{ "meta": { "benchmark": "STRIDE", "split": "nuscenes", "source_dataset": "nuScenes", "n_questions": 1150, "n_templates": 23, "n_scenes": 761, "counts_by_template": { "SC-6": 50, "SP-1": 50, "SP-2": 50, "SP-3": 50, "SP-5": 50, "SP-7": 50, "SU-1": 50, "SU-2": 50, "SU-4": 50, "SU-7": 50, "TE-1": 50, "TE-6": 50, "TM-2": 50, "TM-3": 50, "TM-4": 50, "TM-5": 50, "TRJ-1": 50, "TRJ-2": 50, "TRJ-3": 50, "TRJ-4": 50, "TRJ-5": 50, "TRJ-6": 50, "TRJ-7": 50 }, "counts_by_task_family": { "scene-context": 50, "space-perception": 250, "space-understanding": 200, "time-extrapolation": 100, "time-memory": 200, "trajectory-prediction": 350 }, "counts_by_format": { "OEQ": 350, "MCQ": 800 }, "notes": [ "Ground-truth answers included for open evaluation.", "Images are NOT bundled; resolve via formatted_scenes using source_group_file.", "Open-OEQ ground truths include human-verified revisions." ], "packaged_at": "2026-09-28T23:42:03.998571+00:00" }, "tasks": [ { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_299/group_001_vehicle_annotations.json", "scene_description": "Cross bridge, peds, ban, parked bicycle", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_299", "nuscenes_scene_name": "scene-0299", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under partly cloudy conditions with adequate natural lighting. It is set in a straight urban road environment with light to moderate traffic. The road features double yellow lines and crosswalks. Notable surrounding activity includes parked vehicles, industrial buildings (including a 'New Boston Seafood' facility), and some pedestrian activity near sidewalks. There are no visible bridges, barriers, or roundabouts. Traffic flow is steady but not dense.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_378/group_001_vehicle_annotations.json", "scene_description": "Construction zone, excavator, workers, nature, parked buses, ped", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_378", "nuscenes_scene_name": "scene-0378", "nuscenes_log_location": "singapore-onenorth", "ground_truth": "The scene takes place during daytime under overcast/cloudy conditions with diffused natural lighting. It is set in an urban environment with moderate traffic on a multi-lane road and adjacent parking lots. The baseline scene risk is moderate, and notable surrounding activity includes pedestrians crossing the street, vehicles navigating intersections, and parked cars in lots; no construction, excavators, or buses are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_379/group_001_vehicle_annotations.json", "scene_description": "Parked cars, tricycle, narrow road, overtake, sitting peds, passed by electric vehicle", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_379", "nuscenes_scene_name": "scene-0379", "nuscenes_log_location": "singapore-onenorth", "ground_truth": "The scene takes place during daytime under overcast/cloudy conditions with diffused natural lighting. It is set in a multi-lane urban road environment with moderate traffic and numerous parked cars along both sides. The baseline scene risk is moderate. Notable surrounding activity includes pedestrians walking on the sidewalk, vehicles parked in large lots, and a mix of cars and trucks in motion. There are no tricycles, electric vehicles, bicycles, scooters, or overtaking maneuvers visible in the frames.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_388/group_001_vehicle_annotations.json", "scene_description": "Cross intersection, bridge, arrive at intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_388", "nuscenes_scene_name": "scene-0388", "nuscenes_log_location": "boston-seaport", "ground_truth": "Urban downtown street scene in daytime under cloudy/overcast skies with diffused lighting. The road is a paved city street approaching a signalized intersection with crosswalk markings and some construction-related lane/curb restrictions. Surroundings include brick institutional/commercial buildings, a fenced construction site with equipment, cones, and barriers. Traffic is moderate with cars, a bus, a UPS truck, and a Coca-Cola truck moving or stopped nearby; a few pedestrians are present near the intersection.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_389/group_001_vehicle_annotations.json", "scene_description": "Go straight, parked cars, dense traffic, bicycle rack, truck with empty trailer", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_389", "nuscenes_scene_name": "scene-0389", "nuscenes_log_location": "boston-seaport", "ground_truth": "Urban city intersection in daytime under overcast/cloudy skies with diffuse lighting. The road is a paved multi-lane street with marked crosswalks and traffic lights. Surroundings include brick commercial/office buildings, a construction-fenced sidewalk area, and a UPS truck nearby. There is moderate traffic with cars and vans passing through, plus pedestrians crossing the street.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_398/group_001_vehicle_annotations.json", "scene_description": "Very dense traffic, congestion, overtake car, peds", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_398", "nuscenes_scene_name": "scene-0398", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under overcast/cloudy conditions with diffused natural lighting. It is set in a straight urban road environment with moderate traffic flow. The baseline scene risk is low to moderate. Notable surrounding activity includes steady multi-vehicle traffic, parked cars along the sides, and occasional pedestrians near crosswalks and sidewalks. No overtaking maneuvers or congestion are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_399/group_001_vehicle_annotations.json", "scene_description": "Wait at highway-like intersection, many cars, motorcycle, trash cans", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_399", "nuscenes_scene_name": "scene-0399", "nuscenes_log_location": "singapore-hollandvillage", "ground_truth": "Urban downtown environment on overcast daylight conditions, with muted lighting and dry pavement. The vehicle travels on multi-lane city streets through intersections with traffic lights, crosswalks, lane markings, and curbside parking. Surroundings include tall office/industrial buildings, sidewalks, loading/garage entrances, parked cars, a few moving vehicles including trucks and a USPS vehicle, with light pedestrian activity and generally light traffic.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_459/group_001_vehicle_annotations.json", "scene_description": "Rain, parking lot, crane", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_459", "nuscenes_scene_name": "scene-0459", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under clear, dry weather conditions with adequate natural lighting. It is set in an urban street environment with moderate traffic flow. The road surface is dry asphalt, and the surrounding area includes modern buildings, trees, traffic signals, and a bus stop. Notable surrounding activity includes steady multi-vehicle traffic, parked vehicles along the curb, and pedestrian infrastructure such as sidewalks and crosswalks. There is no visible rain, wet surfaces, or construction (e.g., crane).", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_469/group_001_vehicle_annotations.json", "scene_description": "Rain, truck, cross intersection, ped, parked cars, bicyclist, sign trailer, bicycle rack", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_469", "nuscenes_scene_name": "scene-0469", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under clear, sunny weather with dry road surfaces. It is set in an urban intersection environment with light to moderate traffic. The baseline scene risk is low to moderate. Notable surrounding activity includes multiple vehicles (cars, trucks, vans), pedestrians on sidewalks, bicycle racks, traffic signals, and commercial buildings. There is no rain or wet pavement visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_477/group_001_vehicle_annotations.json", "scene_description": "Rain, bridge, peds crossing, peds sidewalk stroller, wet ground", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_477", "nuscenes_scene_name": "scene-0477", "nuscenes_log_location": "boston-seaport", "ground_truth": "Rainy, overcast daytime scene with wet pavement and reduced visibility from raindrops on the cameras. The vehicle is driving on a low-speed urban access road through a large parking/garage area, with crosswalks, parking gates/barriers, signs, curbs, and landscaped medians. Surroundings include multi-story buildings, lots of parked cars, a few moving vehicles, and occasional pedestrians near the sidewalks.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_478/group_001_vehicle_annotations.json", "scene_description": "Rain, waiting traffic light, oncoming bicyclist, bike rack, peds, reflections", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_478", "nuscenes_scene_name": "scene-0478", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during overcast daytime with heavy rain, resulting in wet road surfaces and reduced visibility. It is set in an industrial/commercial area with wide, mostly empty roads, parking lots, and large buildings. Notable surrounding activity includes parked and slowly moving vehicles, including trucks and vans, and no visible pedestrians, cyclists, or traffic signals. The environment appears quiet with minimal traffic flow, and there are no roundabouts, bike racks, or waiting traffic lights visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_479/group_001_vehicle_annotations.json", "scene_description": "Rain, many peds, jaywalker, bicycle rack, parked cars", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_479", "nuscenes_scene_name": "scene-0479", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under overcast, rainy conditions with wet road surfaces. It is set in an urban/suburban environment with a straight road and moderate traffic flow. Notable surrounding activity includes construction equipment (excavators) visible in the distance, parked vehicles along the roadside, and no visible pedestrians or bicycle racks. The overall environment appears calm with no immediate safety hazards.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_568/group_001_vehicle_annotations.json", "scene_description": "Rain, parked cars, parked truck on the lane", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_568", "nuscenes_scene_name": "scene-0568", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under rainy conditions, with wet roads and overcast lighting. It is set in an urban environment with multi-lane roads, intersections, and moderate traffic flow. Notable surrounding activity includes parked cars, moving vehicles, pedestrians, and visible storefronts. There is no parked truck on the lane; the road is clear of obstructions. The baseline scene risk is moderate due to wet conditions and traffic density.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_575/group_001_vehicle_annotations.json", "scene_description": "Rain, parked cars, cones, construction workers", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_575", "nuscenes_scene_name": "scene-0575", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during overcast daytime with wet road surfaces due to recent or ongoing rain. It is an urban environment with moderate traffic flow, featuring multi-lane roads, crosswalks, and buildings with commercial storefronts. Notable surrounding activity includes pedestrians with umbrellas, vehicles navigating intersections, and a white delivery van with 'MacCo' branding. There is no visible construction zone, construction workers, cones, or barriers. The overall risk level is moderate due to wet conditions and traffic density.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_576/group_001_vehicle_annotations.json", "scene_description": "Rain, construction, ped on road, turn right", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_576", "nuscenes_scene_name": "scene-0576", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during overcast daytime with wet road surfaces due to recent or ongoing rain. It is set in an urban environment with active construction zones, including scaffolding and barriers along the roadside. Traffic is moderate, with vehicles navigating intersections and construction detours marked by orange cones. Notable surrounding activity includes construction work, wet road conditions, and steady multi-vehicle traffic flow. No pedestrians are visible on the road.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_577/group_001_vehicle_annotations.json", "scene_description": "Rain, turn right, bicycle rack, cross intersection, parking lot", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_577", "nuscenes_scene_name": "scene-0577", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under rainy conditions with wet road surfaces. It is set in an urban city street environment with moderate traffic, featuring multi-lane roads, construction sites with scaffolding, traffic signals, and vehicles navigating intersections. Notable surrounding activity includes ongoing construction, vehicles turning at intersections, and wet pavement reflecting city lights.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_578/group_001_vehicle_annotations.json", "scene_description": "Rain, cross intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_578", "nuscenes_scene_name": "scene-0578", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during overcast daytime with heavy rain, resulting in wet road surfaces and reduced visibility. It is set in an urban city street environment with moderate traffic, including cars, trucks, and construction vehicles. Notable surrounding activity includes ongoing construction with barriers and cranes, wet pavement, and active traffic flow. The baseline scene risk is high due to poor visibility and wet conditions.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_584/group_001_vehicle_annotations.json", "scene_description": "Rain, turn right, exiting car, parking lot", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_584", "nuscenes_scene_name": "scene-0584", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under overcast, rainy conditions with wet road surfaces. It is set in an urban city street environment with multi-lane roads, visible traffic flow, and surrounding buildings including commercial structures and construction sites. Notable surrounding activity includes steady vehicle traffic, wet pavement reflecting light, and pedestrians near storefronts. The baseline scene risk is moderate due to wet conditions and urban traffic.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_586/group_001_vehicle_annotations.json", "scene_description": "Rain, construction, parked car, close to intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_586", "nuscenes_scene_name": "scene-0586", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under overcast, rainy conditions with wet road surfaces. It is set in an urban environment with moderate traffic flow. The baseline scene risk is moderate. Notable surrounding activity includes rain, wet roads, vehicles navigating an intersection, and parked cars along the roadside. There is no visible construction zone or construction-related roadside activity.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_587/group_001_vehicle_annotations.json", "scene_description": "Rain, wait at intersection, truck", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_587", "nuscenes_scene_name": "scene-0587", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime (adequate natural lighting) under rain / wet road surface conditions. It is set in a urban intersection / crossroad environment with moderate traffic. The baseline scene risk is moderate risk, and notable surrounding activity includes rain, wait at intersection, truck; intersection-related interactions; steady multi-vehicle traffic flow.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_588/group_001_vehicle_annotations.json", "scene_description": "Rain, turn right, parking lot", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_588", "nuscenes_scene_name": "scene-0588", "nuscenes_log_location": "boston-seaport", "ground_truth": "Rainy, overcast daytime in a dense downtown urban area. The road is a wet multi-lane city street with intersections, crosswalks, traffic lights, and reflective pavement. Visibility is slightly reduced by rain/water on the cameras. Surroundings include tall office buildings, sidewalks, street trees, parked and moving cars, and a USPS delivery truck nearby, with light to moderate traffic and occasional pedestrians/cyclists.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_595/group_001_vehicle_annotations.json", "scene_description": "Rain, wait at intersection, peds, construction, ship, ped with bag", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_595", "nuscenes_scene_name": "scene-0595", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during overcast daytime with wet road surfaces due to recent or ongoing rain. It is set in an urban environment with moderate traffic, featuring brick buildings, parked vehicles, and active construction zones with fencing and orange traffic barrels. Notable surrounding activity includes steady multi-vehicle traffic flow, construction work, and wet pavement conditions. No pedestrians, ships, or bags are visible in the frames.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_597/group_001_vehicle_annotations.json", "scene_description": "Rain, parked cars, peds and children, parked truck", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_597", "nuscenes_scene_name": "scene-0597", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under rainy conditions, with wet road surfaces and overcast lighting. It is set in a straight urban road environment with moderate traffic flow. Notable surrounding activity includes pedestrians crossing the street, parked cars along the curbs, and a FedEx truck and other vehicles in motion. There are no children visible, and the traffic is not dense.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_598/group_001_vehicle_annotations.json", "scene_description": "Rain, construction, turn right, roundabout", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_598", "nuscenes_scene_name": "scene-0598", "nuscenes_log_location": "boston-seaport", "ground_truth": "Urban downtown street scene in rainy, overcast daytime conditions. The road is wet and reflective, with marked lanes, crosswalks, traffic lights, and intersections. Surroundings include mid-rise brick/commercial buildings, storefronts, scaffolding/construction areas, parked cars, moving vehicles including a FedEx truck, and a few pedestrians on sidewalks/crosswalks. Visibility is slightly reduced by rain/water on the cameras.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_648/group_001_vehicle_annotations.json", "scene_description": "Rain, cross intersection, reflections, bicycle rack, bus", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_648", "nuscenes_scene_name": "scene-0648", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under clear, sunny weather with dry road surfaces. It is set in an urban environment featuring a multi-lane road with construction zones marked by orange traffic cones and barriers. Notable surrounding activity includes construction work, delivery trucks, pedestrians, and moderate vehicular traffic. The road includes intersections and turn lanes, with modern office buildings and greenery lining the streets.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_649/group_001_vehicle_annotations.json", "scene_description": "Rain, parked scooter, parked bicycle, wait at intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_649", "nuscenes_scene_name": "scene-0649", "nuscenes_log_location": "boston-seaport", "ground_truth": "Sunny daytime urban street, likely in a campus or business district, with bright natural lighting and strong tree shadows. The vehicle is driving on a paved multi-lane city road lined with sidewalks, fences, large trees, and institutional/commercial buildings. Traffic is light, with a few parked and moving cars nearby. Several pedestrians are walking along the sidewalks, and there are occasional scooters/motorcycles and street-side activity, but the scene is generally calm.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_658/group_001_vehicle_annotations.json", "scene_description": "Wait at intersection, peds crossing crosswalk, scooter, turn left, heavy truck, bus stop", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_658", "nuscenes_scene_name": "scene-0658", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under overcast or hazy conditions with diffused lighting. It is set in a modern urban street environment with low to moderate traffic, featuring wide roads with double yellow lines, covered walkways, and modern high-rise buildings. Notable surrounding activity includes pedestrians walking on sidewalks, a few vehicles moving slowly or stopped, and no visible crosswalks or heavy traffic. There are no buses, heavy trucks, scooters, or active intersections with turning vehicles. The environment appears calm with minimal pedestrian-vehicle interaction.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_659/group_001_vehicle_annotations.json", "scene_description": "Peds, parking lot, large truck, scooter", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_659", "nuscenes_scene_name": "scene-0659", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under partly cloudy conditions with adequate natural lighting. It is set in an urban street environment with light traffic. The baseline scene risk is low. Notable surrounding activity includes pedestrians, parked vehicles, and occasional moving cars; the road features multiple lanes, traffic signs, and modern buildings with greenery along the sidewalks.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_667/group_001_vehicle_annotations.json", "scene_description": "Bendy bus, peds having a picnic, overtake truck, parked car", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_667", "nuscenes_scene_name": "scene-0667", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under partly cloudy conditions with adequate natural lighting. It is set in a multi-lane urban/suburban road environment with moderate traffic flow. The road includes a roundabout or intersection with visible lane markings and traffic signs. Notable surrounding activity includes vehicles navigating the intersection, including a white van, a black sedan, and a red sedan, with no pedestrians or unusual events visible. The environment features trees, buildings, and roadside greenery.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_668/group_001_vehicle_annotations.json", "scene_description": "Parked bus, crane, construction", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_668", "nuscenes_scene_name": "scene-0668", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under partly cloudy conditions with adequate natural lighting. It is set in a residential suburban area with quiet, low-traffic roads. The baseline scene risk is low. Notable surrounding activity includes parked vehicles, residential houses, trees, and occasional passing cars. No construction zones, cranes, or buses are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_669/group_001_vehicle_annotations.json", "scene_description": "Turn right, dense traffic, ped sitting at bus stop, parking lot", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_669", "nuscenes_scene_name": "scene-0669", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under clear/sunny conditions. It is set in a quiet residential street with single-lane traffic flow and minimal vehicle activity. The road is paved and lined with trees and single-family homes. Notable surrounding activity includes parked vehicles, occasional passing cars, and no visible pedestrians or traffic congestion.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_695/group_001_vehicle_annotations.json", "scene_description": "Parked cars, peds, bicycle racks, cones, through intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_695", "nuscenes_scene_name": "scene-0695", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under clear/sunny conditions with bright natural lighting. It is set in a suburban or semi-urban intersection with light to moderate traffic. The road is a multi-lane asphalt intersection with visible crosswalks and traffic signals. Notable surrounding activity includes a few vehicles (including a white van and a silver van) moving through the intersection, a small roadside food stall with a covered seating area, trees lining the road, and a sidewalk with a traffic light pole. There are no visible pedestrians, bicycle racks, cones, or parked cars in the immediate vicinity. The overall environment appears calm with low to moderate risk.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_696/group_001_vehicle_annotations.json", "scene_description": "Cones, parked cars, ped crossing crosswalk", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_696", "nuscenes_scene_name": "scene-0696", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under clear/sunny conditions with adequate natural lighting. It is set in a suburban or urban road environment with moderate traffic flow, featuring multi-lane roads, trees lining the sides, and occasional parked vehicles. Notable surrounding activity includes vehicles navigating intersections, a white van in the foreground, and a bus visible in the distance. There are no visible construction zones, cones, or pedestrian crossings in the provided frames. The road type is a standard asphalt road with lane markings, and the overall risk level is low to moderate due to normal traffic conditions.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_697/group_001_vehicle_annotations.json", "scene_description": "Ped crossing road, parked cars, ped on sidewalk, pole, bench, trailer, cross intersection, parked bus, parking lot", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_697", "nuscenes_scene_name": "scene-0697", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during daytime under clear/sunny conditions with adequate natural lighting. It is set on a multi-lane urban road with light to moderate traffic flow. Notable surrounding activity includes pedestrians at a bus stop, parked vehicles along the roadside, trees lining the road, and a bus and cars in motion. The environment features green metal barriers, sidewalks, and buildings in the background. There is no parking lot, trailer, or bicycle/scooter activity visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_747/group_001_vehicle_annotations.json", "scene_description": "Wait at intersection, peds crossing crosswalk, parking lot", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_747", "nuscenes_scene_name": "scene-0747", "nuscenes_log_location": "boston-seaport", "ground_truth": "Overcast daytime scene on a curving two-way urban/campus access road with marked lanes, zebra crossings, sidewalks, signs, and landscaped greenery. Lighting is diffuse with likely light rain or recent rain, as some pedestrians carry umbrellas. The area is bordered by modern institutional/commercial buildings, trees, hedges, and pedestrian paths. Traffic is light, with a few cars and a scooter/motorbike, and several pedestrians crossing or walking near the road.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_749/group_001_vehicle_annotations.json", "scene_description": "Wait at intersection, parked large trucks, ped with pet, heavy truck turning close to ego vehicle, avoidance maneuver", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_749", "nuscenes_scene_name": "scene-0749", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during overcast daytime with diffused natural lighting. It is set in a suburban campus or institutional environment with a single-lane road, bordered by grassy slopes, shrubbery, and modern buildings. Traffic is light to absent, with no visible heavy trucks or intersection-related interactions. Notable surrounding activity includes pedestrians walking near the road and a bus visible in the distance. The overall environment appears calm and low-risk.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_758/group_001_vehicle_annotations.json", "scene_description": "Ped crossing, construction vehicle, UPS truck, wait at intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_758", "nuscenes_scene_name": "scene-0758", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during twilight or early night under low-light conditions with artificial lighting. It is set on a multi-lane urban road with moderate traffic. The baseline scene risk is low to moderate, and notable surrounding activity includes pedestrian crossings, steady multi-vehicle traffic flow, and roadside trees and buildings. No construction zone or construction vehicles are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_759/group_001_vehicle_annotations.json", "scene_description": "Wait at intersection, ped crossing crosswalk", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_759", "nuscenes_scene_name": "scene-0759", "nuscenes_log_location": "boston-seaport", "ground_truth": "An urban, tree-lined roadway at dusk/night under cloudy skies, with low ambient light and vehicle headlights/streetlights providing most illumination. The road is paved, multi-lane and curving/divided in places, with lane arrows, curbs, medians, railings and a roundabout/turning section. Surroundings include lit buildings, sidewalks, parked cars, and occasional pedestrians, with light to moderate traffic moving through the area.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_767/group_001_vehicle_annotations.json", "scene_description": "Parked cars, cones, trailer, peds, children, car trunk being loaded", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_767", "nuscenes_scene_name": "scene-0767", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during nighttime under clear conditions with artificial lighting. It is set in a multi-lane urban road environment with light to moderate traffic. The baseline scene risk is low to moderate. Notable surrounding activity includes parked cars, illuminated buildings, and occasional moving vehicles; no pedestrians, children, or construction activity are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_768/group_001_vehicle_annotations.json", "scene_description": "Parked cars, truck overtaking us, peds on sidewalk, bus, turck, construction vehicle, bicycle rack, bicycle, truck", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_768", "nuscenes_scene_name": "scene-0768", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during nighttime under clear conditions with artificial lighting. It is set on a relatively quiet, two-lane road with minimal traffic. The baseline scene risk is low. Notable surrounding activity includes parked cars along the roadside, illuminated buildings, and sparse vehicle movement ahead. No construction, pedestrians, or heavy traffic are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_769/group_001_vehicle_annotations.json", "scene_description": "Truck, parked cars, ped crossing crosswalk, turn right, parking lot", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_769", "nuscenes_scene_name": "scene-0769", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place during nighttime under low-light conditions with artificial street lighting. It is set on a two-lane residential road with sparse traffic. The baseline scene risk is low, and notable surrounding activity includes occasional oncoming vehicles, streetlights, trees, and houses along the roadside.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_775/group_001_vehicle_annotations.json", "scene_description": "Stationary ego vehicle, parking lot, peds, truck, cars pass by", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_775", "nuscenes_scene_name": "scene-0775", "nuscenes_log_location": "boston-seaport", "ground_truth": "Nighttime urban/residential street scene in clear, dry weather. The road is a lit two-lane city street with lane markings, zebra crossings, and intersections, bordered by sidewalks, hedges, apartment/campus-like buildings, and bus stops. Visibility is mainly from streetlights, headlights, and building lights. There is moderate surrounding activity, including pedestrians waiting/walking near bus stops, oncoming cars, parked vehicles, and a passing bus.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_777/group_001_vehicle_annotations.json", "scene_description": "Stationary ego vehicle, parking lot, person getting inside ego vehicle", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_777", "nuscenes_scene_name": "scene-0777", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place at night under artificial lighting conditions. It is set on a multi-lane urban road with moderate traffic flow. The baseline scene risk is moderate, and notable surrounding activity includes moving vehicles, streetlights, traffic signals, and roadside vegetation. The weather appears clear.