
      "ground_truth_text": "At an intersection, Candidate A is the most appropriate trajectory as it shows a clear forward progression with a significant path length and lateral displacement, indicating a proper turn or through movement. In contrast, Candidates B and C exhibit minimal forward motion and remain nearly stationary, suggesting they do not properly navigate the intersection.",
      "frame_name": "adba328ccd36b14cb2d1f2c777859861-148",
      "anchor_image_path": "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "adba328ccd36b14cb2d1f2c777859861",
      "scenario_cluster": "Intersection",
      "bundle_id": "adba328ccd36b14cb2d1f2c777859861:group_148",
      "image_paths": [
        "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-128_FRONT.jpg",
        "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-133_FRONT.jpg",
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        "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-148_FRONT.jpg"
      ],
      "image_path": "adba328ccd36b14cb2d1f2c777859861/CAM_FRONT/adba328ccd36b14cb2d1f2c777859861-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            468.21,
            1181.99
          ],
          [
            393.58,
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          ],
          [
            199.32,
            856.34
          ]
        ],
        "B": [
          [
            562.9,
            4324.19
          ],
          [
            507.13,
            1723.68
          ]
        ],
        "C": [
          [
            517.74,
            2575.68
          ],
          [
            499.08,
            1448.65
          ],
          [
            492.87,
            1202.45
          ]
        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-133_FRONT.jpg",
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        "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            0.654,
            -0.001
          ],
          [
            0.998,
            -0.0
          ],
          [
            1.05,
            0.001
          ],
          [
            1.191,
            0.002
          ],
          [
            1.676,
            0.005
          ]
        ],
        "B": [
          [
            0.647,
            -0.0
          ],
          [
            1.166,
            0.001
          ],
          [
            1.977,
            -0.001
          ],
          [
            3.486,
            -0.026
          ],
          [
            6.235,
            -0.232
          ]
        ],
        "C": [
          [
            0.384,
            0.0
          ],
          [
            0.81,
            0.001
          ],
          [
            0.882,
            0.001
          ],
          [
            0.874,
            0.001
          ],
          [
            0.874,
            0.0
          ]
        ]
      },
      "question_id": "waymo_val_ae776e971249029cab5f6b069a677897_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the best choice because it shows a consistent forward trajectory with increasing speed and a clear path away from the vehicle’s current position, indicating a natural and safe continuation of motion. In contrast, Candidate A and C both show minimal displacement and abrupt changes in step size, suggesting hesitation or an unnatural stop, which would be unsafe in a dynamic pedestrian scenario.",
      "bundle_frame_names": [
        "ae776e971249029cab5f6b069a677897-128",
        "ae776e971249029cab5f6b069a677897-133",
        "ae776e971249029cab5f6b069a677897-138",
        "ae776e971249029cab5f6b069a677897-143",
        "ae776e971249029cab5f6b069a677897-148"
      ],
      "ground_truth_text": "Candidate B is the best choice because it shows a consistent forward trajectory with increasing speed and a clear path away from the vehicle’s current position, indicating a natural and safe continuation of motion. In contrast, Candidate A and C both show minimal displacement and abrupt changes in step size, suggesting hesitation or an unnatural stop, which would be unsafe in a dynamic pedestrian scenario.",
      "frame_name": "ae776e971249029cab5f6b069a677897-148",
      "anchor_image_path": "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "ae776e971249029cab5f6b069a677897",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "ae776e971249029cab5f6b069a677897:group_148",
      "image_paths": [
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      ],
      "image_path": "ae776e971249029cab5f6b069a677897/CAM_FRONT/ae776e971249029cab5f6b069a677897-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [],
        "B": [
          [
            525.58,
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          ],
          [
            511.91,
            1733.87
          ],
          [
            547.34,
            1140.59
          ]
        ],
        "C": []
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "bbaa7f64d17a4f25f26d1ef21b5048ca/CAM_FRONT/bbaa7f64d17a4f25f26d1ef21b5048ca-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            -0.0,
            0.0
          ],
          [
            0.13,
            -0.001
          ],
          [
            1.256,
            -0.009
          ],
          [
            3.388,
            -0.06
          ],
          [
            7.529,
            -0.862
          ]
        ],
        "B": [
          [
            0.041,
            -0.0
          ],
          [
            0.382,
            -0.0
          ],
          [
            0.819,
            -0.003
          ],
          [
            1.134,
            -0.008
          ],
          [
            1.659,
            -0.017
          ]
        ],
        "C": [
          [
            0.067,
            0.0
          ],
          [
            0.671,
            -0.003
          ],
          [
            1.747,
            -0.016
          ],
          [
            2.783,
            -0.048
          ],
          [
            4.54,
            -0.165
          ]
        ]
      },
      "question_id": "waymo_val_bbaa7f64d17a4f25f26d1ef21b5048ca_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it maintains a smooth, controlled path with minimal lateral deviation while progressing steadily forward, avoiding abrupt maneuvers or excessive distance traveled. Compared to A, which veers significantly off course, and B, which barely moves, C strikes the optimal balance between safety and progress in response to the foreign object debris scenario.",
      "bundle_frame_names": [
        "bbaa7f64d17a4f25f26d1ef21b5048ca-128",
        "bbaa7f64d17a4f25f26d1ef21b5048ca-133",
        "bbaa7f64d17a4f25f26d1ef21b5048ca-138",
        "bbaa7f64d17a4f25f26d1ef21b5048ca-143",
        "bbaa7f64d17a4f25f26d1ef21b5048ca-148"
      ],
      "ground_truth_text": "Candidate C is the best choice because it maintains a smooth, controlled path with minimal lateral deviation while progressing steadily forward, avoiding abrupt maneuvers or excessive distance traveled. Compared to A, which veers significantly off course, and B, which barely moves, C strikes the optimal balance between safety and progress in response to the foreign object debris scenario.",
      "frame_name": "bbaa7f64d17a4f25f26d1ef21b5048ca-148",
      "anchor_image_path": "bbaa7f64d17a4f25f26d1ef21b5048ca/CAM_FRONT/bbaa7f64d17a4f25f26d1ef21b5048ca-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "bbaa7f64d17a4f25f26d1ef21b5048ca",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "bbaa7f64d17a4f25f26d1ef21b5048ca:group_148",
      "image_paths": [
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        "bbaa7f64d17a4f25f26d1ef21b5048ca/CAM_FRONT/bbaa7f64d17a4f25f26d1ef21b5048ca-148_FRONT.jpg"
      ],
      "image_path": "bbaa7f64d17a4f25f26d1ef21b5048ca/CAM_FRONT/bbaa7f64d17a4f25f26d1ef21b5048ca-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
          [
            535.03,
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          ],
          [
            652.4,
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          ]
        ],
        "B": [],
        "C": [
          [
            640.58,
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          ],
          [
            545.95,
            2294.63
          ],
          [
            556.49,
            1378.1
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        ]
      }
    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
            1.886,
            0.008
          ],
          [
            4.987,
            0.146
          ],
          [
            7.27,
            0.512
          ],
          [
            9.282,
            1.129
          ],
          [
            12.428,
            3.034
          ]
        ],
        "B": [
          [
            1.696,
            0.012
          ],
          [
            2.467,
            -0.0
          ],
          [
            2.2,
            0.016
          ],
          [
            1.95,
            0.033
          ],
          [
            1.385,
            0.101
          ]
        ],
        "C": [
          [
            0.892,
            -0.004
          ],
          [
            3.526,
            0.063
          ],
          [
            4.139,
            0.113
          ],
          [
            5.274,
            0.279
          ],
          [
            6.872,
            0.627
          ]
        ]
      },
      "question_id": "waymo_val_bd09cff83130f018440c580cf71e54dc_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "In a Foreign Object Debris scenario, the vehicle should maintain a safe, controlled path while avoiding the hazard. Candidate C provides a smooth, forward progression with minimal lateral deviation and consistent step sizes, indicating a stable and deliberate maneuver that safely navigates past the debris without abrupt changes or excessive lateral movement. In contrast, Candidate A shows excessive lateral drift and a long, erratic path, while Candidate B abruptly reverses direction and stops too early, both of which are unsafe and impractical responses to the situation.",
      "bundle_frame_names": [
        "bd09cff83130f018440c580cf71e54dc-127",
        "bd09cff83130f018440c580cf71e54dc-132",
        "bd09cff83130f018440c580cf71e54dc-137",
        "bd09cff83130f018440c580cf71e54dc-142",
        "bd09cff83130f018440c580cf71e54dc-147"
      ],
      "ground_truth_text": "In a Foreign Object Debris scenario, the vehicle should maintain a safe, controlled path while avoiding the hazard. Candidate C provides a smooth, forward progression with minimal lateral deviation and consistent step sizes, indicating a stable and deliberate maneuver that safely navigates past the debris without abrupt changes or excessive lateral movement. In contrast, Candidate A shows excessive lateral drift and a long, erratic path, while Candidate B abruptly reverses direction and stops too early, both of which are unsafe and impractical responses to the situation.",
      "frame_name": "bd09cff83130f018440c580cf71e54dc-147",
      "anchor_image_path": "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "bd09cff83130f018440c580cf71e54dc",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "bd09cff83130f018440c580cf71e54dc:group_147",
      "image_paths": [
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      ],
      "image_path": "bd09cff83130f018440c580cf71e54dc/CAM_FRONT/bd09cff83130f018440c580cf71e54dc-147_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
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          [
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          [
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          [
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          [
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      }
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    {
      "group_id": "group_149",
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
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      "candidate_trajectories": {
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          [
            11.067,
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      "question_id": "waymo_val_beb80cd63303bb6251e25af2c6802d68_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the most plausible trajectory for a pedestrian scenario, as it shows a smooth, continuous forward motion with increasing step lengths and a consistent direction, indicating natural walking behavior. In contrast, Candidate A ends abruptly and with minimal lateral deviation, while Candidate C exhibits sharp turns and rapid deceleration, suggesting an unnatural or unstable path inconsistent with typical pedestrian movement.",
      "bundle_frame_names": [
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        "beb80cd63303bb6251e25af2c6802d68-144",
        "beb80cd63303bb6251e25af2c6802d68-149"
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      "ground_truth_text": "Candidate B is the most plausible trajectory for a pedestrian scenario, as it shows a smooth, continuous forward motion with increasing step lengths and a consistent direction, indicating natural walking behavior. In contrast, Candidate A ends abruptly and with minimal lateral deviation, while Candidate C exhibits sharp turns and rapid deceleration, suggesting an unnatural or unstable path inconsistent with typical pedestrian movement.",
      "frame_name": "beb80cd63303bb6251e25af2c6802d68-149",
      "anchor_image_path": "beb80cd63303bb6251e25af2c6802d68/CAM_FRONT/beb80cd63303bb6251e25af2c6802d68-149_FRONT.jpg",
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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      "source_dataset": "waymo_validation",
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      "ground_truth": "Candidate A is the best choice because it maintains a straight, stable path with minimal lateral deviation and consistent forward progression, indicating a natural and safe continuation of the cyclist’s motion. In contrast, Candidate B shows a sharp turn and significant lateral drift, while Candidate C deviates laterally and slows abruptly, both suggesting less predictable or unsafe behavior.",
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      "ground_truth_text": "Candidate A is the best choice because it maintains a straight, stable path with minimal lateral deviation and consistent forward progression, indicating a natural and safe continuation of the cyclist’s motion. In contrast, Candidate B shows a sharp turn and significant lateral drift, while Candidate C deviates laterally and slows abruptly, both suggesting less predictable or unsafe behavior.",
      "frame_name": "d007146fded6c0160d4c6f8ca5b60306-147",