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_778/group_001_vehicle_annotations.json", "scene_description": "Parking lot, U turn, parked cars, peds", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_778", "nuscenes_scene_name": "scene-0778", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place at night under artificial lighting conditions. It is set on a multi-lane urban road with crosswalks and sidewalks, surrounded by buildings, trees, and streetlights. Notable surrounding activity includes pedestrians walking on sidewalks, vehicles driving on the road, and a bus stop with people waiting. The environment appears to be a city or town center with moderate traffic flow.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_786/group_001_vehicle_annotations.json", "scene_description": "Wait at intersection, construction workers, worker digging, start at intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_786", "nuscenes_scene_name": "scene-0786", "nuscenes_log_location": "singapore-queenstown", "ground_truth": "The scene takes place at night under artificial lighting with wet road conditions, indicating recent rain. It is a multi-lane urban roadway with moderate to heavy traffic, including cars, trucks, and motorcycles. Notable surrounding activity includes vehicles waiting at an intersection, vehicles merging or changing lanes, and a motorcycle riding in the center lane. There is no visible construction zone, construction workers, or pedestrians near the roadway.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_787/group_001_vehicle_annotations.json", "scene_description": "Parked bicycles, bus stop, peds at bus stop, wait at intersection", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_787", "nuscenes_scene_name": "scene-0787", "nuscenes_log_location": "singapore-queenstown", "ground_truth": "The scene takes place at night under artificial lighting with wet road conditions, indicating recent rain. It is set on a multi-lane urban highway or expressway with moderate to heavy traffic. The baseline scene risk is high due to wet roads and nighttime visibility. Notable surrounding activity includes vehicles traveling in multiple lanes, a large truck on the left, and a motorcycle in the lower central view. There are no visible pedestrians, bicycles, or bus stops.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_794/group_001_vehicle_annotations.json", "scene_description": "Wait at intersection, many vehicles, motorcycles, light turns green", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_794", "nuscenes_scene_name": "scene-0794", "nuscenes_log_location": "singapore-queenstown", "ground_truth": "The scene takes place at night under low-light conditions with artificial street lighting. It is set on a multi-lane urban road with moderate traffic flow. The baseline scene risk is low to moderate, and notable surrounding activity includes vehicles traveling in both directions, some parked cars along the roadside, and occasional streetlights illuminating the road. The weather appears clear with no precipitation.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_795/group_001_vehicle_annotations.json", "scene_description": "Motorcycle, cars, trucks, bus, cross intersection, stopped bus, cones, peds on sidewalk", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_795", "nuscenes_scene_name": "scene-0795", "nuscenes_log_location": "singapore-queenstown", "ground_truth": "The scene takes place at night under artificial lighting with clear, dry conditions. It is set on a relatively quiet urban road with minimal traffic. The baseline scene risk is low. Notable surrounding activity includes a few vehicles (cars) at a distance, streetlights, and some roadside structures; no pedestrians, motorcycles, buses, trucks, or construction cones are visible. The road appears to be a straight or slightly curved two-lane road with crosswalk markings in one frame.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_819/group_001_vehicle_annotations.json", "scene_description": "Rain, trucks, parking lot, wait at intersection, turn right", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_819", "nuscenes_scene_name": "scene-0819", "nuscenes_log_location": "boston-seaport", "ground_truth": "The scene takes place at night under clear, dry conditions with artificial street lighting. It is set on a multi-lane urban road or highway with moderate traffic flow. The baseline scene risk is low to moderate. Notable surrounding activity includes steady vehicle traffic, streetlights, roadside barriers, and occasional parked vehicles or construction signage.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Describe the overall environment of the scene, including weather, lighting conditions, road type, and notable surrounding activity.", "task": "scene-context", "source_group_file": "scene_849/group_001_vehicle_annotations.json", "scene_description": "Cars, cross intersection, peds on sidewalk, barriers, cars overtaking us", "id": "SC-6", "group_id": "group_001", "scene_id": "scene_849", "nuscenes_scene_name": "scene-0849", "nuscenes_log_location": "singapore-queenstown", "ground_truth": "The scene takes place at night under wet road conditions, indicated by reflections and glare from vehicle lights. Lighting is artificial from streetlights and vehicle headlights. The road is a multi-lane urban street with moderate traffic, including cars and a bus. Notable surrounding activity includes vehicles moving in both directions, a bus ahead, and a metal guardrail on the right side. No construction, pedestrians, or roundabouts are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": { "A": "Moving toward ego", "B": "Moving away from ego", "C": "Moving left-to-right across ego", "D": "Moving right-to-left across ego", "E": "Static", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "abd58e7c79ec435c912afbc2dd93b38e", "source_group_file": "scene_012/group_002_vehicle_annotations.json", "id": "SP-1", "group_id": "group_002", "object_reference": "the vehicle rear-left at about 38m", "scene_id": "scene_012", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving toward ego", "B": "Moving away from ego", "C": "Moving left-to-right across ego", "D": "Moving right-to-left across ego", "E": "Same direction as ego", "F": "Static" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "cac4e5f668e54ac5bac525bfe456c226", "source_group_file": "scene_022/group_001_vehicle_annotations.json", "scene_description": "", "id": "SP-1", "group_id": "group_001", "object_reference": "the vehicle behind at about 18m", "scene_id": "scene_022", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving away from ego", "B": "Moving left-to-right across ego", "C": "Moving toward ego", "D": "Moving right-to-left across ego", "E": "Static", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "9e9e9d9a28c24e72a8ee2ed3a8a4eb4b", "source_group_file": "scene_043/group_001_vehicle_annotations.json", "scene_description": "", "id": "SP-1", "group_id": "group_001", "object_reference": "the vehicle front-right at about 65m", "scene_id": "scene_043", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving left-to-right across ego", "B": "Moving toward ego", "C": "Moving away from ego", "D": "Moving right-to-left across ego", "E": "Static", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "ecdc7f0581bd4b57981de502cee51da0", "source_group_file": "scene_045/group_001_vehicle_annotations.json", "scene_description": "", "id": "SP-1", "group_id": "group_001", "object_reference": "the vehicle behind at about 10m", "scene_id": "scene_045", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving toward ego", "B": "Moving away from ego", "C": "Same direction as ego", "D": "Moving left-to-right across ego", "E": "Moving right-to-left across ego", "F": "Static" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "50b4893d0dfd44f5a288afd8da00ade7", "source_group_file": "scene_048/group_004_vehicle_annotations.json", "scene_description": "", "id": "SP-1", "group_id": "group_004", "object_reference": "the vehicle behind at about 18m", "scene_id": "scene_048", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving toward ego", "B": "Moving left-to-right across ego", "C": "Moving away from ego", "D": "Moving right-to-left across ego", "E": "Static", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "d5fe0be2b5904568ad12ea6ec7fa220d", "source_group_file": "scene_067/group_004_vehicle_annotations.json", "id": "SP-1", "group_id": "group_004", "object_reference": "the vehicle front-left at about 39m", "scene_id": "scene_067", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving toward ego", "B": "Moving away from ego", "C": "Moving left-to-right across ego", "D": "Static", "E": "Moving right-to-left across ego", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "ff9eb11e27a54db28cdcbf3d354ce412", "source_group_file": "scene_102/group_001_vehicle_annotations.json", "id": "SP-1", "group_id": "group_001", "object_reference": "the vehicle behind at about 15m", "scene_id": "scene_102", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving toward ego", "B": "Moving left-to-right across ego", "C": "Moving right-to-left across ego", "D": "Static", "E": "Moving away from ego", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "2fcf8f0ce20c43928c017cf7fabcf48d", "source_group_file": "scene_126/group_003_vehicle_annotations.json", "id": "SP-1", "group_id": "group_003", "object_reference": "the vehicle rear-left at about 26m", "scene_id": "scene_126", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving toward ego", "B": "Moving left-to-right across ego", "C": "Moving right-to-left across ego", "D": "Static", "E": "Moving away from ego", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "5e15b326dc5e48e6bedb5bed93fc7a31", "source_group_file": "scene_129/group_002_vehicle_annotations.json", "id": "SP-1", "group_id": "group_002", "object_reference": "the vehicle behind at about 24m", "scene_id": "scene_129", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving away from ego", "B": "Moving left-to-right across ego", "C": "Moving toward ego", "D": "Moving right-to-left across ego", "E": "Static", "F": "Same direction as ego" }, "question": "What is the trajectory direction of the object in the red bounding box in relation to the ego vehicle?", "task": "space-perception", "object_id": "bc86c16383bc4fc8ad278dedb474ca8f", "source_group_file": "scene_152/group_002_vehicle_annotations.json", "id": "SP-1", "group_id": "group_002", "object_reference": "the vehicle front-right at about 35m", "scene_id": "scene_152", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Moving toward ego", "B": "Moving away from ego", "C": "Static", "D": "Moving left-to-right across ego", "E": "Moving right-to-left across ego", "F": "Same direction as ego" }, "question": "What is the 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}, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "2b9c5d2de8f244cdb5db90ebb5e97e88", "source_group_file": "scene_436/group_003_vehicle_annotations.json", "id": "SP-5", "group_id": "group_003", "object_reference": "the vehicle front-right at about 35m", "scene_id": "scene_436", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially occluded (<50% occluded)", "B": "Mostly occluded (≥50% occluded)", "C": "Fully occluded but inferred", "D": "Fully visible" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "9ddcab0507cb47d0b9c9fb31d2cdc8d2", "source_group_file": "scene_440/group_004_vehicle_annotations.json", "id": "SP-5", "group_id": "group_004", "object_reference": "the vehicle rear-left at about 22m", "scene_id": "scene_440", "ground_truth": "D", "question_format": "MCQ", "scoring_type": 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occluded)", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "641fb819cebf44b3a31ece9d2b4eb5ac", "source_group_file": "scene_516/group_008_vehicle_annotations.json", "id": "SP-5", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 42m", "scene_id": "scene_516", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Fully visible", "B": "Partially occluded (<50% occluded)", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "39b06ac8fa6f488fba8c7fb2bb74dbb7", "source_group_file": "scene_517/group_001_vehicle_annotations.json", "id": "SP-5", "group_id": "group_001", "object_reference": "the vehicle front-left at about 60m", "scene_id": "scene_517", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Mostly occluded (≥50% occluded)", "B": "Fully visible", "C": "Partially occluded (<50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "ead9fb2e87e94eb48c2206ac4fd2f6bc", "source_group_file": "scene_527/group_007_vehicle_annotations.json", "id": "SP-5", "group_id": "group_007", "object_reference": "the vehicle rear-left at about 32m", "scene_id": "scene_527", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Fully visible", "B": "Partially occluded (<50% occluded)", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "a5a38dc15f964f68b2fb1f91cda95bdc", "source_group_file": "scene_556/group_002_vehicle_annotations.json", "id": "SP-5", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 49m", "scene_id": "scene_556", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Fully visible", "B": "Partially occluded (<50% occluded)", "C": "Fully occluded but inferred", "D": "Mostly occluded (≥50% occluded)" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "8126ca0d6eb64ffbabaab701794ef54d", "source_group_file": "scene_567/group_006_vehicle_annotations.json", "id": "SP-5", "group_id": "group_006", "object_reference": "the vehicle behind at about 125m", "scene_id": "scene_567", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Fully visible", "B": "Partially occluded (<50% occluded)", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "a976c6c8dda444c5b36f656fcabf4dca", "source_group_file": "scene_603/group_003_vehicle_annotations.json", "id": "SP-5", "group_id": "group_003", "object_reference": "the vehicle front-left at about 51m", "scene_id": "scene_603", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially occluded (<50% occluded)", "B": "Fully visible", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "bc358be987b849babfce81a5e91fc8e4", "source_group_file": "scene_612/group_004_vehicle_annotations.json", "id": "SP-5", "group_id": "group_004", "object_reference": "the vehicle front-right at about 40m", "scene_id": "scene_612", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially occluded (<50% occluded)", "B": "Fully visible", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "3baf4d096eaa4aec8dbf69187a3a8c6c", "source_group_file": "scene_623/group_002_vehicle_annotations.json", "id": "SP-5", "group_id": "group_002", "object_reference": "the vehicle rear-left at about 30m", "scene_id": "scene_623", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Fully visible", "B": "Partially occluded (<50% occluded)", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "56e2cab6b2ee41ffbf846e9206fbd86b", "source_group_file": "scene_634/group_002_vehicle_annotations.json", "id": "SP-5", "group_id": "group_002", "object_reference": "the vehicle front-right at about 55m", "scene_id": "scene_634", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Mostly occluded (≥50% occluded)", "B": "Fully visible", "C": "Partially occluded (<50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "b85bdeb7c8f44f12abed9091ffd6b67e", "source_group_file": "scene_711/group_001_vehicle_annotations.json", "id": "SP-5", "group_id": "group_001", "object_reference": "the vehicle rear-right at about 75m", "scene_id": "scene_711", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially occluded (<50% occluded)", "B": "Fully visible", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "df3eb856cd56475cbc02cfd2c07cba3c", "source_group_file": "scene_716/group_004_vehicle_annotations.json", "id": "SP-5", "group_id": "group_004", "object_reference": "the vehicle front-right at about 51m", "scene_id": "scene_716", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially occluded (<50% occluded)", "B": "Mostly occluded (≥50% occluded)", "C": "Fully visible", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "c48f2e0ae0f24d91b57b2fc244affaec", "source_group_file": "scene_717/group_008_vehicle_annotations.json", "id": "SP-5", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 97m", "scene_id": "scene_717", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Fully visible", "B": "Mostly occluded (≥50% occluded)", "C": "Partially occluded (<50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "09f8fb2c292a4c8e8e9bf3c1bfa770ce", "source_group_file": "scene_751/group_006_vehicle_annotations.json", "id": "SP-5", "group_id": "group_006", "object_reference": "the vehicle rear-right at about 24m", "scene_id": "scene_751", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Fully visible", "B": "Partially occluded (<50% occluded)", "C": "Mostly occluded (≥50% occluded)", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "556096ae1dbd4fa1b5d0ed7caf967dcb", "source_group_file": "scene_807/group_005_vehicle_annotations.json", "id": "SP-5", "group_id": "group_005", "object_reference": "the vehicle rear-left at about 42m", "scene_id": "scene_807", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially occluded (<50% occluded)", "B": "Mostly occluded (≥50% occluded)", "C": "Fully visible", "D": "Fully occluded but inferred" }, "question": "What is the occlusion state of the object in the red bounding box?", "task": "space-perception", "object_id": "d8276dfe7fbc48f1b1e3c6ef0cc283ad", "source_group_file": "scene_841/group_001_vehicle_annotations.json", "id": "SP-5", "group_id": "group_001", "object_reference": "the vehicle front-left at about 55m", "scene_id": "scene_841", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "ea4d1a3a9f314ac284fa2e4a84a7a141", "source_group_file": "scene_009/group_002_vehicle_annotations.json", "id": "SP-7", "group_id": "group_002", "object_reference": "the vehicle rear-left at about 63m", "scene_id": "scene_009", "ground_truth": "The object in the red bounding box is a stationary white van or truck parked behind a fence, located to the left and slightly behind the ego vehicle, approximately 30-40 meters away. It is not on the road surface and is not moving. The object is partially occluded by trees and a fence, with about 50-70% of its body hidden from view.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "aa29af497eca4b6d8b9e918539426de3", "source_group_file": "scene_049/group_002_vehicle_annotations.json", "id": "SP-7", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 38m", "scene_id": "scene_049", "ground_truth": "There is no vehicle in the rear-right at about 38m. The red bounding box in the query frame highlights a small orange object near the building's entrance, likely a traffic cone or barrier, not a vehicle. It is stationary, located on the right side of the road near the curb, approximately 10–15 meters from the ego vehicle, and not in motion.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "af902bacba5f4eabbc44466215d64433", "source_group_file": "scene_075/group_005_vehicle_annotations.json", "id": "SP-7", "group_id": "group_005", "object_reference": "the vehicle behind at about 60m", "scene_id": "scene_075", "ground_truth": "The vehicle behind is located approximately 60 meters behind the ego vehicle, positioned in the left adjacent lane, slightly to the left of the lane center. It is fully visible on the road and moving away from the ego vehicle in the same direction, consistent with the rear view perspective. The distance is greater than 50 meters, and the vehicle is aligned with the roadway.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "24e8893ef7cd4e25a66db408d6c15a5d", "source_group_file": "scene_082/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle rear-right at about 17m", "scene_id": "scene_082", "ground_truth": "The boxed object is a car located in the rear-right of the ego vehicle, approximately 15–25 meters away, and is two lanes to the right. It is fully visible and on the road, moving away from the ego vehicle. Its orientation of motion indicates it is receding from the ego vehicle's position.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "c95e975aef6741248cbbc5a3707ea4f4", "source_group_file": "scene_090/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle rear-right at about 14m", "scene_id": "scene_090", "ground_truth": "The target is a parked car located behind and to the right of the ego vehicle, in the parking lot beyond the curb. It appears stationary, with no motion relative to the ego vehicle, and is approximately 15 meters away.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "d4da7654cae64f5f9043673b96e3e9ea", "source_group_file": "scene_093/group_005_vehicle_annotations.json", "id": "SP-7", "group_id": "group_005", "object_reference": "the vehicle rear-left at about 41m", "scene_id": "scene_093", "ground_truth": "The boxed object is located behind and to the left of the ego vehicle, situated within a fenced parking or industrial area adjacent to the roadway. It appears stationary and is oriented roughly parallel to the road. The approximate distance is 30–40 meters.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "866afbc00fd64cbe93dbb28450366c87", "source_group_file": "scene_096/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle front-left at about 19m", "scene_id": "scene_096", "ground_truth": "The vehicle in the front-left at about 19m is a black sedan parked in a lot to the left of the ego vehicle, approximately 20-25 meters away. It is stationary and located in a parking area, not on the drivable road surface. It is positioned in the same general area as the ego vehicle but is not in an adjacent lane or moving.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "bcd687076df84aec8a22286a588b47ee", "source_group_file": "scene_103/group_003_vehicle_annotations.json", "id": "SP-7", "group_id": "group_003", "object_reference": "the vehicle front-left at about 21m", "scene_id": "scene_103", "ground_truth": "The vehicle in the red bounding box is a green bus located directly ahead of the ego vehicle in the same lane, approximately 20–30 meters away. It is moving in the same direction as the ego vehicle, indicating a parallel trajectory. The vehicle is on the drivable road surface and is fully visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "e4fd14e25df046eb991924d6c65beb65", "source_group_file": "scene_108/group_005_vehicle_annotations.json", "id": "SP-7", "group_id": "group_005", "object_reference": "the vehicle rear-right at about 15m", "scene_id": "scene_108", "ground_truth": "The vehicle rear-right at about 15m is actually a green bus located to the left of the ego vehicle, in the same lane, and moving in the same direction. It is approximately 10–15 meters ahead, not behind, and is partially visible. The object is not receding but is ahead and slightly to the left.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "1eee2d21d2914edcbc2592fbb69b3764", "source_group_file": "scene_111/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle behind at about 43m", "scene_id": "scene_111", "ground_truth": "There is no vehicle visible in the red bounding box or at approximately 43m behind the ego vehicle. The road ahead is clear, with no vehicles in the same lane or directly behind the ego vehicle. The scene shows a straight road with trees on both sides and a building on the right, but no vehicle is present at the specified distance or position.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "4d830dc2debd4f1fa6c404c415db6705", "source_group_file": "scene_131/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle front-right at about 55m", "scene_id": "scene_131", "ground_truth": "The object in the red bounding box is a pedestrian, not a vehicle, located on the sidewalk to the front-right of the ego vehicle, approximately 30-40 meters away. The pedestrian is walking away from the ego vehicle, parallel to the road, and is not on the drivable road surface. There is no occlusion; the object is clearly visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "59836fbee4344864bae5bd7df39a50c7", "source_group_file": "scene_179/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle front-left at about 57m", "scene_id": "scene_179", "ground_truth": "The vehicle in the red bounding box is a yellow taxi located in the left lane, approximately 60 meters ahead of the ego vehicle. It is moving in the same direction as the ego vehicle, parallel to it, and is not occluded. The vehicle is on the drivable road surface.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "c6e5ac34d57e416f8347f57a234cebb4", "source_group_file": "scene_193/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle rear-left at about 29m", "scene_id": "scene_193", "ground_truth": "The vehicle in the red bounding box is located in a parking lot to the rear-left of the ego vehicle, approximately 20–30 meters away. It is stationary and positioned on a paved parking surface, not on the drivable road. The vehicle is not fully occluded and is clearly visible in the parking area.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "dac076a18b5f4fc78e3a9616877f70ef", "source_group_file": "scene_201/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle front-right at about 18m", "scene_id": "scene_201", "ground_truth": "The object is ahead and to the right of the ego vehicle, located on the sidewalk near the curb. It appears stationary and is oriented vertically relative to the road, not parallel. The approximate distance is about 15–20 meters from the ego vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "361ba279aaba4d929f3ac91e77cb1a20", "source_group_file": "scene_208/group_003_vehicle_annotations.json", "id": "SP-7", "group_id": "group_003", "object_reference": "the vehicle rear-right at about 35m", "scene_id": "scene_208", "ground_truth": "There is no vehicle in the rear-right at about 35m. The red bounding box in the query image highlights a traffic light pole, not a vehicle. The object is stationary and located on the right side of the road, near a pedestrian crossing and traffic cones, approximately 20–30 meters away.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "b1e4f66b95c3438bae0c280ba277ec3c", "source_group_file": "scene_216/group_006_vehicle_annotations.json", "id": "SP-7", "group_id": "group_006", "object_reference": "the vehicle rear-right at about 47m", "scene_id": "scene_216", "ground_truth": "The target object is behind the ego vehicle, positioned to the rear-right, approximately 47 meters away. It is located in the lane to the right of the ego vehicle’s lane and is moving in the same direction, following behind. The object is partially occluded by the white truck and surrounding foliage. The vehicle is on the road and appears to be traveling in the same direction as the ego vehicle but at a greater distance.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "14f1eb9b9dba4412b9bc795e86849ed2", "source_group_file": "scene_231/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle rear-right at about 36m", "scene_id": "scene_231", "ground_truth": "There is no vehicle visible in the red bounding box. The object is a small orange structure, possibly a traffic sign or barrier, located on the roadside curb ahead and to the right of the ego vehicle, approximately 30–40 meters away. It is stationary and not in motion.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "e4fbd70ccb7f4066b804fc55d8d15852", "source_group_file": "scene_234/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle rear-left at about 41m", "scene_id": "scene_234", "ground_truth": "There is no vehicle in the rear-left at 41m. The red bounding box in the query frame is on a small red object on the right side of the road, near the curb, approximately 40–50 meters ahead. It appears to be a stationary object, possibly a traffic cone or marker, not a vehicle. The ego vehicle is moving forward on a straight road with no visible vehicles in the rear-left.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "99ab0ec4a6284f82800cc3ba7e4f77d7", "source_group_file": "scene_235/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle behind at about 61m", "scene_id": "scene_235", "ground_truth": "The vehicle behind at about 61m is a purple and orange bus, located in the left adjacent lane, moving away from the ego vehicle. It is approximately 61 meters behind, on the road, and its trajectory indicates it is receding. The object is clearly visible and not partially occluded.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "ac61ed9173194c33be8ba827c9de3d54", "source_group_file": "scene_238/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle front-right at about 15m", "scene_id": "scene_238", "ground_truth": "The vehicle front-right at about 15m is a black station wagon parked in a parking lot, located to the right and slightly ahead of the ego vehicle. It is stationary, not on the drivable road surface, and is approximately 10–15 meters away.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "d5239ad2e077499cb6d7d5cb0f137cd9", "source_group_file": "scene_252/group_002_vehicle_annotations.json", "id": "SP-7", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 43m", "scene_id": "scene_252", "ground_truth": "The object in the red bounding box is a yellow construction vehicle (likely a bulldozer or loader) located on the left side of the road, adjacent to the construction site, not a vehicle rear-right at 43m. It is stationary and positioned on gravel, not on the road. The distance is approximately 15-20 meters, not 30-50m.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "beac144a6ba6415f94a14bab4d889a51", "source_group_file": "scene_253/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle rear-left at about 57m", "scene_id": "scene_253", "ground_truth": "The vehicle in the red bounding box is located ahead of the ego vehicle, approximately 57 meters away, and is fully visible. It is positioned in the same lane as the ego vehicle and is moving forward in the same direction. The vehicle is on the road and not occluded by other objects.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "dfed902343a84babb02c919c0b5e3a63", "source_group_file": "scene_257/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 36m", "scene_id": "scene_257", "ground_truth": "The yellow bus visible in the rear-left at approximately 36m is located behind and to the left of the ego vehicle, moving away. It is in the adjacent left lane and is clearly visible on the drivable road surface, not occluded.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "dee39e536d2544ef8aee523802f2d0f1", "source_group_file": "scene_298/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle front-left at about 35m", "scene_id": "scene_298", "ground_truth": "The vehicle front-left at about 35m is located in front of the ego vehicle, approximately 30–50 meters away, and is moving in the same direction as the ego vehicle. It is positioned in the left adjacent lane and is not occluded. The vehicle is on the drivable road surface, consistent with a straight roadway in a construction zone, not a roundabout.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "ae82566ad1da4841bb15179ab2a8ec2e", "source_group_file": "scene_323/group_006_vehicle_annotations.json", "id": "SP-7", "group_id": "group_006", "object_reference": "the vehicle rear-left at about 69m", "scene_id": "scene_323", "ground_truth": "There is no visible vehicle in the rear-left at 69m. The provided query frame (CAM_BACK_LEFT) shows a street scene with a lamppost and a concrete barrier, but no vehicle in the specified location or with a red bounding box. The object described in the ground truth does not exist in the image.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "0e5efef53ecf417fb5584a645f28018c", "source_group_file": "scene_333/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle front-left at about 62m", "scene_id": "scene_333", "ground_truth": "The vehicle front-left at about 62m is a white sedan located in front of the ego vehicle, approximately 62 meters away, and is moving in the same direction as the ego vehicle. It is positioned in the adjacent left lane and is fully visible on the road. Its trajectory indicates it is traveling parallel to the ego vehicle's path.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "5f461b3d9c8e4c239141df737d50dfed", "source_group_file": "scene_339/group_002_vehicle_annotations.json", "id": "SP-7", "group_id": "group_002", "object_reference": "the vehicle rear-left at about 55m", "scene_id": "scene_339", "ground_truth": "The vehicle in the red bounding box is located in the rear-left of the ego vehicle, approximately 55 meters away. It is positioned in the leftmost lane of the road, two lanes to the left of the ego vehicle’s current lane. The vehicle is moving away from the ego vehicle in the same general direction of travel. It is partially occluded by a black fence and a utility pole on the right side of the frame, but its rear is visible. The vehicle appears to be a dark-colored sedan.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "4cdb75a37c5d4cb08ec1078ded1c0705", "source_group_file": "scene_348/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle in front at about 80m", "scene_id": "scene_348", "ground_truth": "The target object is a vehicle located ahead of the ego vehicle, positioned in the left adjacent lane and slightly to the left of the ego lane center. It is traveling in the same direction as the ego vehicle, moving away along the road. The vehicle is approximately 35–45 meters ahead and remains fully visible on the drivable ground plane.