      "anchor_image_path": "d007146fded6c0160d4c6f8ca5b60306/CAM_FRONT/d007146fded6c0160d4c6f8ca5b60306-147_FRONT.jpg",
      "task": "trajectory-prediction",
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      "scenario_cluster": "Cyclist",
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      "scoring_type": "open_oeq_text",
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      "history_image_paths": [
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
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      "question_format": "OEQ",
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      "question_id": "waymo_val_d1d943b7f0e8036803e262b8e0f9681c_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "In a construction scenario, Candidate C is the best choice because it maintains a smooth, consistent trajectory with increasing step size, indicating controlled acceleration and safe progression through the area. Unlike Candidate A, which ends abruptly and stays close to the original path, and Candidate B, which veers too far laterally and shows erratic step changes, Candidate C follows a natural, extended path that safely navigates the construction zone while maintaining optimal forward momentum.",
      "bundle_frame_names": [
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        "d1d943b7f0e8036803e262b8e0f9681c-147"
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      "ground_truth_text": "In a construction scenario, Candidate C is the best choice because it maintains a smooth, consistent trajectory with increasing step size, indicating controlled acceleration and safe progression through the area. Unlike Candidate A, which ends abruptly and stays close to the original path, and Candidate B, which veers too far laterally and shows erratic step changes, Candidate C follows a natural, extended path that safely navigates the construction zone while maintaining optimal forward momentum.",
      "frame_name": "d1d943b7f0e8036803e262b8e0f9681c-147",
      "anchor_image_path": "d1d943b7f0e8036803e262b8e0f9681c/CAM_FRONT/d1d943b7f0e8036803e262b8e0f9681c-147_FRONT.jpg",
      "task": "trajectory-prediction",
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      "scenario_cluster": "Construction",
      "bundle_id": "d1d943b7f0e8036803e262b8e0f9681c:group_147",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
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    {
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
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      "candidate_trajectories": {
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      "question_id": "waymo_val_d6ea4dab948d2d1ce93216f3285a38f5_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice at this intersection because it follows a smooth, straight path with consistent forward progression and minimal lateral deviation, indicating a natural continuation through the intersection. In contrast, Candidate B ends earlier with a shallower turn, and Candidate C veers significantly to the left, suggesting an incorrect or unsafe maneuver that could lead to a collision or lane violation.",
      "bundle_frame_names": [
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      "ground_truth_text": "Candidate A is the best choice at this intersection because it follows a smooth, straight path with consistent forward progression and minimal lateral deviation, indicating a natural continuation through the intersection. In contrast, Candidate B ends earlier with a shallower turn, and Candidate C veers significantly to the left, suggesting an incorrect or unsafe maneuver that could lead to a collision or lane violation.",
      "frame_name": "d6ea4dab948d2d1ce93216f3285a38f5-148",
      "anchor_image_path": "d6ea4dab948d2d1ce93216f3285a38f5/CAM_FRONT/d6ea4dab948d2d1ce93216f3285a38f5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d6ea4dab948d2d1ce93216f3285a38f5",
      "scenario_cluster": "Intersection",
      "bundle_id": "d6ea4dab948d2d1ce93216f3285a38f5:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
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          [
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          [
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          [
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        "B": [
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          [
            479.72,
            994.75
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          [
            471.12,
            872.5
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          [
            460.87,
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        "C": [
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            508.87,
            1773.83
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          [
            520.8,
            1000.74
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          [
            537.25,
            879.05
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          [
            556.84,
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        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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          [
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            0.015
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          [
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          [
            1.275,
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          [
            1.309,
            0.068
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          [
            1.361,
            0.078
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        ],
        "C": [
          [
            0.887,
            0.025
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          [
            5.222,
            1.145
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          [
            12.265,
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          [
            17.053,
            16.556
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          [
            21.326,
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        ]
      },
      "question_id": "waymo_val_d7029947c6c42936fd5b9c5ee47bcd8a_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best driving choice because it follows a smooth, continuous path through the intersection with increasing speed and proper trajectory progression, indicating a natural and safe passage. In contrast, Candidate A stops short of completing the intersection, and Candidate B remains nearly stationary, both failing to properly navigate the scene.",
      "bundle_frame_names": [
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        "d7029947c6c42936fd5b9c5ee47bcd8a-134",
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        "d7029947c6c42936fd5b9c5ee47bcd8a-144",
        "d7029947c6c42936fd5b9c5ee47bcd8a-149"
      ],
      "ground_truth_text": "Candidate C is the best driving choice because it follows a smooth, continuous path through the intersection with increasing speed and proper trajectory progression, indicating a natural and safe passage. In contrast, Candidate A stops short of completing the intersection, and Candidate B remains nearly stationary, both failing to properly navigate the scene.",
      "frame_name": "d7029947c6c42936fd5b9c5ee47bcd8a-149",