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "b7b080ede8ea4f29a5c0db5504340d4f", "source_group_file": "scene_353/group_002_vehicle_annotations.json", "id": "SP-7", "group_id": "group_002", "object_reference": "the vehicle front-left at about 71m", "scene_id": "scene_353", "ground_truth": "The vehicle front-left at about 71m is not visible in the provided frames. The frames show a city street with construction barriers, a bus, and several cars, but no object is marked with a red bounding box. The described object does not exist in the scene as presented.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "366cabb7d65a4edd9593a2e1477d541b", "source_group_file": "scene_368/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle rear-left at about 47m", "scene_id": "scene_368", "ground_truth": "There is no vehicle rear-left at about 47m. The object in the red bounding box in the query frame is a stationary yellow construction barrier on the sidewalk, located to the left of the ego vehicle and approximately 20–30 meters away. It is not a vehicle and is not moving.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "7d7b59e76c9c4df181f9ba99475bf1d4", "source_group_file": "scene_381/group_003_vehicle_annotations.json", "id": "SP-7", "group_id": "group_003", "object_reference": "the vehicle front-left at about 46m", "scene_id": "scene_381", "ground_truth": "The object in the red bounding box is a stationary white sign on a fence, located to the front-left of the ego vehicle at approximately 46m. It is not a vehicle, is not moving, and is not on the drivable road surface. It is positioned on the left side of the road, adjacent to a construction or storage area.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "4d27f921e1804509bc2010efafde3dfb", "source_group_file": "scene_382/group_003_vehicle_annotations.json", "id": "SP-7", "group_id": "group_003", "object_reference": "the vehicle rear-left at about 72m", "scene_id": "scene_382", "ground_truth": "The vehicle in the red bounding box is located ahead of the ego vehicle, approximately 72 meters away, and is moving in the same direction. It is positioned in the left lane, slightly to the left of the ego vehicle's center, and is visible on the drivable road surface. Its orientation of motion is forward, consistent with traveling in the same direction as the ego vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "8be7ab0320324341afb2d8de849bcec8", "source_group_file": "scene_425/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle front-right at about 35m", "scene_id": "scene_425", "ground_truth": "The vehicle in the red bounding box is located ahead and to the right of the ego vehicle, approximately 30–50 meters away, positioned in the rightmost lane or adjacent parking area near the curb. It is stationary and oriented parallel to the road, not moving toward the ego vehicle. The vehicle is partially occluded by a parked SUV in front of it and is situated on a paved surface adjacent to the road, not on the main drivable roadway.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "2cd433bffe3a422c81a512c79dc441af", "source_group_file": "scene_432/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle front-left at about 59m", "scene_id": "scene_432", "ground_truth": "The object in the red bounding box is a stationary utility pole or signpost located on the sidewalk, approximately 59 meters ahead and to the front-left of the ego vehicle. It is not a vehicle and is not moving. It is positioned on the non-drivable sidewalk area, adjacent to the parking lot, and is clearly visible without occlusion.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "e734bed5b6574aecbbab6b300596022a", "source_group_file": "scene_447/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle front-left at about 40m", "scene_id": "scene_447", "ground_truth": "The vehicle in the red bounding box is located directly in front of the ego vehicle, approximately 40 meters away, in the same lane. It is moving away from the ego vehicle, traveling in the same direction along the road. The vehicle is fully visible and positioned on the drivable road surface.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "fe063fb1cb494d308ce7456ba339d68b", "source_group_file": "scene_448/group_003_vehicle_annotations.json", "id": "SP-7", "group_id": "group_003", "object_reference": "the vehicle rear-right at about 27m", "scene_id": "scene_448", "ground_truth": "The vehicle in the red bounding box is a white bus located directly ahead of the ego vehicle in the same lane, approximately 27 meters away. It is moving in the same direction as the ego vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "8891b311f6154e9ead52b84a8db62ddc", "source_group_file": "scene_470/group_007_vehicle_annotations.json", "id": "SP-7", "group_id": "group_007", "object_reference": "the vehicle front-left at about 31m", "scene_id": "scene_470", "ground_truth": "The vehicle in the front-left at about 31m is a silver sedan parked on the side of the road, located in the front-left of the ego vehicle, approximately 30 meters away. It is stationary and positioned in a parking space adjacent to the curb, not on the drivable road surface.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "5b6e1b0f91f34cc989d7758aa59f5b5c", "source_group_file": "scene_491/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle rear-right at about 55m", "scene_id": "scene_491", "ground_truth": "The vehicle in the red bounding box is a white van located ahead of the ego vehicle, approximately 55 meters away, and is moving in the same direction. It is in the center lane, directly ahead of the ego vehicle, and is clearly visible on the road.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "8d4c7dd2cd4c4bf29332a3f318ccc119", "source_group_file": "scene_513/group_003_vehicle_annotations.json", "id": "SP-7", "group_id": "group_003", "object_reference": "the vehicle rear-left at about 35m", "scene_id": "scene_513", "ground_truth": "The object in the red bounding box is a stationary black vehicle parked on the side of the road, located approximately 15-20 meters ahead of the ego vehicle and to the right, not behind and to the left. It is not in motion and is not occluded by other vehicles. The bounding box is incorrectly placed on a distant vehicle in the background, not the one described.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "629e9e9e9bc64bc6a0c066bcd695f310", "source_group_file": "scene_554/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 33m", "scene_id": "scene_554", "ground_truth": "There is no vehicle in the rear-left at about 33m. The red bounding box in the query frame highlights a stationary orange traffic barrier on the right shoulder, not a vehicle. The object is on the non-drivable shoulder, not in a lane.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "f4880eeefa9d4078bab06f3489f2776e", "source_group_file": "scene_557/group_002_vehicle_annotations.json", "id": "SP-7", "group_id": "group_002", "object_reference": "the vehicle rear-left at about 32m", "scene_id": "scene_557", "ground_truth": "The object in the red bounding box is not a vehicle, but a traffic signal or sign structure on the right side of the road, approximately 30–40 meters ahead. It is stationary and located on the roadside, not in a lane. The ego vehicle is approaching it from behind and to the left.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "4dd61a8675594eca8ffe1021edb7f028", "source_group_file": "scene_565/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle rear-right at about 73m", "scene_id": "scene_565", "ground_truth": "The object in the red bounding box is a white truck located behind and to the right of the ego vehicle, approximately 73m away. It is stationary in a parking lot beyond a chain-link fence, not on the road or moving. The vehicle is partially occluded by the fence and is not in the same direction of travel as the ego vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "8cbab87d099b4b43b462ddc3f5802a87", "source_group_file": "scene_622/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle front-right at about 83m", "scene_id": "scene_622", "ground_truth": "The vehicle front-right at about 83m is actually a white car visible in the front-right camera view, located approximately 30-40 meters away, not 83m. It is positioned in the adjacent right lane, moving in the same direction as the ego vehicle, and is not occluded. The vehicle is on the road and remains on the drivable ground plane.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "1401e7ab22d54d0bacf17bbe09b751d8", "source_group_file": "scene_672/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle front-right at about 60m", "scene_id": "scene_672", "ground_truth": "The object in the yellow bounding box is a stationary yellow vehicle (likely a bus or van) located directly ahead of the ego vehicle, approximately 60 meters away, in the same lane. It is not moving toward the ego vehicle, and there is no red bounding box in the image.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "5da65b6bb6ae41bb929bd246a3b1518a", "source_group_file": "scene_732/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle rear-right at about 73m", "scene_id": "scene_732", "ground_truth": "The green bus is located behind the ego vehicle, approximately 70 meters away, and is moving away from it. It is positioned in the same lane or adjacent lane to the right, and is clearly visible on the drivable road surface without significant occlusion.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "c6fb051d5d0e4f6eb67c4c62c3221c3e", "source_group_file": "scene_741/group_008_vehicle_annotations.json", "id": "SP-7", "group_id": "group_008", "object_reference": "the vehicle rear-left at about 76m", "scene_id": "scene_741", "ground_truth": "The vehicle in the red bounding box is located behind the ego vehicle, approximately 76 meters away, and is moving away from it. It is positioned in the lane to the left of the ego vehicle’s current lane (one lane to the left, not two), and is fully visible on the road surface. The vehicle is oriented in the same direction as the ego vehicle, traveling forward along the road.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "ac6d0adf939748908f62281b63e8abfc", "source_group_file": "scene_742/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle front-right at about 27m", "scene_id": "scene_742", "ground_truth": "The vehicle front-right at about 27m is located in the front-right of the ego vehicle, approximately 25–30 meters away, and is in the same lane or adjacent right lane. It is fully visible and moving away from the ego vehicle, proceeding straight through the intersection ahead. The vehicle is on the road and appears to be continuing forward, not approaching the ego vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "784a659913d84e5082f4cba7bfdda9a1", "source_group_file": "scene_771/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle front-left at about 53m", "scene_id": "scene_771", "ground_truth": "The object in the red bounding box is not a vehicle. It is a distant illuminated structure or sign on the left side of the road, approximately 53 meters ahead. It is stationary and not moving toward the ego vehicle. It is located in the left shoulder or roadside area, not in the lane, and is not two lanes to the left.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "ea3d4e7de9684b83b899608abae3d422", "source_group_file": "scene_802/group_001_vehicle_annotations.json", "id": "SP-7", "group_id": "group_001", "object_reference": "the vehicle rear-right at about 38m", "scene_id": "scene_802", "ground_truth": "The vehicle in the red bounding box is located ahead of the ego vehicle, approximately 30-50 meters away, and is positioned in the same lane or slightly to the right. It is moving away from the ego vehicle along the road, not crossing its path. The vehicle is visible on the road in a residential night scene, with no clear evidence of a turning maneuver or occlusion.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the spatial relationship between the ego vehicle and the object in the red bounding box, including its relative position, orientation of motion, and approximate distance.", "task": "space-perception", "object_id": "cad8425df754459f9a0044ffd242d6af", "source_group_file": "scene_827/group_004_vehicle_annotations.json", "id": "SP-7", "group_id": "group_004", "object_reference": "the vehicle rear-left at about 22m", "scene_id": "scene_827", "ground_truth": "The vehicle in the rear-left at about 22m is not visible in the provided frames. The bounding box in the query frame (CAM_BACK) highlights a distant, indistinct object that appears to be a vehicle or light source on the road ahead, not to the rear-left. It is located approximately 30–50 meters ahead of the ego vehicle, in the same lane or adjacent lane, and is moving away from the ego vehicle. The object is not occluded but is small and dim due to distance and low lighting.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": { "A": "Partially obstructing ego lane", "B": "Blocking ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "627d1840ff814cdd8033e2a68cf8fddd", "source_group_file": "scene_015/group_006_vehicle_annotations.json", "scene_description": "", "id": "SU-1", "group_id": "group_006", "object_reference": "the vehicle behind at about 6m", "scene_id": "scene_015", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "No constraint", "C": "Partially obstructing ego lane", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "e8d8432367d54eadbddaa2a0d054df12", "source_group_file": "scene_032/group_001_vehicle_annotations.json", "scene_description": "", "id": "SU-1", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 12m", "scene_id": "scene_032", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "No constraint", "C": "Dynamic obstruction in ego lane", "D": "Partially obstructing ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "b28a0e2defb84ad7860d31afb79221ba", "source_group_file": "scene_069/group_005_vehicle_annotations.json", "scene_description": "", "id": "SU-1", "group_id": "group_005", "object_reference": "the vehicle in front at about 21m", "scene_id": "scene_069", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "No constraint", "B": "Blocking ego lane", "C": "Partially obstructing ego lane", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "b3879eee571e4bd8bdbf3a07db2c222e", "source_group_file": "scene_095/group_004_vehicle_annotations.json", "scene_description": "", "id": "SU-1", "group_id": "group_004", "object_reference": "the vehicle in front at about 32m", "scene_id": "scene_095", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "No constraint", "C": "Partially obstructing ego lane", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "dbb398a48db44ed69df3649fd6efdb63", "source_group_file": "scene_099/group_007_vehicle_annotations.json", "scene_description": "", "id": "SU-1", "group_id": "group_007", "object_reference": "the vehicle front-right at about 31m", "scene_id": "scene_099", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "Blocking ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "87740c353bbc47eba79adfd06ff4646a", "source_group_file": "scene_104/group_007_vehicle_annotations.json", "id": "SU-1", "group_id": "group_007", "object_reference": "the vehicle behind at about 9m", "scene_id": "scene_104", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "Dynamic obstruction in ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "aeeb0289f56e440ca56d09fbda8ca1c8", "source_group_file": "scene_144/group_006_vehicle_annotations.json", "id": "SU-1", "group_id": "group_006", "object_reference": "the vehicle rear-left at about 33m", "scene_id": "scene_144", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "Dynamic obstruction in ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "fc99896584e0441bb2beca2d2bffaca8", "source_group_file": "scene_165/group_001_vehicle_annotations.json", "id": "SU-1", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 25m", "scene_id": "scene_165", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "Dynamic obstruction in ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "6946c79cbada492a91b223faffba3af2", "source_group_file": "scene_171/group_001_vehicle_annotations.json", "id": "SU-1", "group_id": "group_001", "object_reference": "the vehicle front-left at about 20m", "scene_id": "scene_171", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "Blocking ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "9d12e9080545421aab5a1dfee5ff7935", "source_group_file": "scene_181/group_001_vehicle_annotations.json", "id": "SU-1", "group_id": "group_001", "object_reference": "the vehicle behind at about 5m", "scene_id": "scene_181", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "Blocking ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "8576fbee279d44bbbe8589e13e4b7bef", "source_group_file": "scene_212/group_007_vehicle_annotations.json", "id": "SU-1", "group_id": "group_007", "object_reference": "the vehicle rear-right at about 10m", "scene_id": "scene_212", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "Dynamic obstruction in ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "c3b4efadd5e145d48a843bb97db1c8e8", "source_group_file": "scene_223/group_001_vehicle_annotations.json", "id": "SU-1", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 17m", "scene_id": "scene_223", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "No constraint", "C": "Blocking ego lane", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "cf0945a4c01d4ca19a4b9fb3c96263f5", "source_group_file": "scene_227/group_001_vehicle_annotations.json", "id": "SU-1", "group_id": "group_001", "object_reference": "the vehicle behind at about 9m", "scene_id": "scene_227", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Dynamic obstruction in ego lane", "B": "Blocking ego lane", "C": "Partially obstructing ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "cbe7138fb91940a0af6a8dadd99dc993", "source_group_file": "scene_260/group_007_vehicle_annotations.json", "id": "SU-1", "group_id": "group_007", "object_reference": "the vehicle front-right at about 13m", "scene_id": "scene_260", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "No constraint", "C": "Dynamic obstruction in ego lane", "D": "Blocking ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "3f1b9bf61b3649f7a648b1e306319afe", "source_group_file": "scene_271/group_008_vehicle_annotations.json", "id": "SU-1", "group_id": "group_008", "object_reference": "the vehicle behind at about 7m", "scene_id": "scene_271", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "cdc11b5ed9444b63bc9a5d2aeb15757b", "source_group_file": "scene_277/group_002_vehicle_annotations.json", "id": "SU-1", "group_id": "group_002", "object_reference": "the vehicle front-right at about 22m", "scene_id": "scene_277", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Dynamic obstruction in ego lane", "B": "Blocking ego lane", "C": "Partially obstructing ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "44fe49a12d1444df9e85957afeecf2be", "source_group_file": "scene_283/group_002_vehicle_annotations.json", "id": "SU-1", "group_id": "group_002", "object_reference": "the vehicle front-right at about 24m", "scene_id": "scene_283", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "290f734dd270490da5ced4fbe64e32dd", "source_group_file": "scene_295/group_003_vehicle_annotations.json", "id": "SU-1", "group_id": "group_003", "object_reference": "the vehicle behind at about 14m", "scene_id": "scene_295", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Dynamic obstruction in ego lane", "B": "Blocking ego lane", "C": "Partially obstructing ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "b14b2757ca2a4d85b5c5a521dbde0eab", "source_group_file": "scene_314/group_006_vehicle_annotations.json", "id": "SU-1", "group_id": "group_006", "object_reference": "the vehicle front-left at about 19m", "scene_id": "scene_314", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "No constraint", "C": "Partially obstructing ego lane", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "f16dbea5c6114a36b31dc61ddc6e70e8", "source_group_file": "scene_318/group_008_vehicle_annotations.json", "id": "SU-1", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 11m", "scene_id": "scene_318", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "a7e0fc1d62ed4bc09ab6a0776f0c5a36", "source_group_file": "scene_328/group_007_vehicle_annotations.json", "id": "SU-1", "group_id": "group_007", "object_reference": "the vehicle in front at about 8m", "scene_id": "scene_328", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "Blocking ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "3cdcf622b0d84686894b34a575e7afc3", "source_group_file": "scene_332/group_002_vehicle_annotations.json", "id": "SU-1", "group_id": "group_002", "object_reference": "the vehicle behind at about 7m", "scene_id": "scene_332", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "No constraint", "B": "Blocking ego lane", "C": "Partially obstructing ego lane", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "f3ed0df683bb49adbe40a225ae72bcd8", "source_group_file": "scene_412/group_008_vehicle_annotations.json", "id": "SU-1", "group_id": "group_008", "object_reference": "the vehicle behind at about 115m", "scene_id": "scene_412", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "961a897cd2364fbdad1e4b86f0d2aa5f", "source_group_file": "scene_414/group_008_vehicle_annotations.json", "id": "SU-1", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 4m", "scene_id": "scene_414", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "b5a70dc2af034e77bfeb58d868e34798", "source_group_file": "scene_422/group_001_vehicle_annotations.json", "id": "SU-1", "group_id": "group_001", "object_reference": "the vehicle behind at about 9m", "scene_id": "scene_422", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Dynamic obstruction in ego lane", "B": "Blocking ego lane", "C": "Partially obstructing ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "199ff1dbc6b74ca899cbc9f36f4c5772", "source_group_file": "scene_423/group_008_vehicle_annotations.json", "id": "SU-1", "group_id": "group_008", "object_reference": "the vehicle front-left at about 21m", "scene_id": "scene_423", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "c7078cecba7a46c69ad6ad4ea655dc00", "source_group_file": "scene_437/group_007_vehicle_annotations.json", "id": "SU-1", "group_id": "group_007", "object_reference": "the vehicle rear-right at about 57m", "scene_id": "scene_437", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "Blocking ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "5ababb7396f049efa2d19a66af282e90", "source_group_file": "scene_442/group_007_vehicle_annotations.json", "id": "SU-1", "group_id": "group_007", "object_reference": "the vehicle behind at about 7m", "scene_id": "scene_442", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Partially obstructing ego lane", "B": "Blocking ego lane", "C": "No constraint", "D": "Dynamic obstruction in ego lane" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "ed4fef30a0e044e99159d45fe5ae2b33", "source_group_file": "scene_445/group_003_vehicle_annotations.json", "id": "SU-1", "group_id": "group_003", "object_reference": "the vehicle in front at about 17m", "scene_id": "scene_445", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Blocking ego lane", "B": "Partially obstructing ego lane", "C": "Dynamic obstruction in ego lane", "D": "No constraint" }, "question": "What physical constraints affect the ego vehicle given the object in the red bounding box?", "task": "space-understanding", "object_id": "8ad4ffaeebd24d7f8a5f963c490204a7", "source_group_file": 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"space-understanding", "object_id": "9c9ebd15dbbb4131826af2cb623cdacd", "source_group_file": "scene_647/group_008_vehicle_annotations.json", "id": "SU-4", "group_id": "group_008", "object_reference": "the vehicle front-left at about 29m", "scene_id": "scene_647", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe to change right", "B": "Safe to change left", "C": "Unsafe both sides", "D": "Safe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "824cbd34b31b4eeaaf40af6a46abf5db", "source_group_file": "scene_663/group_006_vehicle_annotations.json", "id": "SU-4", "group_id": "group_006", "object_reference": "the vehicle front-right at about 22m", "scene_id": "scene_663", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe to change left", "B": "Safe to change right", "C": "Unsafe both sides", "D": "Safe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "c0c333f9b1ae4cec98be62bbe29b82df", "source_group_file": "scene_670/group_002_vehicle_annotations.json", "id": "SU-4", "group_id": "group_002", "object_reference": "the vehicle front-left at about 19m", "scene_id": "scene_670", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe to change right", "B": "Unsafe both sides", "C": "Safe both sides", "D": "Safe to change left" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "933aaa3a1fe3486eb5caa2ebe08e3eac", "source_group_file": "scene_707/group_007_vehicle_annotations.json", "id": "SU-4", "group_id": "group_007", "object_reference": "the vehicle front-right at about 29m", "scene_id": "scene_707", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe to change right", "B": "Unsafe both sides", "C": "Safe to change left", "D": "Safe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "be29b694c6a84a2dbb7686c5d8befafb", "source_group_file": "scene_736/group_008_vehicle_annotations.json", "id": "SU-4", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 16m", "scene_id": "scene_736", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe both sides", "B": "Safe to change left", "C": "Safe to change right", "D": "Unsafe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "a5bfdc739a2d4ac8ba2bdf7d171bf85d", "source_group_file": "scene_746/group_007_vehicle_annotations.json", "id": "SU-4", "group_id": "group_007", "object_reference": "the vehicle front-left at about 45m", "scene_id": "scene_746", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe to change right", "B": "Safe to change left", "C": "Unsafe both sides", "D": "Safe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "0fd0eb4eaec24dfeb6ae0104a5945cf4", "source_group_file": "scene_763/group_006_vehicle_annotations.json", "id": "SU-4", "group_id": "group_006", "object_reference": "the vehicle front-left at about 25m", "scene_id": "scene_763", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe to change right", "B": "Safe to change left", "C": "Unsafe both sides", "D": "Safe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "10afbf4de6c54f13a6dac5f1e43da01b", "source_group_file": "scene_764/group_008_vehicle_annotations.json", "id": "SU-4", "group_id": "group_008", "object_reference": "the vehicle front-right at about 9m", "scene_id": "scene_764", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe both sides", "B": "Safe to change left", "C": "Safe to change right", "D": "Unsafe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "5f0f3d40cef448418ceeecbf4fde9a1a", "source_group_file": "scene_774/group_002_vehicle_annotations.json", "id": "SU-4", "group_id": "group_002", "object_reference": "the vehicle behind at about 14m", "scene_id": "scene_774", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Safe to change left", "B": "Safe to change right", "C": "Safe both sides", "D": "Unsafe both sides" }, "question": "Does the ego vehicle have sufficient clearance to change lanes?", "task": "space-understanding", "object_id": "e46f9b7fdbbb4dcc9b80bb83f6bac401", "source_group_file": "scene_834/group_005_vehicle_annotations.json", "id": "SU-4", "group_id": "group_005", "object_reference": "the vehicle rear-right at about 50m", "scene_id": "scene_834", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "be8e94b8ef144604b70d6ef58f17e23b", "source_group_file": "scene_012/group_005_vehicle_annotations.json", "id": "SU-7", "group_id": "group_005", "object_reference": "the vehicle rear-left at about 67m", "scene_id": "scene_012", "ground_truth": "The ego vehicle is in a medium-risk environment with a large semi-truck (blue trailer) located directly in the rear-left at approximately 67m, which acts as a physical constraint. The truck is positioned in the adjacent lane, reducing available drivable space and increasing risk for lane changes. The scene includes a concrete barrier on the left and a construction zone on the right, limiting maneuverability. The geometrically feasible maneuver is to maintain current lane and proceed with caution, as lane changes to the left are blocked by the barrier and to the right are constrained by the truck and construction zone.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "ce0e2fa59f3446a68f475a103fc6a2f6", "source_group_file": "scene_019/group_002_vehicle_annotations.json", "scene_description": "", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle in front at about 33m", "scene_id": "scene_019", "ground_truth": "The ego vehicle is in a moderately dense environment with a vehicle in front at about 33m. The road is narrow with a solid white line on the right, indicating no lane change to the right. The left side is bordered by a sidewalk and trees, with no visible lane for leftward movement. Drivable space is limited to the current lane. The risk level is moderate due to the vehicle ahead. The most feasible maneuver is to maintain the current lane and proceed with caution.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "130463d0e66e4c3d82ac5fe21cd68ba8", "source_group_file": "scene_027/group_007_vehicle_annotations.json", "scene_description": "", "id": "SU-7", "group_id": "group_007", "object_reference": "the vehicle in front at about 14m", "scene_id": "scene_027", "ground_truth": "The ego vehicle is following a yellow taxi at approximately 14m in a two-lane road with clear visibility. The drivable space is sufficient in the current lane, with no immediate physical constraints. The surrounding environment is relatively open with trees and buildings on both sides. The risk level is low, as the vehicle ahead is moving slowly and there are no sudden obstructions. Lane change to the left is not feasible due to the presence of a bus in the adjacent lane and a solid white line. The situation is stable and requires normal following behavior.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "5c4d70f3de2e403b93e68c4cc97b1c03", "source_group_file": "scene_052/group_005_vehicle_annotations.json", "scene_description": "", "id": "SU-7", "group_id": "group_005", "object_reference": "the vehicle in front at about 45m", "scene_id": "scene_052", "ground_truth": "The ego vehicle is on a narrow urban street flanked by modern buildings, with physical constraints from curbs, sidewalks, hedges, and structural columns on both sides. The forward drivable lane is mostly open, but the highlighted vehicle ahead at approximately 45m occupies the center of the lane, reducing safe longitudinal clearance. There are no parked cars visible in the immediate vicinity, and pedestrians are present on the sidewalks but not encroaching on the roadway.\n\nRisk level is moderate: the vehicle ahead is at a distance that allows for safe deceleration or stopping if necessary, but requires attention to maintain a safe following distance. The constrained roadside space limits lateral maneuverability.\n\nFeasible maneuvers include continuing forward at a reduced speed, preparing to stop, or maintaining current lane position with caution. Lane changes are not advisable due to the narrow width and lack of visible clearance on either side. Aggressive swerving or overtaking is unsafe given the proximity of the building structures and the presence of the vehicle ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "3845a3c505124f5eaca3b1dbf9065e8e", "source_group_file": "scene_055/group_006_vehicle_annotations.json", "id": "SU-7", "group_id": "group_006", "object_reference": "the vehicle behind at about 21m", "scene_id": "scene_055", "ground_truth": "The ego vehicle is driving forward on a narrow road between two buildings, with a white car ahead and a yellow vehicle further down the road. The vehicle behind at about 21m is a yellow car, not obstructing the path. Drivable space is available ahead in the lane. The environment is moderately dense with buildings and trees, but no immediate physical constraints or high-risk factors are present. Feasible maneuvers include continuing forward or stopping if required by traffic signals ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "e1c0daa37c8b4d78ab25660d210e899a", "source_group_file": "scene_056/group_006_vehicle_annotations.json", "id": "SU-7", "group_id": "group_006", "object_reference": "the vehicle behind at about 26m", "scene_id": "scene_056", "ground_truth": "The ego vehicle is in a moderately dense environment with a red car directly ahead at about 26m, not behind. The drivable space is the current lane and possibly the adjacent lane to the right, with no visible parked motorcycle or obstruction. The road is clear ahead, and the risk level is low. Feasible maneuvers include continuing forward or changing lanes to the right, with no immediate need for maneuvering.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "520c2dae29df4e49a178dc7fe92a3073", "source_group_file": "scene_060/group_002_vehicle_annotations.json", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle front-left at about 95m", "scene_id": "scene_060", "ground_truth": "The ego vehicle is driving on a curved, two-lane road with physical constraints on both sides: a green metal fence and grassy embankment on the left, and a grassy verge with trees and a curb on the right. Drivable space is limited to the current lane, with no visible adjacent lanes for safe lane changes. The road ahead is clear, and there is no vehicle visible at 95m front-left. The scene is safe for continuing straight ahead within the lane, but lateral maneuvers are not feasible due to the physical barriers.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "fc0b789c400f4d36a03db4be873a9a97", "source_group_file": "scene_070/group_007_vehicle_annotations.json", "id": "SU-7", "group_id": "group_007", "object_reference": "the vehicle front-left at about 9m", "scene_id": "scene_070", "ground_truth": "The ego vehicle is in a dense urban intersection, stopped at a traffic light within a construction zone marked by orange barriers and roadwork signage. A large silver van is positioned very close to the ego vehicle’s back-left side, creating a significant physical constraint and limiting lateral clearance. The front-left area contains a vehicle at approximately 9m, which, while not immediately obstructing, reduces available space for forward or lateral movement. The front-right area is partially clear but bordered by construction barriers and pedestrian activity, making a right lane change risky and inadvisable. Drivable space is primarily forward within the current lane, with minimal lateral adjustment possible due to the adjacent van and roadside constraints. Risk level is moderate to high due to the close proximity of the van, limited visibility, and active construction. Feasible maneuvers: proceed slowly straight ahead once the light turns green, maintain current lane position, and avoid any lateral movements until the left side is clear and traffic conditions allow.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "343ef6e8176c4837b15c4df448eab6ba", "source_group_file": "scene_073/group_004_vehicle_annotations.json", "id": "SU-7", "group_id": "group_004", "object_reference": "the vehicle front-right at about 44m", "scene_id": "scene_073", "ground_truth": "The ego vehicle is driving on a two-lane road bordered by orange construction barriers on the left and a white wall with trees on the right. The construction zone includes visible excavators and temporary fencing. The vehicle front-right at about 44m is a yellow utility vehicle, not a truck or excavator, and is stationary. The drivable space is narrow with no safe lane change options due to barriers on the left and a curb on the right. The risk level is moderate due to the construction zone and stationary vehicle ahead. Feasible maneuvers are limited to forward motion with lane maintenance; no lane changes are safe.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "9642dbe5b91e406496a3ed1e2d4ca54e", "source_group_file": "scene_077/group_002_vehicle_annotations.json", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle in front at about 20m", "scene_id": "scene_077", "ground_truth": "The ego vehicle is positioned behind a blue sedan at about 20m, which is stopped at a traffic light. The front path is blocked by the traffic signal and the vehicle ahead. The front-right region is not safely drivable due to the intersection and cross-traffic. The environment is moderately dense with modern buildings and traffic infrastructure. Given the red light and stopped vehicle, no lane change or forward movement is feasible at this moment. The risk level is low as the vehicle ahead is stationary and traffic is controlled.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "b52707f1bee247efab353c8637e426bc", "source_group_file": "scene_110/group_007_vehicle_annotations.json", "id": "SU-7", "group_id": "group_007", "object_reference": "the vehicle behind at about 17m", "scene_id": "scene_110", "ground_truth": "The ego vehicle is in a moderately dense residential environment with multi-story buildings on both sides and trees lining the road. The drivable space is the two-lane road ahead, with a gray van visible ahead in the same lane, not behind. There are no parked motorcycles or bikes visible. The risk level is low, as the road is clear ahead with no immediate obstacles. Feasible maneuvers include continuing straight ahead or turning right at the upcoming intersection, as indicated by the road markings and signage.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "5e8ca8da54274b1d844230b3fc985fdf", "source_group_file": "scene_120/group_002_vehicle_annotations.json", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle front-right at about 39m", "scene_id": "scene_120", "ground_truth": "The ego vehicle is on a road adjacent to a construction zone, with a white temporary barrier and orange traffic cones visible on the right. The vehicle front-right at about 39m is a dark-colored utility vehicle or trailer, not a standard car. The environment is moderately dense with construction barriers, trees, and vehicles. The drivable space is constrained on the right by the barrier, and lane changes to the right are not feasible. The risk level is low as there are no immediate obstacles or moving vehicles in the immediate path. The ego vehicle can proceed straight or turn left, but must avoid the right lane due to the barrier.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "26a36b3ace764aa385e8a383cf907e0e", "source_group_file": "scene_123/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle front-left at about 30m", "scene_id": "scene_123", "ground_truth": "The ego vehicle is positioned in a moderately dense urban environment with clear forward visibility. The vehicle labeled as 'front-left at about 30m' is actually a white sedan directly ahead in the same lane, not to the front-left. There is no dynamic obstruction posing a moderate risk; the road ahead is clear with green traffic lights. The ego vehicle has sufficient forward clearance and can proceed straight. A right lane change is not geometrically feasible due to the presence of a solid white line and a vehicle in the adjacent lane. The scene includes parked cars and static infrastructure, but no immediate dynamic constraints.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "b6b401a4e8c2492f93c47736f3fc5aea", "source_group_file": "scene_140/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 12m", "scene_id": "scene_140", "ground_truth": "The ego vehicle is in a dense environment with multiple parked vehicles nearby. The vehicle rear-left at about 12m is a dark-colored sedan parked in a lot, posing no immediate risk as it is stationary and not in the ego vehicle's path. The front-right region is clear and drivable, but the presence of parked cars along the roadside and a curb restricts lateral movement. A right lane change is not feasible due to the curb and parked vehicles, and the road markings do not support such a maneuver. The current situation is low-risk, and the safest action is to continue straight ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "0854d0e5107d4c8aab96373fe1efe41f", "source_group_file": "scene_150/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle behind at about 19m", "scene_id": "scene_150", "ground_truth": "The ego vehicle is on a relatively narrow, tree-lined urban/campus road. The lane behind is mostly clear, with a curb, sidewalk, fence/building frontage, and large trees constraining the right side, while a raised/marked central divider with bollards constrains the left side. The red-boxed object is near the roadside/sidewalk area and does not appear to intrude significantly into the drivable lane.\n\nAvailable drivable space is sufficient to continue straight within the lane, but lateral maneuvering is limited by the curb and divider. Risk level is low to moderate due to roadside objects, trees causing partial occlusion, and limited lane width. Feasible maneuvers are to proceed straight slowly and stay centered, with readiness to brake; avoid moving toward the curb or making abrupt lateral shifts.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "b36037dc3fbe4285a14fdda5bf828842", "source_group_file": "scene_156/group_002_vehicle_annotations.json", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle front-left at about 58m", "scene_id": "scene_156", "ground_truth": "The ego vehicle is driving on a narrow road bordered by a concrete wall on the right and trees/buildings on the left. A white van is visible ahead in the center of the road, approximately 58m away, not to the front-left. The drivable space is limited to the center lane, with no safe lane change options due to the wall on the right and parked cars on the left. The environment is moderately risky due to the narrow road and potential for sudden obstacles. The most feasible maneuver is to maintain the current lane and proceed with caution.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "02f3467e42a746c39ed2ae60be15abf8", "source_group_file": "scene_174/group_005_vehicle_annotations.json", "id": "SU-7", "group_id": "group_005", "object_reference": "the vehicle rear-right at about 59m", "scene_id": "scene_174", "ground_truth": "The ego vehicle is traveling on a multi-lane urban road with moderate traffic density. The vehicle rear-right at about 59m is a dark-colored sedan parked in a lot, separated by a fence and grassy median, posing no immediate risk. Drivable space is available in the current lane and adjacent lanes. The scene supports safe lane-keeping and potential lane changes, with no physical constraints from the rear-right vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "b4c4287a6bc146c9a7fd7407b9431faa", "source_group_file": "scene_178/group_003_vehicle_annotations.json", "id": "SU-7", "group_id": "group_003", "object_reference": "the vehicle front-right at about 31m", "scene_id": "scene_178", "ground_truth": "The ego vehicle is in a multi-lane urban roadway with moderate traffic. The vehicle front-right at about 31m is a dark-colored sedan parked or moving slowly in the adjacent lane. There is a physical constraint from the front-right vehicle, as it occupies the adjacent lane and is close to the ego vehicle's path. The drivable space is limited to the current lane and possibly the left lane, with no safe clearance for a right lane change. The risk level is high due to proximity and potential for sudden movement. The most feasible maneuver is to maintain the current lane or change to the left lane if safe, avoiding any rightward movement.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "d609f9d217a14e9fbca5158cde93800a", "source_group_file": "scene_194/group_004_vehicle_annotations.json", "id": "SU-7", "group_id": "group_004", "object_reference": "the vehicle rear-left at about 52m", "scene_id": "scene_194", "ground_truth": "The ego vehicle is in a low-risk situation. The rear-left vehicle at 52m is a white car, visible in the distance on the road ahead, not to the rear-left. The environment is not highly congested; there are only a few visible vehicles and construction equipment. The ego has clear drivable space ahead and to the sides, with no immediate physical constraints. The most feasible maneuver is to continue straight ahead, maintaining lane position.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "f1cf833870a247bab5df37cde206940b", "source_group_file": "scene_217/group_007_vehicle_annotations.json", "id": "SU-7", "group_id": "group_007", "object_reference": "the vehicle front-right at about 25m", "scene_id": "scene_217", "ground_truth": "The ego vehicle is traveling straight on a multi-lane road with clear forward visibility. The front-right at 25m contains a static orange construction barrier, not a vehicle. Physical constraints include lane dividers and barriers on the right, limiting lateral movement. Drivable space is available straight ahead. Risk level is low. Feasible maneuvers include continuing straight or, if permitted, changing lanes to the left. No immediate dynamic threats are present.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "a4726875a74d4cbda34aa27952e89ada", "source_group_file": "scene_219/group_005_vehicle_annotations.json", "id": "SU-7", "group_id": "group_005", "object_reference": "the vehicle rear-right at about 25m", "scene_id": "scene_219", "ground_truth": "The ego vehicle is in a constrained urban environment with construction barriers on both sides and a large dump truck directly ahead on the right. The vehicle rear-right at about 25m is a white hatchback, but the immediate right lane is occupied by a large truck, making a right lane change impossible. The left lane is also occupied by a silver sedan ahead. The ego vehicle has limited forward clearance and is surrounded by physical barriers and vehicles, resulting in high risk. A left lane change is not feasible due to the presence of the sedan and the narrow lane. The drivable space is minimal, and the primary feasible maneuver is to proceed slowly forward with caution.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "4c0a28a6ea454f1bb7aef8788c5b8625", "source_group_file": "scene_221/group_003_vehicle_annotations.json", "id": "SU-7", "group_id": "group_003", "object_reference": "the vehicle in front at about 42m", "scene_id": "scene_221", "ground_truth": "The ego vehicle is positioned at an intersection with a black sedan in front at approximately 42m, which is stopped at a red light. The environment includes construction barriers on the left, a large excavator, and a green dump truck on the right, restricting lateral movement. The drivable space is limited to the current lane, with no safe lane change options due to barriers and the adjacent truck. The risk level is moderate due to the presence of pedestrians and other vehicles. Feasible maneuvers are limited to waiting for the light to turn green and proceeding straight.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "ef706726614d4a31a727fc7dcc2fce30", "source_group_file": "scene_232/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle rear-left at about 11m", "scene_id": "scene_232", "ground_truth": "The ego vehicle is on a multi-lane road with a large blue truck visible ahead on the right shoulder, not rear-left. The truck is stationary or moving slowly, creating a physical constraint and reducing available drivable space on the right. The front-right region is not safely drivable due to the truck's presence. A right lane change is not feasible and would be a high-risk maneuver. The environment is moderately dense with buildings and trees, but the immediate constraint is the truck ahead on the right.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "84ed1a4ff4ac45c28910933efb54b45f", "source_group_file": "scene_250/group_006_vehicle_annotations.json", "id": "SU-7", "group_id": "group_006", "object_reference": "the vehicle front-right at about 54m", "scene_id": "scene_250", "ground_truth": "The ego vehicle is in a moderately dense environment with a yellow utility box located front-right at about 54m, which is a fixed obstacle and poses a low risk. The surrounding area is safely drivable in all directions, and the ego vehicle has sufficient clearance to continue straight or change lanes on either side. Given the spatial configuration, maintaining the current lane is the most geometrically feasible maneuver.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "f5e7b1de1b254449a2d79a87ccb8a642", "source_group_file": "scene_255/group_002_vehicle_annotations.json", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle front-left at about 38m", "scene_id": "scene_255", "ground_truth": "The ego vehicle is approaching an intersection with a crosswalk and traffic signals. A dark-colored sedan is visible front-left at approximately 38m, stopped at the intersection. The vehicle is not a direct obstacle but is in the path of potential left-turn or straight-through movement. The drivable space is constrained by the crosswalk, traffic signals, and a construction barrier on the left. The risk level is moderate due to the presence of pedestrians and other vehicles at the intersection. Feasible maneuvers include stopping at the intersection, proceeding straight if the light is green, or making a left turn after yielding to cross traffic and pedestrians.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "f8f3ce34af684bed950d4b27249d0c32", "source_group_file": "scene_264/group_004_vehicle_annotations.json", "id": "SU-7", "group_id": "group_004", "object_reference": "the vehicle front-right at about 32m", "scene_id": "scene_264", "ground_truth": "The ego vehicle is in a congested urban environment with multiple vehicles occupying both adjacent lanes. The vehicle front-right at about 32m is a dark-colored SUV, and the lane to the right is occupied by a line of vehicles, including a silver pickup truck, making a right lane change infeasible. The left lane is also occupied by a black sedan. Drivable space is limited to the current lane, and the risk level is moderate due to close proximity to surrounding vehicles. The most geometrically feasible maneuver is to maintain the current lane and proceed cautiously.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "ca4121843f684ac6ac49e861c3bcfc71", "source_group_file": "scene_267/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle front-right at about 74m", "scene_id": "scene_267", "ground_truth": "The ego vehicle is on a multi-lane bridge with moderate traffic and pedestrians on the sidewalk to the right. The vehicle front-right at 74m is a white truck, positioned in the adjacent lane ahead, which is a potential obstacle. The scene includes a metal railing on the right, limiting lateral movement. The ego vehicle has limited clearance to change lanes to the right due to the truck and the railing, and lane changes to the left are possible but require checking for other vehicles. The risk level is moderate due to the presence of the truck and pedestrians. Feasible maneuvers include maintaining current lane, or changing left if safe, but not changing right.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "d58b0d3926ac4563b8214a6ccbfd523e", "source_group_file": "scene_294/group_006_vehicle_annotations.json", "id": "SU-7", "group_id": "group_006", "object_reference": "the vehicle rear-left at about 14m", "scene_id": "scene_294", "ground_truth": "The ego vehicle is traveling on an urban street with a clear forward lane. There are physical constraints from metal railings/curbs on the left side and parked or slow-moving vehicles along the right side, limiting lateral escape space. The highlighted white pickup truck is located to the rear-left, approximately 14 meters away, and is separated from the ego vehicle by the roadside barrier/curb area, placing it outside the immediate path and posing no immediate collision threat.\n\nRisk level is low to moderate due to the constrained street environment, presence of parked vehicles, and potential occlusions near buildings and intersections. Feasible maneuvers include continuing straight in the current lane, maintaining a safe speed and following distance, and avoiding unnecessary lane changes or leftward movement. A right lane change is not recommended due to the presence of parked vehicles and limited clearance on the right side.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "0e828bdf4e4c4e5baeb2869521295c9a", "source_group_file": "scene_305/group_005_vehicle_annotations.json", "id": "SU-7", "group_id": "group_005", "object_reference": "the vehicle front-left at about 87m", "scene_id": "scene_305", "ground_truth": "The ego vehicle is driving on a two-lane urban road with a sidewalk and trees on both sides. The vehicle front-left at about 87m is a parked car on the left side of the road, which does not obstruct the current lane. The drivable space is confined to the two lanes, with no available space for lane changes due to parked vehicles and curbs. The risk level is low, and the feasible maneuver is to continue straight in the current lane.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "264a8094ea8c41e580ecf2aafe59f017", "source_group_file": "scene_315/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 21m", "scene_id": "scene_315", "ground_truth": "The ego vehicle is in a moderately congested urban environment with multiple vehicles in adjacent lanes. The vehicle rear-left at about 21m is a white sedan, positioned in the left lane, not directly behind or to the immediate rear-left of the ego vehicle. There is no direct physical constraint from this vehicle, but the ego vehicle is in a multi-lane road with traffic ahead and to the sides. The front-right region is occupied by a black sedan, making a right lane change currently infeasible due to proximity and lack of clearance. The risk level is moderate due to surrounding traffic, and feasible maneuvers include continuing straight or changing lanes to the left if safe.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "8e2311257eef424cbd58e3235dbcb8f6", "source_group_file": "scene_319/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle rear-right at about 16m", "scene_id": "scene_319", "ground_truth": "The ego vehicle is in a dense urban environment with construction barriers and traffic cones on both sides of the road. The vehicle rear-right at about 16m is a parked car, which is a physical constraint. The drivable space is limited to the current lane, with no safe clearance for a left lane change due to the presence of the parked vehicle and construction barriers. The risk level is moderate to high due to the constrained space and ongoing construction. Feasible maneuvers include proceeding straight with caution or stopping if required by traffic conditions.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "afee4857b56242bda095714d8453dacc", "source_group_file": "scene_344/group_003_vehicle_annotations.json", "id": "SU-7", "group_id": "group_003", "object_reference": "the vehicle in front at about 4m", "scene_id": "scene_344", "ground_truth": "The ego vehicle is stopped in traffic behind a dark gray sedan at approximately 4 meters. The scene is a multi-lane urban road with moderate congestion. The vehicle directly ahead is in the same lane, and there is no clear, safe space to perform a left lane change due to the presence of a red pickup truck in the adjacent lane. The front-left area is occupied, and the risk level is moderate due to close proximity to the vehicle ahead. Feasible maneuvers include waiting for the vehicle ahead to move or following it cautiously.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "63e01e4aa93c4c8e80c2f07d807f98fc", "source_group_file": "scene_360/group_002_vehicle_annotations.json", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle front-right at about 37m", "scene_id": "scene_360", "ground_truth": "The ego vehicle is in a constrained urban environment with a physical barrier (black metal railing) visible at the front-right at about 37m, which poses a moderate risk. The drivable space is limited to the current lane and adjacent lane to the left, with no safe clearance to change lanes to the right. The geometrically feasible maneuver is to maintain lane and proceed with caution, as the right-side path is obstructed by the railing and sidewalk.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "e141d4d2afec47919a96b54c375f9b2a", "source_group_file": "scene_380/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle front-right at about 34m", "scene_id": "scene_380", "ground_truth": "The ego vehicle is driving on a two-lane road with parked cars on the right side and a chain-link fence on the left. The vehicle front-right at 34m is a silver SUV parked parallel to the road, creating a physical constraint that limits lane change to the right. The drivable space is constrained to the current lane, with moderate risk due to proximity to parked vehicles. The ego vehicle can safely maintain its lane but should avoid right lane changes. The environment is urban with multi-story buildings in the background under overcast skies.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "77ed5b970eb04e67aaf1774b37d04a9b", "source_group_file": "scene_401/group_005_vehicle_annotations.json", "id": "SU-7", "group_id": "group_005", "object_reference": "the vehicle rear-right at about 40m", "scene_id": "scene_401", "ground_truth": "The ego vehicle is stationary in a parking lot. The vehicle rear-right at about 40m is a dark-colored sedan, visible through a chain-link fence with barbed wire, indicating a physical barrier. This vehicle is not in the ego's direct path but is adjacent to a constrained area. The environment is spatially dense with parked vehicles and fencing, limiting drivable space. The presence of the fence and closely parked vehicles on the right side creates a high risk for right-side maneuvers. Feasible maneuvers are limited to forward movement or slight left adjustments, with no safe lane change to the right due to the barrier and proximity of the vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "3db11f9b14c149fbb7960c63be75a53b", "source_group_file": "scene_407/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle front-left at about 45m", "scene_id": "scene_407", "ground_truth": "The ego vehicle is approaching a signalized urban intersection on a multi-lane road, with traffic ahead slowing or stopped due to red traffic lights. The vehicle directly ahead is a white sedan, and a large white bus is in the right lane, occupying the adjacent lane and reducing lateral clearance. The highlighted object at approximately 45m in the front-left is a small, yellow construction barricade or traffic control device positioned near the left edge of the roadway, adjacent to a concrete barrier and construction zone, which constrains the leftmost drivable area.\n\nPhysical constraints include the construction barricade and cones on the left, the lead vehicle ahead, the bus in the right lane, lane markings, and roadside railings. The center lane remains drivable, but space is limited by the stopped traffic and the red light. The risk level is moderate to high due to the intersection control, proximity of vehicles, and the presence of construction elements.\n\nFeasible maneuvers: continue straight slowly in the current lane, maintain a safe following distance, prepare to stop for the red light, and avoid drifting left toward the barricade. Lane changes are not recommended at this time due to the bus in the right lane and the construction barrier on the left.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "16dc6a01de4f4d1aa21da0a247f0002d", "source_group_file": "scene_416/group_004_vehicle_annotations.json", "id": "SU-7", "group_id": "group_004", "object_reference": "the vehicle in front at about 89m", "scene_id": "scene_416", "ground_truth": "Ego is on a straight, narrow two-way industrial street, centered in its lane. The drivable path ahead is mostly open, but constrained by a double yellow centerline on the left and a curb, fence, and trees on the right. Large trucks and parked vehicles line both sides of the road, creating occlusions and reducing lateral clearance, particularly on the right where a chain-link fence and dense vegetation are present.\n\nThe target object (a white vehicle) is far ahead in the ego lane, near the lane center, at approximately 89m. Immediate collision risk is low, but the narrow roadway, limited escape space, and roadside obstacles contribute to a low-to-moderate overall risk level.\n\nFeasible maneuvers: continue straight at a cautious speed, maintain lane position and following distance, and be prepared to slow or stop if the target vehicle or any other obstacle ahead becomes obstructive. Overtaking or crossing the centerline is not advisable unless the opposite lane is clearly free and safe to enter. Lane changes are not recommended due to the narrow width and proximity of parked vehicles and fences on both sides.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "b7b0fdfac8da471586818a95e443fc51", "source_group_file": "scene_430/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle front-left at about 78m", "scene_id": "scene_430", "ground_truth": "The ego vehicle is approaching an urban intersection with traffic signals and a crosswalk ahead. The road is flanked by multi-story buildings, curbs, and parked vehicles on both sides. The drivable space is confined to the center of the lane, with parked cars on the right and a sidewalk/curb on the left. There is no physical barrier or obstruction in the ego vehicle’s path; the object referenced as \"front-left at about 78m\" is a distant vehicle, which does not impede movement and poses negligible risk. The intersection presents moderate risk due to traffic signals, potential cross-traffic, and pedestrian crossings. Feasible maneuvers include slowing down, stopping at the intersection if the signal is red, or proceeding straight when the light turns green. Lane changes are not recommended due to parked vehicles on the right and limited space on the left. The most appropriate action is to remain in the current lane and follow traffic signal indications.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "8da6643cb80444bd9b06ed2d625fc50b", "source_group_file": "scene_472/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle front-left at about 33m", "scene_id": "scene_472", "ground_truth": "The ego vehicle is on a city street, not in a parking lot. The vehicle front-left at about 33m is a white SUV parked on the side of the road, which is a static obstacle and does not pose a dynamic risk. The scene is not highly congested; there is a clear lane ahead and to the right. The left lane is occupied by parked cars, making a left lane change infeasible. The right lane is clear, allowing for a safe right lane change. Maintaining the current lane is geometrically feasible and safe.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "f766fe867c824fd89678b2499fddf3b9", "source_group_file": "scene_502/group_006_vehicle_annotations.json", "id": "SU-7", "group_id": "group_006", "object_reference": "the vehicle rear-left at about 31m", "scene_id": "scene_502", "ground_truth": "The ego vehicle is in a constrained urban environment with active construction on the right, including barriers and fencing. The vehicle rear-left at about 31m is a dark SUV, positioned closely in the adjacent lane, limiting safe lane change options. The scene is overcast and wet, with visible traffic and construction, increasing risk. Drivable space is narrow, primarily forward and slightly left; lane changes to the right are unsafe due to barriers and the adjacent vehicle. The ego vehicle should maintain current lane and proceed cautiously.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "f2b00e92efaf4542b4ba6179f2454ed3", "source_group_file": "scene_506/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle in front at about 36m", "scene_id": "scene_506", "ground_truth": "Ego vehicle is on a wet urban bridge/roadway, flanked by concrete barriers and curbs, with tall buildings close on both sides. The target vehicle is ahead in the same forward direction, positioned near the center of the lane, leaving some drivable space but requiring caution due to reduced visibility from rain and road spray. Adjacent lanes appear mostly clear, though lateral movement is constrained by the barriers and limited visibility.\n\nRisk level is moderate due to slippery pavement, limited lateral escape space, and the presence of the vehicle ahead. Feasible maneuvers include continuing straight at a reduced speed, maintaining or increasing following distance, and braking smoothly if necessary. A cautious lane change may be possible if the adjacent lane is confirmed clear, but abrupt steering or hard braking should be avoided to prevent loss of control.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "1048644abfc34d7b8282df270dfbb18c", "source_group_file": "scene_581/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle front-right at about 32m", "scene_id": "scene_581", "ground_truth": "The ego vehicle is in a dense urban environment with wet roads and reduced visibility due to rain. A dark-colored sedan is visible directly ahead in the same lane, not to the front-right at 32m. The road ahead is occupied by this vehicle, and there are no clear indications of lane markings or open space to the right. The presence of a crosswalk and traffic signals ahead suggests the ego vehicle is approaching an intersection. Given the wet conditions and limited visibility, the risk level is high, and the most feasible maneuver is to slow down and stop at the intersection. Lane changes are not safe due to the proximity of the vehicle ahead and the lack of visible clearance.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "118250da8ccf44f4961b4a7cc2deb579", "source_group_file": "scene_631/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle front-right at about 40m", "scene_id": "scene_631", "ground_truth": "The ego vehicle is in a moderately dense urban environment with wet road conditions. A construction barrier is visible front-right at about 40m, not a parked vehicle. This barrier imposes a physical constraint, reducing available drivable space. Lane changes are not feasible due to the barrier and adjacent construction zone. The current lane is the only safe and geometrically feasible path forward. Risk level is moderate due to reduced visibility and wet pavement.