      "anchor_image_path": "d7029947c6c42936fd5b9c5ee47bcd8a/CAM_FRONT/d7029947c6c42936fd5b9c5ee47bcd8a-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d7029947c6c42936fd5b9c5ee47bcd8a",
      "scenario_cluster": "Intersection",
      "bundle_id": "d7029947c6c42936fd5b9c5ee47bcd8a:group_149",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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        "C": [
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          [
            -226.82,
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          [
            -687.4,
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          [
            -1312.49,
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    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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        ],
        "B": [
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            -0.012
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          [
            14.607,
            -0.078
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          [
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          [
            30.682,
            -0.183
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          [
            37.795,
            -0.234
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        ],
        "C": [
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          [
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          [
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          [
            15.199,
            -0.311
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          [
            16.153,
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      "question_id": "waymo_val_d7a169aa0b0b8247631ead8c0dad382f_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it follows a smooth, straight path with consistent forward progression and minimal lateral deviation, indicating a natural and safe pedestrian crossing. In contrast, Candidate B shows a slight lateral drift and longer path, while Candidate C abruptly terminates early with erratic step sizes, suggesting an incomplete or unsafe maneuver.",
      "bundle_frame_names": [
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        "d7a169aa0b0b8247631ead8c0dad382f-132",
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        "d7a169aa0b0b8247631ead8c0dad382f-142",
        "d7a169aa0b0b8247631ead8c0dad382f-147"
      ],
      "ground_truth_text": "Candidate A is the best choice because it follows a smooth, straight path with consistent forward progression and minimal lateral deviation, indicating a natural and safe pedestrian crossing. In contrast, Candidate B shows a slight lateral drift and longer path, while Candidate C abruptly terminates early with erratic step sizes, suggesting an incomplete or unsafe maneuver.",
      "frame_name": "d7a169aa0b0b8247631ead8c0dad382f-147",
      "anchor_image_path": "d7a169aa0b0b8247631ead8c0dad382f/CAM_FRONT/d7a169aa0b0b8247631ead8c0dad382f-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "d7a169aa0b0b8247631ead8c0dad382f",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "d7a169aa0b0b8247631ead8c0dad382f:group_147",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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          [
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          [
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        "B": [
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            506.7,
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          [
            500.83,
            874.87
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          [
            499.98,
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          [
            499.93,
            790.12
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          [
            499.93,
            776.59
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        "C": [
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            509.41,
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          [
            508.86,
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          [
            516.75,
            876.46
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          [
            519.42,
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          [
            518.84,
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        ]
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    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
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          [
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          [
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          [
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          [
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        ],
        "B": [
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            -0.004
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          [
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            -0.042
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          [
            1.79,
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          [
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          [
            2.213,
            -0.125
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        ],
        "C": [
          [
            0.323,
            -0.004
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          [
            1.335,
            -0.035
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          [
            2.221,
            -0.085
          ],
          [
            2.956,
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          ],
          [
            4.072,
            -0.069
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        ]
      },
      "question_id": "waymo_val_dabe56f068d94714e5181d3607498d1f_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "In a Foreign Object Debris scenario, the safest and most appropriate response is to maintain a stable, minimal lateral deviation while avoiding the hazard. Candidate A achieves this by staying close to the original path with nearly zero lateral movement and a short, controlled trajectory, minimizing risk to the vehicle and surrounding area. In contrast, Candidates B and C show increasing lateral drift and longer paths, which suggest unnecessary deviation and higher risk in a debris-affected environment.",
      "bundle_frame_names": [
        "dabe56f068d94714e5181d3607498d1f-128",
        "dabe56f068d94714e5181d3607498d1f-133",
        "dabe56f068d94714e5181d3607498d1f-138",
        "dabe56f068d94714e5181d3607498d1f-143",
        "dabe56f068d94714e5181d3607498d1f-148"
      ],
      "ground_truth_text": "In a Foreign Object Debris scenario, the safest and most appropriate response is to maintain a stable, minimal lateral deviation while avoiding the hazard. Candidate A achieves this by staying close to the original path with nearly zero lateral movement and a short, controlled trajectory, minimizing risk to the vehicle and surrounding area. In contrast, Candidates B and C show increasing lateral drift and longer paths, which suggest unnecessary deviation and higher risk in a debris-affected environment.",
      "frame_name": "dabe56f068d94714e5181d3607498d1f-148",
      "anchor_image_path": "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "dabe56f068d94714e5181d3607498d1f",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "dabe56f068d94714e5181d3607498d1f:group_148",
      "image_paths": [
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      "image_path": "dabe56f068d94714e5181d3607498d1f/CAM_FRONT/dabe56f068d94714e5181d3607498d1f-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [],
        "B": [
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          [
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          [
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        "C": [
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          [
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          [
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    },
    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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        "db145e0aed4000127391a3658d55f921/CAM_FRONT/db145e0aed4000127391a3658d55f921-147_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
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          [
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          [
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          [
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          [
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        ],
        "B": [
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          [
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          [
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          [
            0.259,
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          [
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        ],
        "C": [
          [
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          [
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          ],
          [
            6.141,
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          [
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            -3.75
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          [
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        ]
      },
      "question_id": "waymo_val_db145e0aed4000127391a3658d55f921_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it represents a natural, continuous forward trajectory with increasing speed and a smooth lateral drift, consistent with a cyclist maintaining momentum while navigating a turn. In contrast, Candidate A shows a sharp deceleration and abrupt lateral movement, while Candidate B indicates minimal motion, both of which are inconsistent with typical cyclist behavior in this scenario.",
      "bundle_frame_names": [
        "db145e0aed4000127391a3658d55f921-127",
        "db145e0aed4000127391a3658d55f921-132",
        "db145e0aed4000127391a3658d55f921-137",
        "db145e0aed4000127391a3658d55f921-142",
        "db145e0aed4000127391a3658d55f921-147"
      ],
      "ground_truth_text": "Candidate C is the best choice because it represents a natural, continuous forward trajectory with increasing speed and a smooth lateral drift, consistent with a cyclist maintaining momentum while navigating a turn. In contrast, Candidate A shows a sharp deceleration and abrupt lateral movement, while Candidate B indicates minimal motion, both of which are inconsistent with typical cyclist behavior in this scenario.",
      "frame_name": "db145e0aed4000127391a3658d55f921-147",
      "anchor_image_path": "db145e0aed4000127391a3658d55f921/CAM_FRONT/db145e0aed4000127391a3658d55f921-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "db145e0aed4000127391a3658d55f921",
      "scenario_cluster": "Cyclist",
      "bundle_id": "db145e0aed4000127391a3658d55f921:group_147",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
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          [
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          [
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        "B": [],
        "C": [
          [
            674.51,
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          [
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          [
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      }
    },
    {
      "group_id": "group_147",
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      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
          [
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          [
            2.146,
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          [
            2.529,
            0.02
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          [
            3.957,
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          [
            8.156,
            0.41
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        ],
        "B": [
          [
            1.625,
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          [
            4.811,
            0.134
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          [
            11.357,
            0.133
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          [
            18.558,
            -0.23
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          [
            28.525,
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        ],
        "C": [
          [
            1.531,
            -0.024
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          [
            2.81,
            0.03
          ],
          [
            5.462,
            0.325
          ],
          [
            9.97,
            0.534
          ],
          [
            18.23,
            0.137
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        ]
      },
      "question_id": "waymo_val_e350da1e9a0a9a8a5a0f2665ae0c4bdc_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it maintains a smooth, controlled trajectory with moderate acceleration and lateral stability, avoiding abrupt maneuvers or excessive speed that could compromise safety during a cut-in scenario. Compared to A, which shows erratic lateral movement and a short path, and B, which accelerates too aggressively and veers too far laterally, C provides a balanced and predictable response that safely accommodates the cut-in.",
      "bundle_frame_names": [
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-127",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-132",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-137",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-142",
        "e350da1e9a0a9a8a5a0f2665ae0c4bdc-147"
      ],
      "ground_truth_text": "Candidate C is the best choice because it maintains a smooth, controlled trajectory with moderate acceleration and lateral stability, avoiding abrupt maneuvers or excessive speed that could compromise safety during a cut-in scenario. Compared to A, which shows erratic lateral movement and a short path, and B, which accelerates too aggressively and veers too far laterally, C provides a balanced and predictable response that safely accommodates the cut-in.",
      "frame_name": "e350da1e9a0a9a8a5a0f2665ae0c4bdc-147",
      "anchor_image_path": "e350da1e9a0a9a8a5a0f2665ae0c4bdc/CAM_FRONT/e350da1e9a0a9a8a5a0f2665ae0c4bdc-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "e350da1e9a0a9a8a5a0f2665ae0c4bdc",