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "bc6bda63e2744ba3911deb6470dd9058", "source_group_file": "scene_690/group_001_vehicle_annotations.json", "id": "SU-7", "group_id": "group_001", "object_reference": "the vehicle behind at about 34m", "scene_id": "scene_690", "ground_truth": "The ego vehicle is on a straight urban road with clear lane markings. The right side is physically constrained by a curb and grass verge, while the left side has an adjacent traffic lane. A vehicle is following behind in the same lane at approximately 34 meters, maintaining a moderate distance, with another vehicle visible further ahead and to the left.\n\nAvailable drivable space ahead within the current lane is sufficient, but the presence of rear traffic limits the feasibility of sudden braking or abrupt maneuvers. The right lane change is not safe due to the curb and lack of space, and the left lane change is also not safe as the adjacent lane contains active traffic and no clear gap is visible. The overall risk level is moderate due to the following vehicle and limited maneuverability.\n\nThe safest and most feasible maneuver is to continue straight, maintaining the current lane and speed smoothly. Any lane change or pull-over should be avoided until a safe opportunity arises with adequate clearance and visibility.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "3fa70c769a9d4b6c8b7ff1e2b43422b1", "source_group_file": "scene_700/group_008_vehicle_annotations.json", "id": "SU-7", "group_id": "group_008", "object_reference": "the vehicle rear-left at about 42m", "scene_id": "scene_700", "ground_truth": "The ego vehicle is approaching a T-junction with a visible traffic island and road markings. The vehicle rear-left at about 42m is a stationary orange traffic barrier, not a moving vehicle. The drivable space is constrained by the road geometry and the barrier, with no clearance for lane changes. The risk level is low as the barrier is static. Feasible maneuvers include proceeding straight or turning right, but not changing lanes.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "ce4fc86034ee4e3783af21c3932bb7a4", "source_group_file": "scene_706/group_006_vehicle_annotations.json", "id": "SU-7", "group_id": "group_006", "object_reference": "the vehicle front-right at about 11m", "scene_id": "scene_706", "ground_truth": "The ego vehicle is on a curved road with a clear drivable path ahead. To the front-right, there is a parked silver SUV at approximately 11 meters, which is a static obstacle, not a dynamic obstruction. The drivable space extends forward and to the left, with no immediate lane change restrictions. The risk level is low, as the obstacle is stationary and there is sufficient clearance. A left lane change is not required or feasible, as there is only one lane visible. The environment is not highly congested; it is a quiet residential or commercial area with parked vehicles along the side.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "3f1db7c6e5254431974a2a81fccdbb33", "source_group_file": "scene_714/group_007_vehicle_annotations.json", "id": "SU-7", "group_id": "group_007", "object_reference": "the vehicle front-right at about 28m", "scene_id": "scene_714", "ground_truth": "The ego vehicle is in a residential area with parked cars on the right and a road curve ahead. The vehicle front-right at about 28m is a dark-colored SUV parked in a lot, posing no immediate risk. The front-left region is drivable, but a left lane change is not feasible due to the road geometry and lack of a dedicated lane. The scene is safe for proceeding straight or turning right, with minimal risk.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "037d3ff6d78f408fb1402bf3aa74b4f8", "source_group_file": "scene_718/group_003_vehicle_annotations.json", "id": "SU-7", "group_id": "group_003", "object_reference": "the vehicle front-right at about 79m", "scene_id": "scene_718", "ground_truth": "The ego vehicle is on a narrow, curbed urban road with buildings and a sidewalk on the left and a vegetated embankment with a curb on the right. A small blue vehicle is ahead in the same lane at a moderate distance (approximately 79m), limiting forward speed. Drivable space is primarily confined to the current lane, with minimal shoulder or escape space due to curbs and roadside obstacles. Risk is low to moderate: there is no immediate collision threat, but the ego vehicle should monitor the lead vehicle and maintain a safe following distance. Feasible maneuvers include continuing straight and lane-following, with speed adjustments as needed; overtaking or lane changes are not recommended due to limited space and the presence of curbs and vegetation on both sides.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "0c58f3f9cdfd4940a8f361253cb30be4", "source_group_file": "scene_736/group_005_vehicle_annotations.json", "id": "SU-7", "group_id": "group_005", "object_reference": "the vehicle front-right at about 4m", "scene_id": "scene_736", "ground_truth": "The ego vehicle is on a straight urban road with moderate tree cover and adjacent buildings. A silver sedan is visible in the front-right at approximately 4m, traveling in the same direction and not obstructing the ego vehicle's path. The road ahead is clear with no immediate physical constraints. The front-left lane is also clear and drivable. The risk level is low, and a left lane change is feasible if needed, but no immediate obstruction or risk exists from the front-right vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe the overall spatial situation of the ego vehicle in the scene, including existing physical constraints, available drivable space, risk level, and feasible maneuvers.", "task": "space-understanding", "object_id": "6a0c83fb056147bba6369fcb86c5bd62", "source_group_file": "scene_831/group_002_vehicle_annotations.json", "id": "SU-7", "group_id": "group_002", "object_reference": "the vehicle front-left at about 42m", "scene_id": "scene_831", "ground_truth": "The ego vehicle is in a dark, narrow residential street at night with wet pavement, illuminated by sparse streetlights. A physical constraint exists at approximately 42m front-left: a stationary vehicle or object (possibly a parked car or barrier) is visible, partially obstructing the left lane. Drivable space is limited to the center and right portions of the road. The risk level is moderate due to low visibility, wet conditions, and the presence of an obstacle. Feasible maneuvers include proceeding cautiously in the current lane or slight right adjustment, but lane changes are not safe due to the obstacle and limited visibility.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "8392bb721640483b9c2fdaae060e7503", "source_group_file": "scene_014/group_007_vehicle_annotations.json", "id": "TE-1", "group_id": "group_007", "object_reference": "the vehicle front-right at about 65m", "scene_id": "scene_014", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Stop", "D": "Turn left", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "ec498acfa2a54e21bab15af639eb4d90", "source_group_file": "scene_020/group_003_vehicle_annotations.json", "scene_description": "", "id": "TE-1", "group_id": "group_003", "object_reference": "the vehicle front-left at about 36m", "scene_id": "scene_020", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Cross ego path", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "bfff988da17f4e8ab72d69c503acdcf2", "source_group_file": "scene_046/group_007_vehicle_annotations.json", "scene_description": "", "id": "TE-1", "group_id": "group_007", "object_reference": "the vehicle front-left at about 38m", "scene_id": "scene_046", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Stop", "C": "Turn left", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "92dbda9a6cb74cf9bdbcffc75f08818e", "source_group_file": "scene_068/group_001_vehicle_annotations.json", "scene_description": "", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle front-right at about 55m", "scene_id": "scene_068", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Cross ego path", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "bb91895a0f3e4b3eafdc29af853ee039", "source_group_file": "scene_078/group_006_vehicle_annotations.json", "scene_description": "", "id": "TE-1", "group_id": "group_006", "object_reference": "the vehicle front-right at about 39m", "scene_id": "scene_078", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Continue straight", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "1d80d7e9cbbb459a90a25fa0f8ca2d6b", "source_group_file": "scene_176/group_001_vehicle_annotations.json", "scene_description": "", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle front-left at about 35m", "scene_id": "scene_176", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Stop", "D": "Continue straight", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "3d2dcb5dc3e44794a6e81cdcdfc90fab", "source_group_file": "scene_177/group_004_vehicle_annotations.json", "scene_description": "", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle front-left at about 25m", "scene_id": "scene_177", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Cross ego path", "C": "Turn left", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "a1be8e64da3745d79ebb6dcb449508fc", "source_group_file": "scene_239/group_008_vehicle_annotations.json", "scene_description": "", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle front-left at about 54m", "scene_id": "scene_239", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Turn left", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "89aa0b7cc19b4bf9a61ad038d4525acb", "source_group_file": "scene_282/group_007_vehicle_annotations.json", "id": "TE-1", "group_id": "group_007", "object_reference": "the vehicle rear-left at about 20m", "scene_id": "scene_282", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Stop", "B": "Continue straight", "C": "Turn left", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "0e6f6c47cd294807bf03bf1a1b22d34b", "source_group_file": "scene_310/group_003_vehicle_annotations.json", "id": "TE-1", "group_id": "group_003", "object_reference": "the vehicle front-left at about 69m", "scene_id": "scene_310", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn right", "B": "Continue straight", "C": "Turn left", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "78a0ce970cdb486eab25fe1f90fe94c8", "source_group_file": "scene_311/group_007_vehicle_annotations.json", "id": "TE-1", "group_id": "group_007", "object_reference": "the vehicle front-left at about 36m", "scene_id": "scene_311", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Stop", "D": "Cross ego path", "E": "Continue straight" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "bd96ed4a8f374df9a5bfcf6c987e64b5", "source_group_file": "scene_317/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle front-left at about 54m", "scene_id": "scene_317", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Turn left", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "86493908bc64465fa25585397874b295", "source_group_file": "scene_325/group_003_vehicle_annotations.json", "id": "TE-1", "group_id": "group_003", "object_reference": "the vehicle front-left at about 21m", "scene_id": "scene_325", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Continue straight", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "4ff4ab6871614c9daa17db4992a9b720", "source_group_file": "scene_341/group_003_vehicle_annotations.json", "id": "TE-1", "group_id": "group_003", "object_reference": "the vehicle front-left at about 74m", "scene_id": "scene_341", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Cross ego path", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "54879873de984e0c9d1dfbedaf26ef5a", "source_group_file": "scene_366/group_005_vehicle_annotations.json", "id": "TE-1", "group_id": "group_005", "object_reference": "the vehicle front-right at about 17m", "scene_id": "scene_366", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Cross ego path", "B": "Continue straight", "C": "Turn left", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "818efb1e3297499dbe4daafc432d0dcd", "source_group_file": "scene_370/group_001_vehicle_annotations.json", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle front-right at about 27m", "scene_id": "scene_370", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "51deb8d3d5e14e6aadff8edb5cb697ae", "source_group_file": "scene_378/group_005_vehicle_annotations.json", "id": "TE-1", "group_id": "group_005", "object_reference": "the vehicle front-right at about 32m", "scene_id": "scene_378", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Stop", "D": "Cross ego path", "E": "Turn left" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "cf8d598f85b9411c8f62abdf9d7fe24a", "source_group_file": "scene_384/group_008_vehicle_annotations.json", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle in front at about 28m", "scene_id": "scene_384", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "ec0bf3bba84e4bb98beb584debc79df3", "source_group_file": "scene_398/group_007_vehicle_annotations.json", "id": "TE-1", "group_id": "group_007", "object_reference": "the vehicle rear-left at about 13m", "scene_id": "scene_398", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Stop", "D": "Turn left", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "81a03208de0c4590ada71d78c1af39b0", "source_group_file": "scene_433/group_005_vehicle_annotations.json", "id": "TE-1", "group_id": "group_005", "object_reference": "the vehicle in front at about 62m", "scene_id": "scene_433", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Cross ego path", "B": "Continue straight", "C": "Turn left", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "fd2f1585dd4d4ac792d0fea3a28f517f", "source_group_file": "scene_436/group_008_vehicle_annotations.json", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle front-right at about 56m", "scene_id": "scene_436", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Stop", "D": "Cross ego path", "E": "Turn left" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "6baaea74f39c4ae4a900bedf60418aa8", "source_group_file": "scene_451/group_001_vehicle_annotations.json", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle front-left at about 14m", "scene_id": "scene_451", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Stop", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "8ee2c8702def499c9cbcfce42769f4ab", "source_group_file": "scene_452/group_001_vehicle_annotations.json", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle front-right at about 86m", "scene_id": "scene_452", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Stop", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "f9bc2742906a4b649e7d6dcfca12c0ba", "source_group_file": "scene_453/group_001_vehicle_annotations.json", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 36m", "scene_id": "scene_453", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "849f05fa6f384daea39b9c74bfde9a7c", "source_group_file": "scene_456/group_003_vehicle_annotations.json", "id": "TE-1", "group_id": "group_003", "object_reference": "the vehicle behind at about 17m", "scene_id": "scene_456", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Stop", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "b10b105be88d4170b07dda3dc75511ac", "source_group_file": "scene_482/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle rear-right at about 49m", "scene_id": "scene_482", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Continue straight", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "fd84bfe65ade4213b151e5dd1dd2c9cf", "source_group_file": "scene_485/group_002_vehicle_annotations.json", "id": "TE-1", "group_id": "group_002", "object_reference": "the vehicle rear-left at about 36m", "scene_id": "scene_485", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Turn left", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "ddde67f266fe421ea8239d9ea37de6fe", "source_group_file": "scene_510/group_001_vehicle_annotations.json", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle front-left at about 26m", "scene_id": "scene_510", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Stop", "D": "Continue straight", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "af0d0b0438f64b17bc9e5ea7b08bfbab", "source_group_file": "scene_517/group_008_vehicle_annotations.json", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle behind at about 21m", "scene_id": "scene_517", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Stop", "C": "Turn left", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "3ae1e9f5cfff4301ba3afd7c2bcfa04d", "source_group_file": "scene_534/group_008_vehicle_annotations.json", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 11m", "scene_id": "scene_534", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Stop", "C": "Turn left", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "bb3eb223860a44abababd8d4cfea69f4", "source_group_file": "scene_560/group_008_vehicle_annotations.json", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle rear-left at about 42m", "scene_id": "scene_560", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Cross ego path", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "ae2ab149783a4cee9c15bdbea5cd6be3", "source_group_file": "scene_575/group_007_vehicle_annotations.json", "id": "TE-1", "group_id": "group_007", "object_reference": "the vehicle front-right at about 46m", "scene_id": "scene_575", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn right", "B": "Continue straight", "C": "Turn left", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "9bdb7a7cc5ff477e97d75fda6bff4f72", "source_group_file": "scene_576/group_005_vehicle_annotations.json", "id": "TE-1", "group_id": "group_005", "object_reference": "the vehicle rear-right at about 63m", "scene_id": "scene_576", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Continue straight", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "8d3ffa0ffacf4ccbb73cfb979a291a88", "source_group_file": "scene_595/group_007_vehicle_annotations.json", "id": "TE-1", "group_id": "group_007", "object_reference": "the vehicle in front at about 26m", "scene_id": "scene_595", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Continue straight", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "d4ae898b14d343fe872fff8a4df6e8bd", "source_group_file": "scene_598/group_005_vehicle_annotations.json", "id": "TE-1", "group_id": "group_005", "object_reference": "the vehicle behind at about 33m", "scene_id": "scene_598", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Cross ego path", "B": "Continue straight", "C": "Turn left", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "f31d0bd1ab1e4be283f185879019a67a", "source_group_file": "scene_617/group_006_vehicle_annotations.json", "id": "TE-1", "group_id": "group_006", "object_reference": "the vehicle front-right at about 26m", "scene_id": "scene_617", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Stop", "B": "Continue straight", "C": "Turn left", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "51ee6beca63c47feafea08d66c1fcc09", "source_group_file": "scene_628/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle rear-left at about 76m", "scene_id": "scene_628", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Stop", "D": "Cross ego path", "E": "Turn right" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "e9bbb59caf814fbd83a36900b91fb23e", "source_group_file": "scene_667/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle front-right at about 34m", "scene_id": "scene_667", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Turn left", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "e8e5a70e0c8e4c8fa94e4a90edfdf683", "source_group_file": "scene_677/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle rear-right at about 25m", "scene_id": "scene_677", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Stop", "D": "Cross ego path", "E": "Turn left" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "c8a0820dfdfb42fb8f69e30e3a7af0e4", "source_group_file": "scene_680/group_001_vehicle_annotations.json", "id": "TE-1", "group_id": "group_001", "object_reference": "the vehicle in front at about 32m", "scene_id": "scene_680", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Stop", "D": "Cross ego path", "E": "Continue straight" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "9fcd07d90dff4ddd82dcfffa0ad50e1e", "source_group_file": "scene_696/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle front-right at about 48m", "scene_id": "scene_696", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Stop", "D": "Turn right", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "6e996801f19d4ca18eadc7c749ebfd33", "source_group_file": "scene_733/group_002_vehicle_annotations.json", "id": "TE-1", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 27m", "scene_id": "scene_733", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Continue straight", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "bd9b3dd91a854b4f868dbe9c0eff675f", "source_group_file": "scene_734/group_006_vehicle_annotations.json", "id": "TE-1", "group_id": "group_006", "object_reference": "the vehicle behind at about 11m", "scene_id": "scene_734", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Cross ego path", "C": "Turn left", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "56d0ef328f3f468aad599ddcf2b4c5af", "source_group_file": "scene_758/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle rear-right at about 94m", "scene_id": "scene_758", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Cross ego path", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "085eed0af2ca4e1fbbe83b30aaedca5e", "source_group_file": "scene_789/group_005_vehicle_annotations.json", "id": "TE-1", "group_id": "group_005", "object_reference": "the vehicle front-right at about 20m", "scene_id": "scene_789", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "cae389bc27074d2b99fb1eaea1bdfe25", "source_group_file": "scene_797/group_008_vehicle_annotations.json", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle front-right at about 16m", "scene_id": "scene_797", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Cross ego path", "C": "Turn left", "D": "Turn right", "E": "Stop" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "bc5ab5cb540e4dd9a34472afeb6e79c0", "source_group_file": "scene_814/group_006_vehicle_annotations.json", "id": "TE-1", "group_id": "group_006", "object_reference": "the vehicle front-left at about 51m", "scene_id": "scene_814", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn left", "C": "Turn right", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "fa96e00a65874ef9bdf5085a5f8eaab1", "source_group_file": "scene_842/group_002_vehicle_annotations.json", "id": "TE-1", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 17m", "scene_id": "scene_842", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Continue straight", "B": "Turn right", "C": "Turn left", "D": "Stop", "E": "Cross ego path" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "75dc7c9e2ae94d7c8c4b30cc414db769", "source_group_file": "scene_848/group_008_vehicle_annotations.json", "id": "TE-1", "group_id": "group_008", "object_reference": "the vehicle front-right at about 8m", "scene_id": "scene_848", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Turn right", "C": "Stop", "D": "Cross ego path", "E": "Continue straight" }, "question": "What is the most likely future motion of the object in the red bounding box over the next few seconds?", "task": "time-extrapolation", "object_id": "f53bfde38e9f49158f410f3bdc6ff2cb", "source_group_file": "scene_849/group_004_vehicle_annotations.json", "id": "TE-1", "group_id": "group_004", "object_reference": "the vehicle in front at about 15m", "scene_id": "scene_849", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "581e1a246be04af78ae00f0fc41951ac", "source_group_file": "scene_006/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle rear-right at about 67m", "scene_id": "scene_006", "ground_truth": "The vehicle rear-right at about 67m is a stationary truck parked along the right side of the road. There is no indication of motion or intent to exit the roadway. The ego vehicle should continue forward at a safe speed, maintaining awareness of the parked truck and other vehicles in the area.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "6d4123bf5af348caacb843792eb2a307", "source_group_file": "scene_011/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle front-left at about 35m", "scene_id": "scene_011", "ground_truth": "The vehicle front-left is a stationary white trailer or utility vehicle parked along the curb, not a moving vehicle. There is no collision risk. The ego vehicle should continue driving straight at a safe speed, maintaining awareness of the parked vehicles and the road ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "b980427fe15b4486bbc7c8d9e9f8548c", "source_group_file": "scene_030/group_006_vehicle_annotations.json", "id": "TE-6", "group_id": "group_006", "object_reference": "the vehicle rear-right at about 61m", "scene_id": "scene_030", "ground_truth": "The object at 61m rear-right is not a vehicle, but a stationary construction barrier or sign located on the left side of the road, within the work zone. It will remain fixed as the ego vehicle moves forward. The scene is a construction zone with lane narrowing and barriers on both sides, under overcast lighting conditions. The ego vehicle should proceed cautiously, maintain a steady speed, stay centered in the available lane, and keep a safe distance from the barriers. It should be prepared to brake if any unexpected movement, such as a worker or equipment, emerges from the construction area. No other vehicles are visible in the rear-right at that distance, and the prediction of a vehicle stopping is incorrect based on the visual evidence.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "8de2cb4606a842f3afa4941a92f1a14d", "source_group_file": "scene_034/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle front-right at about 62m", "scene_id": "scene_034", "ground_truth": "The boxed object ahead is a yellow vehicle located in the right lane, approximately 62 meters from the ego vehicle. Based on its position and the surrounding environment, it is likely to remain stationary or proceed slowly in the same lane. The ego vehicle is approaching at a steady speed and will close the distance to this vehicle within the next 5–10 seconds.\n\nTo maintain safety, the ego vehicle should reduce speed gradually to establish a safe following distance. If the vehicle ahead remains stationary or moves slowly, the ego vehicle should prepare to stop or adjust speed accordingly to avoid a rear-end collision. The vehicle should not attempt to overtake or move left unless the lane is clear and safe, as the left side is bordered by curbs and pedestrian zones. Continuous monitoring of the vehicle’s behavior and surrounding traffic is essential.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "c6b20c2b5b50466fa84fd324055bc0ff", "source_group_file": "scene_062/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 55m", "scene_id": "scene_062", "ground_truth": "The scene shows a construction zone with traffic cones and barriers, but there is no visible vehicle rear-right at 55m. The road ahead is clear with no vehicles in the immediate vicinity. The ego vehicle should proceed cautiously, maintaining a safe speed and following the lane markings, while being alert for any sudden changes or workers in the area.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "f39c4ce17a194c50a206c5a2b5fa4c62", "source_group_file": "scene_064/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 27m", "scene_id": "scene_064", "ground_truth": "The vehicle rear-right at about 27m is a dark-colored sedan parked in a lot, not moving. There is no indication it will stop or change lanes. The ego vehicle should maintain its current course, as there is no dynamic threat. The scene will remain stable with no immediate changes.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "612fca25ccd742e6b8782d88a97a99d7", "source_group_file": "scene_072/group_005_vehicle_annotations.json", "id": "TE-6", "group_id": "group_005", "object_reference": "the vehicle rear-right at about 29m", "scene_id": "scene_072", "ground_truth": "There is no visible vehicle rear-right at about 29m in the provided images. The scene shows an empty road with traffic lights, trees, and buildings, but no vehicle in that position. The ego vehicle should proceed with caution, obeying traffic signals, and monitor for any approaching vehicles or pedestrians. No lane change is necessary at this time.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "cbdb4f8241234c19ab15fde291f1754b", "source_group_file": "scene_121/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 33m", "scene_id": "scene_121", "ground_truth": "The vehicle rear-left at about 33m is a silver sedan parked in a lot, not an active moving vehicle. There is no motion prediction to be made for a stationary object. The ego vehicle should continue its current trajectory without concern for this object, as it is not in motion and not in the ego vehicle's path. The scene context shows a parking lot, not heavy traffic with turning vehicles.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "1b46c9bf387f468091e9e4a8c659ed8d", "source_group_file": "scene_125/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle front-left at about 43m", "scene_id": "scene_125", "ground_truth": "The highlighted vehicle(s) on the left appear parked and are unlikely to move, but they are close to the travel lane. Ahead, traffic cones and a work area partially narrow the roadway, so the ego vehicle should continue straight with a reduced, steady speed, stay centered in the open lane, give extra clearance to the parked cars, and be ready to brake for any pedestrians, opening doors, or vehicles entering from the curb/work zone.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "eeeddb5f0d74471289957c7fe1d60884", "source_group_file": "scene_128/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle front-left at about 32m", "scene_id": "scene_128", "ground_truth": "The vehicle front-left at about 32m is a white sedan, currently in the intersection and moving forward. Given its position and the traffic light status (red), it is likely to stop or slow down. The ego vehicle should reduce speed and prepare to stop to maintain a safe following distance, as the intersection ahead is controlled by traffic signals.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "38341e4165fc4cc3a2c5bd2fd93b59b4", "source_group_file": "scene_133/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle in front at about 13m", "scene_id": "scene_133", "ground_truth": "The vehicle in front is a dark-colored sedan, positioned in the right lane, approximately 13m ahead. It is likely to continue straight. However, the ego vehicle should slow down due to the presence of pedestrians crossing the street at the intersection ahead. The traffic light is green, but pedestrian crossing activity requires caution. The ego vehicle should maintain a safe following distance and be prepared to stop if necessary. No collision is predicted if the ego vehicle reduces speed appropriately.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "5d3bcc5425f5486386c7377dc08bffb9", "source_group_file": "scene_157/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle in front at about 36m", "scene_id": "scene_157", "ground_truth": "The vehicle in front is a blue SUV, positioned in the adjacent lane to the right, not directly ahead. It is not on a collision course with the ego vehicle. The ego vehicle should continue forward cautiously, maintaining awareness of the surrounding parked vehicles and the bus on the left. No immediate action is required beyond standard cautious driving in a parking lot environment.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "ca4cb9255fde4715a3e82813a5c003bd", "source_group_file": "scene_162/group_004_vehicle_annotations.json", "id": "TE-6", "group_id": "group_004", "object_reference": "the vehicle front-right at about 48m", "scene_id": "scene_162", "ground_truth": "The vehicle front-right at about 48m is a distant white car on the right side of the road, moving away from the ego vehicle. There is no visual evidence that it is stopping or on a collision course. The ego vehicle is traveling straight on a multi-lane road, not at an intersection preparing to turn left. The most likely evolution is continued forward motion with no immediate interaction. The ego vehicle should maintain its current speed and lane position, monitoring for any changes in traffic ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "822d8f6b54364af5a6f0da52a442a0dc", "source_group_file": "scene_203/group_003_vehicle_annotations.json", "id": "TE-6", "group_id": "group_003", "object_reference": "the vehicle front-right at about 59m", "scene_id": "scene_203", "ground_truth": "The vehicle front-right at about 59m is a dark-colored sedan moving forward in the adjacent lane, not stopping. The ego vehicle is approaching an intersection and should prepare to yield or stop if required by traffic signals or signs, as the other vehicle is not indicating a stop. The scene shows a typical urban intersection with no immediate collision risk, but the ego vehicle must remain alert to traffic flow and signals.