      "scenario_cluster": "Cut-ins",
      "bundle_id": "e350da1e9a0a9a8a5a0f2665ae0c4bdc:group_147",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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            429.2,
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            458.46,
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          [
            481.11,
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            509.61,
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        "C": [
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            495.69,
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            410.1,
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          [
            426.4,
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          [
            485.46,
            838.31
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        ]
      }
    },
    {
      "group_id": "group_149",
      "frame_index": 149,
      "history_image_paths": [
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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        ],
        "B": [
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          [
            0.07,
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          [
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        ],
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          [
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          [
            1.666,
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          [
            3.373,
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            5.741,
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      "question_id": "waymo_val_e3e6244899ebdbf1a0d0d45fc513b34c_group_149_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the best choice because it represents a minimal, stable movement that keeps the pedestrian safely within the immediate vicinity of their starting position, avoiding any significant lateral or longitudinal displacement. In contrast, Candidate A and Candidate C show large, potentially unsafe trajectories that extend far from the origin, increasing risk in a pedestrian scenario where controlled, predictable motion is critical.",
      "bundle_frame_names": [
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        "e3e6244899ebdbf1a0d0d45fc513b34c-144",
        "e3e6244899ebdbf1a0d0d45fc513b34c-149"
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      "ground_truth_text": "Candidate B is the best choice because it represents a minimal, stable movement that keeps the pedestrian safely within the immediate vicinity of their starting position, avoiding any significant lateral or longitudinal displacement. In contrast, Candidate A and Candidate C show large, potentially unsafe trajectories that extend far from the origin, increasing risk in a pedestrian scenario where controlled, predictable motion is critical.",
      "frame_name": "e3e6244899ebdbf1a0d0d45fc513b34c-149",
      "anchor_image_path": "e3e6244899ebdbf1a0d0d45fc513b34c/CAM_FRONT/e3e6244899ebdbf1a0d0d45fc513b34c-149_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "e3e6244899ebdbf1a0d0d45fc513b34c",
      "scenario_cluster": "Pedestrian",
      "bundle_id": "e3e6244899ebdbf1a0d0d45fc513b34c:group_149",
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
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        "B": [],
        "C": [
          [
            318.72,
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          [
            323.64,
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    {
      "group_id": "group_147",
      "frame_index": 147,
      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
        "A": [
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          [
            2.919,
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          [
            6.6,
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          [
            12.442,
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        ],
        "B": [
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            0.433,
            -0.0
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          [
            2.09,
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          [
            3.53,
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          [
            4.274,
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          [
            4.814,
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        ],
        "C": [
          [
            0.452,
            -0.002
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          [
            3.654,
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          ],
          [
            9.069,
            -0.12
          ],
          [
            13.948,
            -0.91
          ],
          [
            19.787,
            -3.858
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        ]
      },
      "question_id": "waymo_val_f0236dc2f2fb6612f8d6abda39369974_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate A is the best choice because it safely navigates around the foreign object debris by maintaining a consistent path with increasing lateral clearance, avoiding any abrupt maneuvers or excessive deviation. In contrast, Candidate B stops too early and remains too close to the hazard, while Candidate C overcorrects with a large lateral shift and excessive speed, increasing risk.",
      "bundle_frame_names": [
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        "f0236dc2f2fb6612f8d6abda39369974-132",
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        "f0236dc2f2fb6612f8d6abda39369974-142",
        "f0236dc2f2fb6612f8d6abda39369974-147"
      ],
      "ground_truth_text": "Candidate A is the best choice because it safely navigates around the foreign object debris by maintaining a consistent path with increasing lateral clearance, avoiding any abrupt maneuvers or excessive deviation. In contrast, Candidate B stops too early and remains too close to the hazard, while Candidate C overcorrects with a large lateral shift and excessive speed, increasing risk.",
      "frame_name": "f0236dc2f2fb6612f8d6abda39369974-147",
      "anchor_image_path": "f0236dc2f2fb6612f8d6abda39369974/CAM_FRONT/f0236dc2f2fb6612f8d6abda39369974-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f0236dc2f2fb6612f8d6abda39369974",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "f0236dc2f2fb6612f8d6abda39369974:group_147",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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        "B": [
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            456.13,
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            414.95,
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            347.61,
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        "C": [
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            490.33,
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          [
            504.95,
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          [
            568.37,
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          [
            721.69,