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "ce680919d60f4eed86fca0d0d30b2330", "source_group_file": "scene_214/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 86m", "scene_id": "scene_214", "ground_truth": "The highlighted object is a small yellow vehicle, likely a utility or service car, located on the grassy area to the rear-left of the ego vehicle, approximately 86 meters away. It is stationary and positioned off the drivable path, near a sidewalk and trees, with no indication of imminent movement onto the roadway.\n\nOver the next few seconds, the object is expected to remain stationary as the ego vehicle continues forward. As the vehicle progresses, the object will gradually move out of the rear-left camera’s field of view due to the ego vehicle’s forward motion and the curvature of the road.\n\nThe ego vehicle should maintain its current speed and path, ensuring normal clearance from the curb and sidewalk. It should continue to monitor the surrounding environment, particularly for pedestrians or cyclists near the sidewalk and trees, as these areas are adjacent to the roadway. No evasive action is required for the highlighted object, as it poses no immediate hazard.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "2e7451ea058f4091be32aea005ca5a9c", "source_group_file": "scene_229/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 96m", "scene_id": "scene_229", "ground_truth": "The scene shows a clear, straight road with no visible vehicle at 96m in the rear-right. There is no offloading truck or parked bicycle in the visible frames. The ego vehicle should continue straight, maintaining awareness of the road ahead and potential cross-traffic at intersections, as the environment is static with no immediate dynamic threats.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "f085b0a65c5a400f986d9a6cd33c41ac", "source_group_file": "scene_244/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle in front at about 23m", "scene_id": "scene_244", "ground_truth": "The vehicle in front is a blue car driving in the same direction, not stopping. The ego vehicle is on a two-way road with oncoming traffic, but there is no indication of a give-way situation or need to stop. The most appropriate action is to maintain a safe following distance and continue driving cautiously. There is no need to change lanes as the road appears clear ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "4ef26c35cfc34618955ad3816c6fe4f6", "source_group_file": "scene_254/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle front-left at about 17m", "scene_id": "scene_254", "ground_truth": "The vehicle front-left at about 17m is not visible in the provided frames. The frames show a city street with parked and moving vehicles, but no specific vehicle at that position and distance is clearly identifiable as the target. The ego vehicle should continue monitoring the road ahead, maintain a safe speed, and be prepared for potential changes in traffic flow, such as vehicles merging or stopping at intersections. There is no evidence of a vehicle stopping in the current lane, so no specific deceleration is required at this time.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "913dbbe6894c42878fa971d4d6c59d4a", "source_group_file": "scene_274/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle front-left at about 42m", "scene_id": "scene_274", "ground_truth": "The boxed object appears to be a parked or stationary vehicle near the curb/driveway on the rear-left side. As the ego vehicle continues forward, it will likely recede and move out of view, with no immediate conflict unless it suddenly pulls out.\n\nThe ego vehicle should maintain its lane and speed, avoid any abrupt leftward movement or lane change, and continue checking the rear-left blind spot/mirror. Be prepared to slow or yield if the vehicle starts moving into the roadway.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "400207b44db24c17a78bdbfc5b429a4d", "source_group_file": "scene_290/group_006_vehicle_annotations.json", "id": "TE-6", "group_id": "group_006", "object_reference": "the vehicle rear-left at about 25m", "scene_id": "scene_290", "ground_truth": "The vehicle rear-left at about 25m is a stationary parked car, not moving. There is no predicted lane change or path crossing. The ego vehicle should maintain its current course and monitor the surroundings, as no immediate action is required. The construction zone requires caution, but no specific maneuver is necessary based on the current scene.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "06ae19f8841e4edfb0bfc67201b05c13", "source_group_file": "scene_301/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle front-left at about 53m", "scene_id": "scene_301", "ground_truth": "The vehicle front-left at about 53m is a dark-colored sedan, currently traveling in the same direction as the ego vehicle. There is no visual evidence to suggest it will stop; it appears to be moving forward in traffic. The ego vehicle should maintain its current speed and lane position, monitoring the surrounding traffic for any changes. No immediate action is required to maintain safety.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "4f333888e71743cea8b446dbc6f65ef4", "source_group_file": "scene_320/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle in front at about 44m", "scene_id": "scene_320", "ground_truth": "The vehicle in front is a dark SUV traveling straight ahead in the same lane, not turning or merging. The ego vehicle should maintain its current speed and lane position, keeping a safe following distance. The scene includes construction barriers and traffic cones on the right, but no immediate lane changes or merging are expected from the lead vehicle.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "467ed0fcb72e4f79a2a20014abd8704b", "source_group_file": "scene_326/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle rear-left at about 63m", "scene_id": "scene_326", "ground_truth": "The vehicle rear-left at about 63m is a yellow taxi, visible in the CAM_BACK frame, traveling in the same direction as the ego vehicle. It is positioned in the adjacent lane and appears to be maintaining a steady course. The ego vehicle should maintain its current speed and lane position, ensuring a safe following distance. Given the presence of pedestrians on the bridge walkway and the urban environment, the ego vehicle should remain vigilant for sudden movements or changes in traffic flow, but no immediate evasive action is required.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "174b0feb2b974af7af00f1476ad50b91", "source_group_file": "scene_331/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle front-left at about 60m", "scene_id": "scene_331", "ground_truth": "The vehicle front-left at about 60m is a black sedan, visible in the foreground of the CAM_FRONT_LEFT view. It is not 60m away but is very close to the ego vehicle, likely in the adjacent lane. The vehicle appears to be moving forward with the flow of traffic, not stopping. The ego vehicle should maintain a safe following distance and be prepared for lane changes or braking if the vehicle ahead slows. There is no indication of a stop at an intersection in this view; the traffic light is not visible for this vehicle’s direction. The most likely evolution is continued forward motion in the same lane.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "f347e18bec384d2091f650e757f8cbd6", "source_group_file": "scene_347/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle front-left at about 32m", "scene_id": "scene_347", "ground_truth": "The vehicle front-left at about 32m is not visible in the current frame. The red bounding box in the query image highlights a pedestrian on the sidewalk, not a vehicle. Therefore, the ego vehicle should continue monitoring the environment, especially the crosswalk and sidewalk, for any potential pedestrian movement, but no immediate action is required from the vehicle in the current lane.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "37f8b92d51264b23af17bb3c62ebbd24", "source_group_file": "scene_364/group_003_vehicle_annotations.json", "id": "TE-6", "group_id": "group_003", "object_reference": "the vehicle behind at about 46m", "scene_id": "scene_364", "ground_truth": "The vehicle behind is located approximately 46 meters back in the same lane, within a narrowed construction corridor. It is likely to continue straight ahead, following the flow of traffic through the work zone. Given the current spacing and the absence of immediate lane changes or braking from the following vehicle, it may close the gap slightly if the ego vehicle slows or stops for traffic ahead.\n\nThe ego vehicle should maintain a steady speed and lane position, avoiding sudden braking or lane changes. It should continue to monitor the rear camera and mirrors for any changes in the following vehicle’s behavior. If slowing or stopping becomes necessary, the ego vehicle should brake smoothly and signal early to ensure the following vehicle has sufficient time to react. The construction zone and surrounding traffic conditions require cautious, predictable driving to maintain safety.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "76a45bce30134ec8bab9251d3e55de45", "source_group_file": "scene_403/group_005_vehicle_annotations.json", "id": "TE-6", "group_id": "group_005", "object_reference": "the vehicle front-right at about 84m", "scene_id": "scene_403", "ground_truth": "The highlighted object appears to be a parked vehicle behind a chain-link fence in a lot to the front-right. It is likely to remain stationary and separated from the roadway, so it should not directly affect the ego vehicle’s path.\n\nThe ego vehicle should continue in its lane at a safe, steady speed, keep normal clearance from the right side, and monitor for any vehicles or pedestrians that could emerge from a driveway or gate near the fenced lot. No braking or evasive maneuver is needed unless new movement appears.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "998b85d42b754835bfdfa67c89cd72c5", "source_group_file": "scene_408/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle front-right at about 44m", "scene_id": "scene_408", "ground_truth": "The vehicle front-right at about 44m is a white bus, which appears to be moving forward in the same lane as the ego vehicle. There is no visual evidence that it is stopping or that a collision is imminent. The ego vehicle should maintain its current speed and lane position, monitoring the bus for any changes in behavior. The scene shows a multi-lane urban road with no immediate obstacles or traffic signals requiring a stop.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "0ddb1ec3164b4ba4801c8ce1c15179c9", "source_group_file": "scene_418/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle front-left at about 37m", "scene_id": "scene_418", "ground_truth": "The vehicle front-left at about 37m is a silver SUV parked on the right side of the road, not a moving vehicle. There is no indication it will stop or move. The ego vehicle should continue driving cautiously, maintaining awareness of the parked vehicles and the crosswalk ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "e0d2b3c2dbd741109d4814764cfcb2c6", "source_group_file": "scene_419/group_004_vehicle_annotations.json", "id": "TE-6", "group_id": "group_004", "object_reference": "the vehicle in front at about 68m", "scene_id": "scene_419", "ground_truth": "The vehicle in front is a dark-colored sedan, not a construction zone. There is no visible construction ahead. The ego vehicle should maintain a safe following distance and be prepared to brake if the sedan decelerates, as the road ahead is clear and no immediate obstacles are present.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "2d284b4ded86452db0cff52ed848a020", "source_group_file": "scene_431/group_002_vehicle_annotations.json", "id": "TE-6", "group_id": "group_002", "object_reference": "the vehicle front-left at about 29m", "scene_id": "scene_431", "ground_truth": "The scene shows a parking lot entrance with a traffic officer directing vehicles. The vehicle front-left at 29m is not clearly visible or identifiable in the provided frames. There is no evidence of a stop command or a need for the ego vehicle to change lanes. The ego vehicle should proceed cautiously, following the traffic officer's signals and maintaining a safe distance from other vehicles.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "aaeb7549c19c40c5905ca149f2a10d6b", "source_group_file": "scene_440/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle rear-right at about 19m", "scene_id": "scene_440", "ground_truth": "The vehicle rear-right at about 19m is a white sedan, which appears to be moving forward in its lane. The ego vehicle should maintain its current lane and speed, as there is no immediate hazard. The construction zone ahead requires caution, but no lane change is necessary unless traffic conditions dictate otherwise.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "2b1c6ba5cb5f454a9b8493b4c616b06d", "source_group_file": "scene_450/group_004_vehicle_annotations.json", "id": "TE-6", "group_id": "group_004", "object_reference": "the vehicle front-right at about 63m", "scene_id": "scene_450", "ground_truth": "The vehicle front-right at about 63m is a stationary black sedan parked along the curb, not moving. It is not likely to stop or change lanes. The ego vehicle should continue driving normally, maintaining its current speed and lane, as there is no immediate collision risk.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "bd0353dc36054f9ca01b13d2ddf5f435", "source_group_file": "scene_518/group_005_vehicle_annotations.json", "id": "TE-6", "group_id": "group_005", "object_reference": "the vehicle front-left at about 30m", "scene_id": "scene_518", "ground_truth": "The vehicle front-left at about 30m is a white sedan parked along the curb. It is stationary and not in motion. The ego vehicle should continue driving straight ahead, maintaining its lane and speed, as there is no immediate threat. The rainy conditions require increased caution, but no evasive maneuvers are necessary.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "3db9920276834c5e9ada5056e39f8edf", "source_group_file": "scene_520/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle front-left at about 15m", "scene_id": "scene_520", "ground_truth": "The vehicle front-left at about 15m is a parked black SUV, not moving. There is no indication of imminent collision or need for lane change. The ego vehicle should maintain current trajectory, observing wet road conditions and construction zone ahead. No motion prediction is available or necessary for a stationary object.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "b30e974d75c4459b89af3f59867f2def", "source_group_file": "scene_521/group_004_vehicle_annotations.json", "id": "TE-6", "group_id": "group_004", "object_reference": "the vehicle front-left at about 66m", "scene_id": "scene_521", "ground_truth": "In the next few seconds, the ego vehicle will likely proceed through the wet intersection while the small target ahead remains distant and may move slowly along or across the roadway. Visibility is reduced by rain and water on the camera, and the road surface is slippery.\n\nThe ego vehicle should continue cautiously, maintain a low speed, keep extra following/stopping distance, monitor the target and nearby crosswalks closely, and be prepared to brake if the object moves into the ego lane or if cross traffic/pedestrians appear.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "02c2b5c82e53434da51ed59f4f9aa46a", "source_group_file": "scene_532/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle front-left at about 26m", "scene_id": "scene_532", "ground_truth": "The vehicle front-left at about 26m is a white van, not a yellow taxi, and is traveling straight in its lane along the adjacent road. The ego vehicle is executing a right turn at an intersection, and the white van is not in the ego vehicle’s path. There is no collision risk, as the van is moving parallel to the ego vehicle’s turning trajectory and will not enter the turning lane. The road surface is wet, reducing traction, so the ego vehicle should proceed with caution, maintain a steady speed, and complete the right turn smoothly. No lane change is necessary or safe at this moment. The ego vehicle should continue to monitor the intersection for other traffic and pedestrians, but no immediate action beyond careful turning is required.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "fe3117a732fa4f71bf53a0d61a00ac79", "source_group_file": "scene_543/group_004_vehicle_annotations.json", "id": "TE-6", "group_id": "group_004", "object_reference": "the vehicle front-left at about 36m", "scene_id": "scene_543", "ground_truth": "The boxed object is a large parked coach/bus along the left side of the wet road. Over the next few seconds, the ego vehicle will continue approaching and passing it while other parked vehicles and trailers remain along the curb. The bus is likely stationary, but it may obstruct pedestrians or vehicles that could emerge from the left.\n\nThe ego vehicle should slow slightly for the rainy, low-visibility conditions, maintain a safe lateral gap from the parked bus, stay within its lane, and be prepared to brake or steer gently if someone or something appears from behind the bus. Avoid sudden maneuvers on the wet pavement.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "dfaa81c86ee1483e9f1a426279d68f30", "source_group_file": "scene_544/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle front-left at about 31m", "scene_id": "scene_544", "ground_truth": "The vehicle front-left at about 31m is a white SUV parked on the side of the road, not an active moving vehicle. It is stationary and not in the ego vehicle's lane. There is no imminent collision risk. The ego vehicle should continue driving normally, maintaining its current speed and lane position, as no action is required for safety.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "e56dc4ddeec5422799074e77ac708e34", "source_group_file": "scene_550/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle front-left at about 34m", "scene_id": "scene_550", "ground_truth": "The vehicle front-left at about 34m is a stationary dark-colored pickup truck parked in a lot, not moving. The ego vehicle should continue forward at a safe speed, maintaining awareness of the wet road conditions and surrounding parked vehicles. No deceleration is required as there is no imminent stop or collision risk.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "a42f0ed37a1b452f84a4bef0983425fd", "source_group_file": "scene_600/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle front-right at about 41m", "scene_id": "scene_600", "ground_truth": "The scene is an urban street with wet pavement and overcast weather, not a parking lot. The vehicle front-right at about 41m is a white SUV parked on the right side of the road. It is stationary and not expected to move. The ego vehicle should maintain its current speed and lane position, as there is no immediate need to slow down. The environment includes construction zones and other vehicles, so the ego vehicle should remain vigilant but no specific action is required at this moment.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "7e9d1f5bb5b049e5a11b6227d0627aaf", "source_group_file": "scene_601/group_004_vehicle_annotations.json", "id": "TE-6", "group_id": "group_004", "object_reference": "the vehicle front-left at about 21m", "scene_id": "scene_601", "ground_truth": "The vehicle front-left at about 21m is a dark-colored sedan parked along the curb, not moving. There is no indication it will stop or is on a collision course. The ego vehicle should maintain its current lane and speed, as no immediate hazard exists. The most likely traffic event is continued normal traffic flow.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "75ce4153ada24ef99194ae9459d92af5", "source_group_file": "scene_608/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle front-left at about 41m", "scene_id": "scene_608", "ground_truth": "The vehicle front-left at about 41m is not visible in the query frame; the bounding box in the image highlights a traffic signal, not a vehicle. Therefore, the ground truth incorrectly identifies the object and predicts its behavior. The ego vehicle should proceed with caution due to wet road conditions and reduced visibility, but no specific action is required based on a non-existent vehicle. The most likely traffic event is continued forward motion under green light.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "0a82495c9bf14f35ac401bfb1563fc7b", "source_group_file": "scene_643/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle behind at about 91m", "scene_id": "scene_643", "ground_truth": "The vehicle behind at about 91m is a dark-colored sedan traveling in the same direction as the ego vehicle, not crossing its path. It is following the road ahead and is not predicted to exit the roadway. Given the presence of pedestrians and a crosswalk ahead, the ego vehicle should reduce speed and prepare to stop if necessary, rather than maintaining current speed. The scene is stable, but caution is required due to potential pedestrian crossings.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "ee89428f78e047b3a9c37538ea5a8af6", "source_group_file": "scene_671/group_008_vehicle_annotations.json", "id": "TE-6", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 31m", "scene_id": "scene_671", "ground_truth": "There is no visible vehicle rear-right at about 31m in the provided images. The scene shows an empty road ahead with no other vehicles in the vicinity. The ego vehicle should continue driving cautiously, maintaining awareness of the surroundings, as there are no immediate traffic events or objects requiring specific action.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "5b0790bf51864efcbf4948a7fa73842e", "source_group_file": "scene_710/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle front-left at about 20m", "scene_id": "scene_710", "ground_truth": "The vehicle front-left at about 20m is a dark-colored sedan parked on the side of the road, not an active moving vehicle. It is stationary and not expected to move. The ego vehicle should maintain its current course and speed, as there is no immediate threat. The scene shows a quiet residential area with no active traffic conflicts. The ground truth incorrectly assumes motion and a need for lane change, which is not supported by the visual evidence.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "22f5c0dba98d40edaa171023cb9b3947", "source_group_file": "scene_720/group_001_vehicle_annotations.json", "id": "TE-6", "group_id": "group_001", "object_reference": "the vehicle rear-left at about 53m", "scene_id": "scene_720", "ground_truth": "The vehicle rear-left at about 53m is not visible in the provided frames. The frames show a white NUS bus in the adjacent lane ahead, not a vehicle to the rear-left. There is no evidence of any vehicle stopping or preparing to park. The ego vehicle should continue monitoring the road ahead, particularly the bus, and maintain a safe following distance. No evasive action is required at this time.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "8dbc3fa3595543fe90c540d639f8d94b", "source_group_file": "scene_743/group_003_vehicle_annotations.json", "id": "TE-6", "group_id": "group_003", "object_reference": "the vehicle front-right at about 14m", "scene_id": "scene_743", "ground_truth": "The object at 14m in the front-right is a stationary red trash bin, not a vehicle. There is no moving vehicle in that location. The ego vehicle should continue driving straight without any evasive maneuvers, as there is no collision risk from this object.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "1f918ecf64db4ce598453e9d73f63f81", "source_group_file": "scene_793/group_003_vehicle_annotations.json", "id": "TE-6", "group_id": "group_003", "object_reference": "the vehicle rear-right at about 23m", "scene_id": "scene_793", "ground_truth": "The vehicle rear-right at about 23m is not clearly visible in the query frame; the bounding box appears to be on a distant, stationary object or possibly a light reflection. There is no evidence of a vehicle in motion or a need for lane change. The ego vehicle should maintain its current course and monitor the environment, especially the crosswalk and approaching traffic, while ensuring safe following distance. No proactive lane change is warranted based on the visible scene.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": null, "question": "Describe how the current scene is likely to evolve over the next few seconds and what actions the ego vehicle should take to maintain safety.", "task": "time-extrapolation", "object_id": "a39d928e153242ef8e99a17fb772bee8", "source_group_file": "scene_805/group_007_vehicle_annotations.json", "id": "TE-6", "group_id": "group_007", "object_reference": "the vehicle rear-right at about 38m", "scene_id": "scene_805", "ground_truth": "The vehicle rear-right at about 38m is a stationary or slow-moving object, likely a parked vehicle or roadside structure, not an active merging vehicle. The ego vehicle should continue forward with caution, maintaining awareness of the static environment. No merging maneuver is occurring.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "choices": { "A": "In front", "B": "Front-right", "C": "Front-left", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "e1f8fc954ccd4047b257d2fcfad62088", "source_group_file": "scene_003/group_007_vehicle_annotations.json", "scene_description": "", "id": "TM-2", "group_id": "group_007", "object_reference": "the vehicle front-right at about 24m", "scene_id": "scene_003", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Left", "D": "Front-right", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "049db6b498284f6b9cb7437edfcd75fa", "source_group_file": "scene_008/group_001_vehicle_annotations.json", "scene_description": "", "id": "TM-2", "group_id": "group_001", "object_reference": "the vehicle front-right at about 11m", "scene_id": "scene_008", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Right", "E": "Behind", "F": "Left" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "f2ac41bd42a4466fa4d8c862ebf9ab26", "source_group_file": "scene_013/group_003_vehicle_annotations.json", "id": "TM-2", "group_id": "group_003", "object_reference": "the vehicle rear-left at about 29m", "scene_id": "scene_013", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "8fb3dc243ac54c43aa7a1bd8b0c266d3", "source_group_file": "scene_022/group_004_vehicle_annotations.json", "id": "TM-2", "group_id": "group_004", "object_reference": "the vehicle in front at about 34m", "scene_id": "scene_022", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "637db46fea8b4d9f9a7b59b2453a17df", "source_group_file": "scene_029/group_006_vehicle_annotations.json", "id": "TM-2", "group_id": "group_006", "object_reference": "the vehicle front-right at about 12m", "scene_id": "scene_029", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Left", "E": "Behind", "F": "Right" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "b2f43a3176fe44fb8a243fb8ad34eae5", "source_group_file": "scene_031/group_002_vehicle_annotations.json", "id": "TM-2", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 6m", "scene_id": "scene_031", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Right", "E": "Left", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "02b6ffd8c4ff469386d031eddeb93e07", "source_group_file": "scene_044/group_001_vehicle_annotations.json", "id": "TM-2", "group_id": "group_001", "object_reference": "the vehicle rear-right at about 37m", "scene_id": "scene_044", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Behind", "E": "Left", "F": "Right" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "0c8e48ab7fb742eba6d9420e0f1b78aa", "source_group_file": "scene_051/group_001_vehicle_annotations.json", "id": "TM-2", "group_id": "group_001", "object_reference": "the vehicle rear-right at about 48m", "scene_id": "scene_051", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Right", "D": "Front-right", "E": "Left", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "4b89390e458f4c5abc40d74e84dacbd4", "source_group_file": "scene_087/group_002_vehicle_annotations.json", "id": "TM-2", "group_id": "group_002", "object_reference": "the vehicle front-right at about 49m", "scene_id": "scene_087", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left", "B": "In front", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "02fbdac226074cc7b03dd686e5ef9e27", "source_group_file": "scene_102/group_006_vehicle_annotations.json", "id": "TM-2", "group_id": "group_006", "object_reference": "the vehicle front-left at about 11m", "scene_id": "scene_102", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Behind", "B": "In front", "C": "Front-left", "D": "Front-right", "E": "Left", "F": "Right" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "4771bace039d4e6ab11a347d6eddb02a", "source_group_file": "scene_116/group_007_vehicle_annotations.json", "id": "TM-2", "group_id": "group_007", "object_reference": "the vehicle rear-left at about 23m", "scene_id": "scene_116", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-right", "C": "Front-left", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "cf347fb53ec24b6ab2525bb394356cea", "source_group_file": "scene_127/group_007_vehicle_annotations.json", "id": "TM-2", "group_id": "group_007", "object_reference": "the vehicle front-left at about 38m", "scene_id": "scene_127", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left", "B": "In front", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "87e26e8c78bb47c6bb91affde5740d90", "source_group_file": "scene_141/group_008_vehicle_annotations.json", "id": "TM-2", "group_id": "group_008", "object_reference": "the vehicle front-left at about 21m", "scene_id": "scene_141", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Right", "B": "In front", "C": "Front-left", "D": "Front-right", "E": "Left", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "f69aba2fec6b400a9ab2d52d88122ba4", "source_group_file": "scene_158/group_007_vehicle_annotations.json", "id": "TM-2", "group_id": "group_007", "object_reference": "the vehicle front-right at about 22m", "scene_id": "scene_158", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left", "B": "In front", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "772fc205fed14777ad2a4565bcab80ad", "source_group_file": "scene_172/group_006_vehicle_annotations.json", "id": "TM-2", "group_id": "group_006", "object_reference": "the vehicle front-left at about 14m", "scene_id": "scene_172", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Behind", "C": "Front-left", "D": "Front-right", "E": "Left", "F": "Right" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "f7150dae1f624ddb8b1da060541ab8bc", "source_group_file": "scene_175/group_006_vehicle_annotations.json", "id": "TM-2", "group_id": "group_006", "object_reference": "the vehicle rear-left at about 47m", "scene_id": "scene_175", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left", "B": "In front", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "52b2adf11e004cca86f22d9b6eb8b3d3", "source_group_file": "scene_200/group_003_vehicle_annotations.json", "id": "TM-2", "group_id": "group_003", "object_reference": "the vehicle front-right at about 49m", "scene_id": "scene_200", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-right", "B": "In front", "C": "Front-left", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "c3e14ba620ac482cb70191ecadbf6a00", "source_group_file": "scene_204/group_007_vehicle_annotations.json", "id": "TM-2", "group_id": "group_007", "object_reference": "the vehicle front-right at about 23m", "scene_id": "scene_204", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Right", "E": "Left", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "ecdc57ab1b0a4164892120ffad3f844c", "source_group_file": "scene_225/group_002_vehicle_annotations.json", "id": "TM-2", "group_id": "group_002", "object_reference": "the vehicle rear-left at about 32m", "scene_id": "scene_225", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Left", "D": "Right", "E": "Behind", "F": "Front-right" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "9b599b9c1b1a44429e55aecdab85ba83", "source_group_file": "scene_276/group_007_vehicle_annotations.json", "id": "TM-2", "group_id": "group_007", "object_reference": "the vehicle front-right at about 9m", "scene_id": "scene_276", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left", "B": "In front", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "03c876aa99fa41cc9fe8c7db682fe311", "source_group_file": "scene_281/group_008_vehicle_annotations.json", "id": "TM-2", "group_id": "group_008", "object_reference": "the vehicle front-right at about 71m", "scene_id": "scene_281", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "f4bce6af1179465b8479fc1c0fe1a1c9", "source_group_file": "scene_297/group_004_vehicle_annotations.json", "id": "TM-2", "group_id": "group_004", "object_reference": "the vehicle rear-right at about 21m", "scene_id": "scene_297", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-right", "C": "Left", "D": "Front-left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "7fc35b4f232b46e6899b5cfd720dacb0", "source_group_file": "scene_306/group_002_vehicle_annotations.json", "id": "TM-2", "group_id": "group_002", "object_reference": "the vehicle front-left at about 