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        ]
      }
    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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        "B": [
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            9.757,
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          [
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          [
            87.732,
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          [
            115.535,
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        "C": [
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            9.733,
            -0.101
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          [
            37.179,
            -1.972
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          [
            64.682,
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          [
            86.694,
            -10.568
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          [
            114.063,
            -18.306
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      },
      "question_id": "waymo_val_f1dd0e328596edd3c4cd977a0d09aef5_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate B is the best choice because it maintains a smooth, consistent trajectory with a slightly longer path that safely navigates the lane change while keeping lateral movement within a controlled range. Compared to A, it provides a more gradual and stable lateral progression, and unlike C, it avoids excessive lateral deviation while still achieving a safe and efficient maneuver.",
      "bundle_frame_names": [
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        "f1dd0e328596edd3c4cd977a0d09aef5-133",
        "f1dd0e328596edd3c4cd977a0d09aef5-138",
        "f1dd0e328596edd3c4cd977a0d09aef5-143",
        "f1dd0e328596edd3c4cd977a0d09aef5-148"
      ],
      "ground_truth_text": "Candidate B is the best choice because it maintains a smooth, consistent trajectory with a slightly longer path that safely navigates the lane change while keeping lateral movement within a controlled range. Compared to A, it provides a more gradual and stable lateral progression, and unlike C, it avoids excessive lateral deviation while still achieving a safe and efficient maneuver.",
      "frame_name": "f1dd0e328596edd3c4cd977a0d09aef5-148",
      "anchor_image_path": "f1dd0e328596edd3c4cd977a0d09aef5/CAM_FRONT/f1dd0e328596edd3c4cd977a0d09aef5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f1dd0e328596edd3c4cd977a0d09aef5",
      "scenario_cluster": "Multi-Lane Maneuvers",
      "bundle_id": "f1dd0e328596edd3c4cd977a0d09aef5:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
        "A": [
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          [
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          [
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          [
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          [
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          [
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        "C": [
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          [
            555.71,
            772.8
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          [
            598.23,
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          [
            631.86,
            740.42
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          [
            674.73,
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        ]
      }
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    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "f8af7b57e4867a282dd7c52043566c1d/CAM_FRONT/f8af7b57e4867a282dd7c52043566c1d-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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        "C": [
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          [
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            -1.827
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          [
            17.218,
            -3.59
          ],
          [
            25.421,
            -5.315
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          [
            37.032,
            -7.861
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        ]
      },
      "question_id": "waymo_val_f8af7b57e4867a282dd7c52043566c1d_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it maintains a smooth, consistent trajectory with increasing step size, indicating stable acceleration and a clear path away from the foreign object debris. Unlike Candidate A, which shows a sharp lateral deviation and high lateral displacement, and Candidate B, which terminates prematurely with a short path and low speed, Candidate C provides a longer, more controlled escape route with minimal lateral deviation, ensuring safe and efficient clearance.",
      "bundle_frame_names": [
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        "f8af7b57e4867a282dd7c52043566c1d-133",
        "f8af7b57e4867a282dd7c52043566c1d-138",
        "f8af7b57e4867a282dd7c52043566c1d-143",
        "f8af7b57e4867a282dd7c52043566c1d-148"
      ],
      "ground_truth_text": "Candidate C is the best choice because it maintains a smooth, consistent trajectory with increasing step size, indicating stable acceleration and a clear path away from the foreign object debris. Unlike Candidate A, which shows a sharp lateral deviation and high lateral displacement, and Candidate B, which terminates prematurely with a short path and low speed, Candidate C provides a longer, more controlled escape route with minimal lateral deviation, ensuring safe and efficient clearance.",
      "frame_name": "f8af7b57e4867a282dd7c52043566c1d-148",
      "anchor_image_path": "f8af7b57e4867a282dd7c52043566c1d/CAM_FRONT/f8af7b57e4867a282dd7c52043566c1d-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "f8af7b57e4867a282dd7c52043566c1d",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "f8af7b57e4867a282dd7c52043566c1d:group_148",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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    {
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      "history_image_paths": [
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      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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      "question_id": "waymo_val_fb0ed944efebd34d756103188d59da85_group_147_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it maintains a smoother, more controlled path with the smallest lateral deviation and consistent step sizes, avoiding excessive lateral movement and abrupt changes in speed. Compared to A and B, it approaches the obstacle more directly while staying within a safer lateral boundary, minimizing risk in the presence of foreign object debris.",
      "bundle_frame_names": [
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        "fb0ed944efebd34d756103188d59da85-132",
        "fb0ed944efebd34d756103188d59da85-137",
        "fb0ed944efebd34d756103188d59da85-142",
        "fb0ed944efebd34d756103188d59da85-147"
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      "ground_truth_text": "Candidate C is the best choice because it maintains a smoother, more controlled path with the smallest lateral deviation and consistent step sizes, avoiding excessive lateral movement and abrupt changes in speed. Compared to A and B, it approaches the obstacle more directly while staying within a safer lateral boundary, minimizing risk in the presence of foreign object debris.",