50m", "scene_id": "scene_306", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-left", "C": "Front-right", "D": "Right", "E": "Left", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "bf9c6ea6c1d84a41b30e3670ba30a58c", "source_group_file": "scene_329/group_007_vehicle_annotations.json", "id": "TM-2", "group_id": "group_007", "object_reference": "the vehicle rear-left at about 17m", "scene_id": "scene_329", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Behind", "B": "In front", "C": "Front-left", "D": "Front-right", "E": "Left", "F": "Right" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "1ebe2fd9f17c46659a6039fbd2df497b", "source_group_file": "scene_338/group_004_vehicle_annotations.json", "id": "TM-2", "group_id": "group_004", "object_reference": "the vehicle rear-left at about 81m", "scene_id": "scene_338", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "In front", "B": "Front-right", "C": "Left", "D": "Front-left", "E": "Right", "F": "Behind" }, "question": "Where was the object in the red bounding box located relative to the ego vehicle in the previous frame(s)?", "task": "time-memory", "object_id": "a37182a7b76d466bbbf0ac5c71dc980a", "source_group_file": "scene_367/group_007_vehicle_annotations.json", "id": "TM-2", "group_id": "group_007", "object_reference": "the 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"scene_443/group_008_vehicle_annotations.json", "id": "TM-5", "group_id": "group_008", "object_reference": "the vehicle front-right at about 81m", "scene_id": "scene_443", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Same lane", "B": "Left adjacent lane", "C": "Right adjacent lane", "D": "Outside roadway" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "2ed265ebabbb42088d0668bc1d358ca2", "source_group_file": "scene_464/group_004_vehicle_annotations.json", "id": "TM-5", "group_id": "group_004", "object_reference": "the vehicle in front at about 36m", "scene_id": "scene_464", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Same lane", "B": "Right adjacent lane", "C": "Left adjacent lane", "D": "Outside roadway" }, "question": "In which lane was the object in the red bounding box located earlier in the 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"Same lane" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "eeb38ba0b0314c7b8018efd40ad14c32", "source_group_file": "scene_529/group_001_vehicle_annotations.json", "id": "TM-5", "group_id": "group_001", "object_reference": "the vehicle in front at about 58m", "scene_id": "scene_529", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Left adjacent lane", "B": "Same lane", "C": "Right adjacent lane", "D": "Outside roadway" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "b2b1cd611f9c455ab7ac3d5e98b50b38", "source_group_file": "scene_536/group_008_vehicle_annotations.json", "id": "TM-5", "group_id": "group_008", "object_reference": "the vehicle behind at about 19m", "scene_id": "scene_536", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Same lane", "B": "Right adjacent lane", "C": "Left adjacent lane", "D": "Outside roadway" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "a48c41f5aa264fa79c74044fda29a6de", "source_group_file": "scene_546/group_006_vehicle_annotations.json", "id": "TM-5", "group_id": "group_006", "object_reference": "the vehicle rear-left at about 40m", "scene_id": "scene_546", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Same lane", "B": "Right adjacent lane", "C": "Outside roadway", "D": "Left adjacent lane" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "cb593819a7574347aaea1cc57aac07ce", "source_group_file": "scene_603/group_008_vehicle_annotations.json", "id": "TM-5", "group_id": "group_008", "object_reference": "the vehicle rear-left at about 60m", 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"scene_606/group_002_vehicle_annotations.json", "id": "TM-5", "group_id": "group_002", "object_reference": "the vehicle rear-right at about 50m", "scene_id": "scene_606", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Same lane", "B": "Right adjacent lane", "C": "Outside roadway", "D": "Left adjacent lane" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "41d19ea57b554dde91dda151df9d5c24", "source_group_file": "scene_620/group_003_vehicle_annotations.json", "id": "TM-5", "group_id": "group_003", "object_reference": "the vehicle rear-left at about 30m", "scene_id": "scene_620", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Same lane", "B": "Left adjacent lane", "C": "Outside roadway", "D": "Right adjacent lane" }, "question": "In which lane was the object in the red bounding box located earlier in the 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{ "choices": { "A": "Left adjacent lane", "B": "Right adjacent lane", "C": "Outside roadway", "D": "Same lane" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "cbc0f67edc15469fa7e1637a300e4b78", "source_group_file": "scene_770/group_006_vehicle_annotations.json", "id": "TM-5", "group_id": "group_006", "object_reference": "the vehicle behind at about 74m", "scene_id": "scene_770", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Left adjacent lane", "B": "Right adjacent lane", "C": "Outside roadway", "D": "Same lane" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "b1ff66e8a5354dcfa4f5e40857617db5", "source_group_file": "scene_782/group_007_vehicle_annotations.json", "id": "TM-5", "group_id": "group_007", "object_reference": "the vehicle in front at about 58m", "scene_id": "scene_782", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Left adjacent lane", "B": "Right adjacent lane", "C": "Outside roadway", "D": "Same lane" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "e2e7bbd63aca46b39645dd3247bd90f6", "source_group_file": "scene_808/group_004_vehicle_annotations.json", "id": "TM-5", "group_id": "group_004", "object_reference": "the vehicle behind at about 105m", "scene_id": "scene_808", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Same lane", "B": "Right adjacent lane", "C": "Left adjacent lane", "D": "Outside roadway" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "e607cacbd0fe4f799cb45a81bd292725", "source_group_file": "scene_815/group_008_vehicle_annotations.json", "id": "TM-5", "group_id": "group_008", "object_reference": "the vehicle rear-right at about 20m", "scene_id": "scene_815", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Left adjacent lane", "B": "Right adjacent lane", "C": "Outside roadway", "D": "Same lane" }, "question": "In which lane was the object in the red bounding box located earlier in the sequence?", "task": "time-memory", "object_id": "c1b3ad460a674a1ba8148e7e23eb8da7", "source_group_file": "scene_833/group_006_vehicle_annotations.json", "id": "TM-5", "group_id": "group_006", "object_reference": "the vehicle behind at about 43m", "scene_id": "scene_833", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slightly veer right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_188/group_006_vehicle_annotations.json", "scene_description": "", "id": "TRJ-1", "group_id": "group_006", "scene_id": "scene_188", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slightly veer left", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_199/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_199", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Slightly veer left" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_249/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_249", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_285/group_007_vehicle_annotations.json", "scene_description": "", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_285", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_357/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_357", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slow to near stop", "C": "Slightly veer left", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_389/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_389", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer right", "B": "Go straight", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_399/group_005_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_005", "scene_id": "scene_399", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Slightly veer left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_482/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_482", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Slightly veer right", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_489/group_002_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_002", "scene_id": "scene_489", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Slightly veer right", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_492/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_492", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_496/group_002_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_002", "scene_id": "scene_496", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slow to near stop", "C": "Slightly veer left", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_499/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_499", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slightly veer left", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_539/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_539", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Slightly veer right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_549/group_003_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_003", "scene_id": "scene_549", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Slightly veer left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_568/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_568", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Slow to near stop", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_579/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_579", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Slightly veer right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_587/group_005_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_005", "scene_id": "scene_587", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slow to near stop", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_588/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_588", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slow to near stop", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_589/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_589", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_593/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_593", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slow to near stop", "C": "Slightly veer left", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_596/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_596", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Slow to near stop", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_597/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_597", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_599/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_599", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Slightly veer right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_653/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_653", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_664/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_664", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_669/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_669", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Go straight", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_679/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_679", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Go straight", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_687/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_687", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_688/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_688", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_689/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_689", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slow to near stop", "B": "Go straight", "C": "Slightly veer left", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_695/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_695", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_697/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_697", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_698/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_698", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_699/group_004_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_004", "scene_id": "scene_699", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slightly veer right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_753/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_753", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_759/group_006_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_006", "scene_id": "scene_759", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Slightly veer left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_766/group_003_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_003", "scene_id": "scene_766", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Go straight", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_769/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_769", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slightly veer left", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_774/group_006_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_006", "scene_id": "scene_774", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Go straight" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_777/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_777", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_778/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_778", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Go straight", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_779/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_779", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slow to near stop", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_786/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_786", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Slow to near stop", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_787/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_787", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_788/group_004_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_004", "scene_id": "scene_788", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_796/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_796", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_798/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_798", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Go straight" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_799/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_799", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slightly veer right", "F": "Slow to near stop" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_828/group_002_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_002", "scene_id": "scene_828", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slow to near stop", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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?", "task": "trajectory-prediction", "source_group_file": "scene_839/group_007_vehicle_annotations.json", "id": "TRJ-1", "group_id": "group_007", "scene_id": "scene_839", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_063/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_063", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Slow to near stop", "E": "Turn left", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_084/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_084", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_088/group_003_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_003", "scene_id": "scene_088", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Slow to near stop", "E": "Turn left", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_089/group_004_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_004", "scene_id": "scene_089", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Slow to near stop", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_112/group_001_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_001", "scene_id": "scene_112", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_166/group_002_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_002", "scene_id": "scene_166", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slightly veer left", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_167/group_001_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_001", "scene_id": "scene_167", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Slightly veer left" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_189/group_004_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_004", "scene_id": "scene_189", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slow to near stop", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_196/group_005_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_005", "scene_id": "scene_196", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Slightly veer right", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_237/group_004_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_004", "scene_id": "scene_237", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_248/group_001_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_001", "scene_id": "scene_248", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_296/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_296", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer right", "B": "Go straight", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_342/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_342", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Slow to near stop", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_385/group_003_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_003", "scene_id": "scene_385", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Go straight", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_386/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_386", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Go straight" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_397/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_397", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slow to near stop", "B": "Go straight", "C": "Slightly veer left", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_417/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_417", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Slow to near stop", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_426/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_426", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Slightly veer right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_457/group_002_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_002", "scene_id": "scene_457", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Turn right", "E": "Slightly veer right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_463/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_463", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_468/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_468", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Turn left", "B": "Go straight", "C": "Slightly veer left", "D": "Slightly veer right", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_490/group_001_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_001", "scene_id": "scene_490", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_498/group_004_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_004", "scene_id": "scene_498", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_519/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_519", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_551/group_003_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_003", "scene_id": "scene_551", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Slow to near stop", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_552/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_552", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_572/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_572", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slow to near stop", "D": "Slightly veer right", "E": "Turn left", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_573/group_004_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_004", "scene_id": "scene_573", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_590/group_001_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_001", "scene_id": "scene_590", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Go straight", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_639/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_639", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_652/group_002_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_002", "scene_id": "scene_652", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slightly veer left", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_655/group_002_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_002", "scene_id": "scene_655", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_657/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_657", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Go straight", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_658/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_658", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_674/group_005_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_005", "scene_id": "scene_674", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Go straight", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_675/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_675", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_681/group_001_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_001", "scene_id": "scene_681", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Go straight", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_685/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_685", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Go straight", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_692/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_692", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Slightly veer left" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_701/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_701", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_712/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_712", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Turn left", "D": "Slightly veer right", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_729/group_004_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_004", "scene_id": "scene_729", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_752/group_006_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_006", "scene_id": "scene_752", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Slow to near stop", "E": "Turn left", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_765/group_004_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_004", "scene_id": "scene_765", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Go straight", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_791/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_791", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_823/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_823", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer right", "B": "Go straight", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_825/group_001_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_001", "scene_id": "scene_825", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer left", "C": "Slightly veer right", "D": "Slow to near stop", "E": "Turn left", "F": "Turn right" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_841/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_841", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Go straight", "B": "Slightly veer right", "C": "Slightly veer left", "D": "Turn left", "E": "Turn right", "F": "Slow to near stop" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_845/group_003_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_003", "scene_id": "scene_845", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Slightly veer left", "B": "Slightly veer right", "C": "Turn left", "D": "Turn right", "E": "Slow to near stop", "F": "Go straight" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_846/group_007_vehicle_annotations.json", "id": "TRJ-2", "group_id": "group_007", "scene_id": "scene_846", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Left-side region", "D": "Right-side region", "E": "Front-right region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_026/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_026", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_048/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_048", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Left-side region", "D": "Front-left region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_139/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_139", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Left-side region", "D": "Right-side region", "E": "Front-left region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_198/group_001_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_001", "scene_id": "scene_198", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Front-left region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_287/group_002_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_002", "scene_id": "scene_287", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Left-side region", "D": "Right-side region", "E": "Near the current position", "F": "Front-right region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_289/group_006_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_006", "scene_id": "scene_289", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Left-side region", "D": "Right-side region", "E": "Near the current position", "F": "Front-right region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_292/group_003_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_003", "scene_id": "scene_292", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-right region", "B": "Front-left region", "C": "Front-center region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_293/group_002_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_002", "scene_id": "scene_293", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-left region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_299/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_299", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Near the current position", "B": "Front-left region", "C": "Front-center region", "D": "Front-right region", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_388/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_388", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Near the current position", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_396/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_396", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Near the current position", "C": "Front-center region", "D": "Front-right region", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_449/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_449", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Near the current position", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_459/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_459", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Left-side region", "D": "Front-left region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_460/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_460", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-left region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_479/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_479", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Left-side region", "D": "Right-side region", "E": "Near the current position", "F": "Front-left region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_494/group_001_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_001", "scene_id": "scene_494", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_508/group_004_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_004", "scene_id": "scene_508", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Left-side region", "D": "Right-side region", "E": "Front-right region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_527/group_002_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_002", "scene_id": "scene_527", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_537/group_001_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_001", "scene_id": "scene_537", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Near the current position", "C": "Front-center region", "D": "Front-right region", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_538/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_538", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Near the current position", "D": "Front-right region", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_558/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_558", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Left-side region", "D": "Right-side region", "E": "Near the current position", "F": "Front-center region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_559/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_559", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Left-side region", "D": "Right-side region", "E": "Front-right region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_562/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_562", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Near the current position", "B": "Front-left region", "C": "Front-center region", "D": "Front-right region", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_583/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_583", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Near the current position", "C": "Front-center region", "D": "Front-right region", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_585/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_585", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_586/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_586", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Front-center region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_625/group_003_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_003", "scene_id": "scene_625", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Near the current position", "B": "Front-left region", "C": "Front-center region", "D": "Front-right region", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_638/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_638", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Front-left region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_654/group_001_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_001", "scene_id": "scene_654", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-left region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_659/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_659", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_665/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_665", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Left-side region", "D": "Right-side region", "E": "Front-center region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_668/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_668", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_678/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_678", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Left-side region", "D": "Right-side region", "E": "Near the current position", "F": "Front-center region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_686/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_686", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Left-side region", "D": "Front-left region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_703/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_703", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Left-side region", "D": "Right-side region", "E": "Front-right region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_708/group_002_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_002", "scene_id": "scene_708", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Left-side region", "D": "Right-side region", "E": "Front-left