      "frame_name": "fb0ed944efebd34d756103188d59da85-147",
      "anchor_image_path": "fb0ed944efebd34d756103188d59da85/CAM_FRONT/fb0ed944efebd34d756103188d59da85-147_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "fb0ed944efebd34d756103188d59da85",
      "scenario_cluster": "Foreign Object Debris",
      "bundle_id": "fb0ed944efebd34d756103188d59da85:group_147",
      "image_paths": [
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      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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        "B": [
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        "C": [
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          [
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    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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        ],
        "B": [
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          [
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          [
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          [
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          [
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        ],
        "C": [
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          [
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          [
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          [
            18.286,
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          [
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        ]
      },
      "question_id": "waymo_val_fd52a15b681dc3920df93665305231f5_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "At an intersection, the correct trajectory must safely navigate through the intersection while maintaining appropriate speed and path alignment. Candidate C is the best choice because it follows a smooth, continuous path through the intersection with a consistent forward progression and appropriate lateral clearance, unlike A which stops short and B which barely moves, both failing to complete the intended maneuver.",
      "bundle_frame_names": [
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        "fd52a15b681dc3920df93665305231f5-133",
        "fd52a15b681dc3920df93665305231f5-138",
        "fd52a15b681dc3920df93665305231f5-143",
        "fd52a15b681dc3920df93665305231f5-148"
      ],
      "ground_truth_text": "At an intersection, the correct trajectory must safely navigate through the intersection while maintaining appropriate speed and path alignment. Candidate C is the best choice because it follows a smooth, continuous path through the intersection with a consistent forward progression and appropriate lateral clearance, unlike A which stops short and B which barely moves, both failing to complete the intended maneuver.",
      "frame_name": "fd52a15b681dc3920df93665305231f5-148",
      "anchor_image_path": "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "fd52a15b681dc3920df93665305231f5",
      "scenario_cluster": "Intersection",
      "bundle_id": "fd52a15b681dc3920df93665305231f5:group_148",
      "image_paths": [
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      "image_path": "fd52a15b681dc3920df93665305231f5/CAM_FRONT/fd52a15b681dc3920df93665305231f5-148_FRONT.jpg",
      "scoring_type": "open_oeq_text",
      "candidate_trajectories_image": {
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          [
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        "B": [
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        ],
        "C": [
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          [
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    },
    {
      "group_id": "group_148",
      "frame_index": 148,
      "history_image_paths": [
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        "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-148_FRONT.jpg"
      ],
      "question": "Explain why the selected trajectory is the best driving choice in this scene.",
      "source_dataset": "waymo_validation",
      "bundle_size": 5,
      "question_format": "OEQ",
      "candidate_trajectories": {
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          [
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          [
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          [
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          [
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        ],
        "B": [
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            -0.14
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          [
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          [
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            -1.346
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          [
            18.668,
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          [
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        ],
        "C": [
          [
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          [
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          [
            21.329,
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          [
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          [
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        ]
      },
      "question_id": "waymo_val_fd7cce36d47f456b4811d3ca4807517e_group_148_TRJ-9",
      "id": "TRJ-9",
      "ground_truth": "Candidate C is the best choice because it follows a smooth, continuous path through the intersection with consistent forward progression and minimal lateral deviation, maintaining a natural trajectory that aligns with standard intersection navigation. In contrast, Candidate A extends too far beyond the intersection with a longer path and higher lateral offset, while Candidate B terminates prematurely and shows erratic step sizes, indicating an incomplete or abrupt maneuver.",
      "bundle_frame_names": [
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        "fd7cce36d47f456b4811d3ca4807517e-133",
        "fd7cce36d47f456b4811d3ca4807517e-138",
        "fd7cce36d47f456b4811d3ca4807517e-143",
        "fd7cce36d47f456b4811d3ca4807517e-148"
      ],
      "ground_truth_text": "Candidate C is the best choice because it follows a smooth, continuous path through the intersection with consistent forward progression and minimal lateral deviation, maintaining a natural trajectory that aligns with standard intersection navigation. In contrast, Candidate A extends too far beyond the intersection with a longer path and higher lateral offset, while Candidate B terminates prematurely and shows erratic step sizes, indicating an incomplete or abrupt maneuver.",
      "frame_name": "fd7cce36d47f456b4811d3ca4807517e-148",
      "anchor_image_path": "fd7cce36d47f456b4811d3ca4807517e/CAM_FRONT/fd7cce36d47f456b4811d3ca4807517e-148_FRONT.jpg",
      "task": "trajectory-prediction",
      "scene_id": "fd7cce36d47f456b4811d3ca4807517e",
      "scenario_cluster": "Intersection",
      "bundle_id": "fd7cce36d47f456b4811d3ca4807517e:group_148",
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