region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_717/group_003_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_003", "scene_id": "scene_717", "ground_truth": "E", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_745/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_745", "ground_truth": "F", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Front-center region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_749/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_749", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-left region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_761/group_005_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_005", "scene_id": "scene_761", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Front-center region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_767/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_767", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Left-side region", "D": "Front-center region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_768/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_768", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_785/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_785", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Front-center region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_794/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_794", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Left-side region", "D": "Front-center region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_795/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_795", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-right region", "C": "Front-center region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_824/group_007_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_007", "scene_id": "scene_824", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-right region", "B": "Front-left region", "C": "Front-center region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_829/group_004_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_004", "scene_id": "scene_829", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-left region", "C": "Front-right region", "D": "Left-side region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_830/group_006_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_006", "scene_id": "scene_830", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-center region", "B": "Front-right region", "C": "Left-side region", "D": "Front-left region", "E": "Right-side region", "F": "Near the current position" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_835/group_003_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_003", "scene_id": "scene_835", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Front-left region", "B": "Front-center region", "C": "Front-right region", "D": "Near the current position", "E": "Left-side region", "F": "Right-side region" }, "question": "Based on the past 5 consecutive 360-degree multi-camera frames, at the 5th future frame (approximately 2.5 seconds ahead), which relative endpoint region best matches the ego vehicle trajectory in the current ego-centric coordinate system?", "task": "trajectory-prediction", "source_group_file": "scene_838/group_004_vehicle_annotations.json", "id": "TRJ-3", "group_id": "group_004", "scene_id": "scene_838", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_088/group_006_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_006", "scene_id": "scene_088", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_097/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_097", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Accelerating", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_187/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_187", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Decelerating", "C": "Accelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_189/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_189", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Stopping / nearly stopped", "B": "Accelerating", "C": "Maintaining roughly constant speed", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_267/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_267", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_277/group_006_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_006", "scene_id": "scene_277", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_287/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_287", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_298/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_298", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_356/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_356", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_359/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_359", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Decelerating", "C": "Stopping / nearly stopped", "D": "Maintaining roughly constant speed" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_368/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_368", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_369/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_369", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Accelerating", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_377/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_377", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Stopping / nearly stopped", "C": "Maintaining roughly constant speed", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_387/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_387", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Decelerating", "C": "Stopping / nearly stopped", "D": "Maintaining roughly constant speed" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_395/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_395", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_448/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_448", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_488/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_488", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_494/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_494", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Decelerating", "C": "Maintaining roughly constant speed", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_495/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_495", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_496/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_496", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_497/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_497", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Stopping / nearly stopped", "C": "Maintaining roughly constant speed", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_498/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_498", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Decelerating", "B": "Accelerating", "C": "Maintaining roughly constant speed", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_529/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_529", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Decelerating", "C": "Accelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_548/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_548", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_565/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_565", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_573/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_573", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Accelerating", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_655/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_655", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Decelerating", "C": "Maintaining roughly constant speed", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_656/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_656", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_666/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_666", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Decelerating", "C": "Accelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_683/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_683", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Stopping / nearly stopped", "C": "Maintaining roughly constant speed", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_684/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_684", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_694/group_006_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_006", "scene_id": "scene_694", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Decelerating", "C": "Stopping / nearly stopped", "D": "Accelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_709/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_709", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_718/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_718", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_728/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_728", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Decelerating", "C": "Maintaining roughly constant speed", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_729/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_729", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_738/group_006_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_006", "scene_id": "scene_738", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Decelerating", "B": "Accelerating", "C": "Maintaining roughly constant speed", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_756/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_756", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_765/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_765", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Accelerating", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_766/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_766", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Accelerating", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_776/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_776", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Decelerating", "C": "Maintaining roughly constant speed", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_784/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_784", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Stopping / nearly stopped", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_792/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_792", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Accelerating", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_793/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_793", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Stopping / nearly stopped", "C": "Maintaining roughly constant speed", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_808/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_808", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Decelerating", "B": "Accelerating", "C": "Maintaining roughly constant speed", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_827/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_827", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Maintaining roughly constant speed", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_828/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_828", "ground_truth": "C", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Accelerating", "C": "Decelerating", "D": "Stopping / nearly stopped" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_829/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_829", "ground_truth": "A", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Maintaining roughly constant speed", "B": "Decelerating", "C": "Stopping / nearly stopped", "D": "Accelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_835/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_835", "ground_truth": "D", "question_format": "MCQ", "scoring_type": "mcq" }, { "choices": { "A": "Accelerating", "B": "Stopping / nearly stopped", "C": "Maintaining roughly constant speed", "D": "Decelerating" }, "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)?", "task": "trajectory-prediction", "source_group_file": "scene_838/group_007_vehicle_annotations.json", "id": "TRJ-4", "group_id": "group_007", "scene_id": "scene_838", "ground_truth": "B", "question_format": "MCQ", "scoring_type": "mcq" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-12.833, 0.405], [-9.672, 0.267], [-6.504, 0.147], [-3.26, 0.055], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_028/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_028", "ground_truth": "[[2.547, 0.004], [5.669, 0.096], [8.765, 0.237], [11.849, 0.361], [15.002, 0.441]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-20.243, 1.118], [-15.001, 0.717], [-9.904, 0.355], [-4.893, 0.111], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_059/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_059", "ground_truth": "[[4.885, 0.044], [9.774, 0.237], [14.123, 0.524], [18.974, 0.992], [23.778, 1.62]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-22.209, 0.049], [-16.747, -0.087], [-11.248, -0.075], [-5.644, 0.005], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_079/group_002_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_002", "scene_id": "scene_079", "ground_truth": "[[5.728, -0.029], [11.592, -0.108], [17.421, -0.294], [23.364, -0.705], [29.42, -1.372]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-19.923, 0.472], [-14.833, 0.274], [-9.804, 0.144], [-4.837, 0.042], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_086/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_086", "ground_truth": "[[4.934, -0.013], [9.907, -0.036], [14.848, -0.07], [19.658, -0.16], [24.335, -0.303]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[0.0, 0.004], [0.0, 0.002], [0.0, 0.001], [-0.0, 0.001], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_119/group_006_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_006", "scene_id": "scene_119", "ground_truth": "[[-0.0, -0.001], [-0.0, -0.001], [-0.0, -0.002], [-0.0, -0.002], [0.0, -0.002]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.21, 0.009], [-0.024, 0.007], [0.018, 0.005], [0.001, 0.002], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_169/group_002_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_002", "scene_id": "scene_169", "ground_truth": "[[0.0, -0.002], [0.0, -0.003], [0.0, -0.004], [0.016, -0.005], [0.155, -0.006]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-21.018, 0.188], [-15.847, 0.165], [-10.829, 0.115], [-5.171, 0.05], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_173/group_006_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_006", "scene_id": "scene_173", "ground_truth": "[[5.133, -0.021], [10.322, -0.075], [16.61, -0.11], [22.871, -0.163], [28.518, -0.22]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.0, 0.004], [-0.0, 0.002], [-0.0, 0.002], [-0.0, 0.001], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_263/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_263", "ground_truth": "[[0.0, -0.001], [-0.0, -0.001], [-0.0, -0.002], [-0.0, -0.002], [-0.0, -0.002]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.212, 0.069], [-7.364, 0.078], [-4.64, 0.056], [-2.212, 0.029], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_291/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_291", "ground_truth": "[[1.976, 0.007], [3.88, 0.079], [5.818, 0.24], [7.768, 0.537], [9.808, 1.082]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [-0.0, 0.0], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_309/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_309", "ground_truth": "[[0.0, -0.0], [0.0, -0.0], [0.0, -0.0], [0.0, -0.0], [-0.0, -0.0]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.0, 0.0], [-0.0, 0.0], [-0.0, 0.0], [-0.0, 0.0], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_325/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_325", "ground_truth": "[[-0.0, -0.0], [-0.0, -0.0], [0.0, -0.0], [-0.0, -0.0], [-0.0, -0.0]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-1.941, 0.029], [-0.905, 0.024], [-0.392, 0.013], [-0.11, 0.004], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_352/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_352", "ground_truth": "[[-0.019, -0.005], [-0.027, -0.01], [-0.027, -0.013], [-0.027, -0.016], [-0.018, -0.019]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[0.0, 0.0], [-0.0, 0.0], [-0.0, 0.0], [-0.0, 0.0], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_361/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_361", "ground_truth": "[[0.0, -0.0], [0.0, -0.0], [-0.0, -0.0], [-0.0, -0.0], [0.0, -0.0]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-19.494, 0.329], [-14.674, 0.207], [-9.854, 0.111], [-4.989, 0.041], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_373/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_373", "ground_truth": "[[4.973, -0.033], [9.799, -0.073], [14.48, -0.13], [18.659, -0.195], [23.748, -0.276]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.136, -0.734], [-10.244, -0.44], [-6.753, -0.177], [-3.308, -0.035], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_424/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_424", "ground_truth": "[[3.16, -0.079], [6.077, -0.267], [8.746, -0.531], [11.32, -0.855], [13.688, -1.198]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-14.534, 0.08], [-10.9, 0.068], [-7.606, 0.069], [-3.874, 0.048], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_434/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_434", "ground_truth": "[[3.908, -0.051], [7.844, -0.084], [12.14, -0.098], [16.154, -0.131], [20.337, -0.16]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-21.356, 0.124], [-15.953, 0.095], [-10.621, 0.062], [-5.293, 0.041], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_438/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_438", "ground_truth": "[[5.282, -0.057], [10.528, -0.107], [15.795, -0.179], [21.621, -0.244], [26.859, -0.331]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-15.76, 0.215], [-12.08, 0.186], [-8.418, 0.131], [-3.681, 0.04], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_439/group_006_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_006", "scene_id": "scene_439", "ground_truth": "[[3.691, -0.047], [7.339, -0.112], [11.103, -0.176], [14.896, -0.262], [18.802, -0.362]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-2.686, 0.356], [-2.125, 0.227], [-1.366, 0.094], [-0.558, 0.019], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_474/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_474", "ground_truth": "[[0.253, 0.008], [0.391, 0.02], [0.497, 0.03], [0.751, 0.056], [1.238, 0.124]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-19.033, 0.112], [-14.348, 0.098], [-9.555, 0.065], [-4.742, 0.041], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_481/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_481", "ground_truth": "[[5.19, -0.014], [9.807, -0.035], [14.412, -0.069], [18.496, -0.119], [22.928, -0.167]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.458, -0.07], [-8.016, -0.003], [-5.336, 0.026], [-2.678, 0.024], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_483/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_483", "ground_truth": "[[2.659, -0.016], [5.308, 0.008], [7.986, 0.098], [10.713, 0.28], [13.546, 0.579]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-14.001, 0.119], [-10.451, 0.086], [-7.003, 0.054], [-3.526, 0.02], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_495/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_495", "ground_truth": "[[3.506, -0.023], [6.956, -0.064], [10.376, -0.102], [13.724, -0.13], [17.062, -0.149]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-15.634, -0.128], [-12.055, -0.081], [-7.884, -0.06], [-3.949, -0.026], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_515/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_515", "ground_truth": "[[3.908, -0.005], [7.822, -0.006], [11.739, -0.02], [15.609, -0.005], [19.586, 0.055]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.765, 0.048], [-10.248, 0.066], [-6.839, 0.071], [-3.421, 0.024], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_545/group_003_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_003", "scene_id": "scene_545", "ground_truth": "[[3.753, -0.044], [7.138, -0.121], [10.37, -0.213], [13.573, -0.322], [16.262, -0.412]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-6.364, 0.176], [-4.555, 0.124], [-2.718, 0.067], [-1.212, 0.028], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_553/group_003_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_003", "scene_id": "scene_553", "ground_truth": "[[0.797, -0.027], [1.319, -0.051], [1.541, -0.066], [1.566, -0.078], [1.618, -0.087]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-23.012, 0.148], [-17.612, 0.104], [-11.707, 0.069], [-5.326, 0.044], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_561/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_561", "ground_truth": "[[4.164, -0.02], [9.426, -0.085], [14.209, -0.185], [19.428, -0.303], [24.657, -0.39]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-19.225, 0.082], [-14.455, 0.089], [-9.603, 0.068], [-4.779, 0.038], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_563/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_563", "ground_truth": "[[4.717, -0.023], [10.459, -0.02], [16.291, -0.018], [21.083, -0.008], [26.468, 0.006]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-17.333, 0.214], [-13.185, 0.105], [-9.03, 0.049], [-4.886, 0.019], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_564/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_564", "ground_truth": "[[3.926, -0.025], [7.626, -0.027], [11.127, -0.056], [14.368, -0.106], [16.868, -0.136]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-16.666, 0.095], [-12.475, 0.083], [-8.307, 0.067], [-4.139, 0.039], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_567/group_003_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_003", "scene_id": "scene_567", "ground_truth": "[[4.147, -0.034], [8.319, -0.059], [12.514, -0.072], [17.45, -0.068], [21.377, -0.06]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-12.168, 0.139], [-9.02, 0.101], [-6.095, 0.07], [-3.127, 0.038], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_571/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_571", "ground_truth": "[[3.054, -0.044], [6.067, -0.061], [9.227, -0.073], [12.331, -0.092], [15.357, -0.087]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.773, 0.11], [-7.73, 0.059], [-5.184, 0.035], [-2.597, 0.017], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_609/group_002_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_002", "scene_id": "scene_609", "ground_truth": "[[2.513, -0.028], [5.007, -0.026], [7.629, -0.018], [10.377, -0.007], [13.277, -0.01]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-15.982, 0.729], [-11.989, 0.429], [-7.974, 0.207], [-4.012, 0.078], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_630/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_630", "ground_truth": "[[4.113, -0.014], [8.264, -0.004], [11.607, 0.006], [15.715, -0.049], [19.801, -0.105]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.0, -0.0], [0.0, -0.0], [0.0, 0.0], [0.0, 0.0], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_633/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_633", "ground_truth": "[[0.0, -0.0], [-0.0, 0.0], [-0.0, 0.0], [-0.0, -0.0], [-0.0, -0.0]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.493, 0.284], [-7.364, 0.154], [-4.649, 0.059], [-2.045, 0.011], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_636/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_636", "ground_truth": "[[1.752, 0.003], [3.109, 0.009], [4.338, 0.003], [5.671, -0.015], [7.085, -0.056]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-11.54, 0.141], [-8.494, 0.1], [-5.58, 0.059], [-2.754, 0.023], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_637/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_637", "ground_truth": "[[2.648, -0.008], [5.218, -0.032], [7.6, -0.073], [9.471, -0.091], [11.321, -0.096]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-18.031, 0.28], [-13.674, 0.172], [-9.33, 0.098], [-4.106, 0.036], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_642/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_642", "ground_truth": "[[3.748, -0.022], [6.595, -0.04], [10.059, -0.11], [13.444, -0.227], [16.901, -0.396]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-3.411, 0.105], [-2.522, 0.066], [-1.421, 0.021], [-0.5, 0.004], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_650/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_650", "ground_truth": "[[0.214, -0.003], [0.273, -0.007], [0.254, -0.009], [0.25, -0.012], [0.25, -0.014]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-6.939, 0.138], [-5.275, 0.177], [-3.401, 0.128], [-1.645, 0.042], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_663/group_003_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_003", "scene_id": "scene_663", "ground_truth": "[[1.763, 0.053], [3.72, 0.238], [6.512, 0.671], [9.084, 1.187], [11.011, 1.616]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-16.293, -0.693], [-12.261, -0.421], [-8.164, -0.193], [-4.092, -0.046], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_682/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_682", "ground_truth": "[[4.151, -0.093], [8.229, -0.34], [12.336, -0.73], [16.453, -1.236], [20.592, -1.851]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.806, 0.075], [-10.369, 0.025], [-6.947, 0.007], [-3.514, 0.009], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_683/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_683", "ground_truth": "[[3.481, -0.044], [6.924, -0.112], [10.347, -0.21], [13.78, -0.329], [17.094, -0.452]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-9.867, 1.416], [-7.624, 0.778], [-5.179, 0.325], [-2.649, 0.085], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_716/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_716", "ground_truth": "[[2.707, 0.016], [5.381, 0.083], [8.014, 0.175], [10.573, 0.282], [13.046, 0.405]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-14.536, 0.033], [-10.755, 0.002], [-7.244, -0.003], [-3.664, 0.003], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_722/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_722", "ground_truth": "[[3.743, -0.035], [7.073, -0.136], [10.745, -0.347], [14.558, -0.63], [18.775, -1.021]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-14.399, 0.149], [-10.41, 0.076], [-6.673, 0.037], [-3.2, 0.015], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_725/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_725", "ground_truth": "[[2.946, -0.027], [5.751, -0.066], [8.503, -0.114], [11.186, -0.162], [13.81, -0.151]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-9.094, 0.026], [-6.761, 0.097], [-4.567, 0.08], [-2.288, 0.029], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_750/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_750", "ground_truth": "[[2.306, 0.003], [4.686, 0.026], [6.815, 0.094], [9.093, 0.182], [11.475, 0.242]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-24.555, 0.117], [-18.609, 0.083], [-12.492, 0.049], [-6.27, 0.038], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_790/group_004_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_004", "scene_id": "scene_790", "ground_truth": "[[6.261, -0.032], [12.458, -0.051], [18.57, -0.077], [24.634, -0.1], [30.561, -0.139]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-16.934, 0.127], [-13.114, 0.077], [-8.359, 0.038], [-4.066, 0.016], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_812/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_812", "ground_truth": "[[3.964, -0.028], [7.689, -0.057], [11.215, -0.101], [14.562, -0.141], [17.932, -0.207]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.482, -0.013], [-10.041, -0.003], [-6.556, 0.007], [-3.387, 0.011], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_813/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_813", "ground_truth": "[[3.27, -0.015], [6.468, -0.044], [9.562, -0.066], [12.675, -0.071], [15.941, -0.047]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-12.521, 0.102], [-9.288, 0.055], [-6.071, 0.03], [-2.997, 0.027], [-0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_817/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_817", "ground_truth": "[[2.982, -0.035], [6.112, -0.039], [9.287, 0.041], [12.431, 0.355], [15.575, 1.092]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-19.956, 0.28], [-15.268, 0.167], [-10.213, 0.08], [-4.961, 0.032], [0.0, 0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_818/group_002_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_002", "scene_id": "scene_818", "ground_truth": "[[6.259, -0.055], [12.06, -0.1], [18.181, -0.196], [24.319, -0.274], [30.486, -0.348]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.208, 0.143], [-9.1, 0.073], [-6.431, 0.051], [-3.151, 0.02], [0.0, -0.0]]\nPredict the next 5 future ego positions in the same coordinate system. Output only a JSON array of exactly 5 points formatted as [[x1, y1], [x2, y2], [x3, y3], [x4, y4], [x5, y5]].", "task": "trajectory-prediction", "source_group_file": "scene_832/group_007_vehicle_annotations.json", "id": "TRJ-5", "group_id": "group_007", "scene_id": "scene_832", "ground_truth": "[[3.086, -0.05], [5.893, -0.12], [8.987, -0.231], [12.151, -0.423], [14.622, -0.651]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-12.397, 0.056], [-9.788, 0.031], [-6.833, 0.004], [-3.533, -0.0], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [3.834, -0.027]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_005/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_005", "ground_truth": "[[7.883, -0.085], [12.117, -0.134], [16.56, -0.135], [21.147, -0.078]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-20.697, 0.718], [-15.566, 0.555], [-10.294, 0.299], [-5.139, 0.099], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [5.058, -0.019]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_007/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_007", "ground_truth": "[[10.294, -0.017], [15.512, -0.047], [20.683, -0.075], [25.687, -0.043]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-8.256, 0.175], [-5.899, 0.067], [-3.759, 0.012], [-1.78, -0.008], [0.0, 0.0]]\nKnown first future position [x1, y1]: [1.634, 0.013]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_014/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_014", "ground_truth": "[[3.156, 0.016], [4.525, 0.001], [5.77, -0.016], [6.788, -0.046]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[0.0, 0.012], [0.0, 0.008], [0.0, 0.005], [-0.0, 0.002], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [-0.0, -0.002]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_023/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_023", "ground_truth": "[[0.0, -0.004], [-0.0, -0.005], [0.0, -0.006], [0.0, -0.007]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-8.507, 0.484], [-6.618, 0.179], [-4.546, 0.028], [-2.348, -0.002], [0.0, -0.0]]\nKnown first future position [x1, y1]: [2.54, -0.009]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_037/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_037", "ground_truth": "[[5.226, -0.057], [7.901, -0.122], [10.278, -0.18], [12.925, -0.224]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-14.505, -0.082], [-10.895, -0.031], [-7.211, 0.008], [-3.44, 0.016], [0.0, -0.0]]\nKnown first future position [x1, y1]: [3.827, -0.026]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_042/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_042", "ground_truth": "[[7.653, -0.056], [11.445, -0.074], [15.215, -0.095], [19.302, -0.114]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-2.38, -0.024], [-2.354, -0.024], [-2.036, -0.015], [-1.263, -0.003], [0.0, 0.0]]\nKnown first future position [x1, y1]: [1.562, -0.01]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_050/group_005_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_005", "scene_id": "scene_050", "ground_truth": "[[3.182, -0.027], [5.269, -0.05], [7.719, -0.066], [10.464, -0.057]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-12.702, -0.03], [-9.194, 0.01], [-5.987, 0.027], [-3.2, 0.018], [0.0, 0.0]]\nKnown first future position [x1, y1]: [3.937, -0.038]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_071/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_071", "ground_truth": "[[7.857, -0.156], [12.08, -0.327], [17.514, -0.565], [22.225, -0.778]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-19.316, 0.257], [-14.548, 0.22], [-9.649, 0.153], [-4.575, 0.05], [0.0, -0.0]]\nKnown first future position [x1, y1]: [5.222, 0.002]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_074/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_074", "ground_truth": "[[10.459, 0.017], [15.775, 0.01], [20.972, -0.005], [26.157, -0.024]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-8.783, -1.751], [-6.698, -0.889], [-4.486, -0.351], [-2.248, -0.077], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [2.287, -0.041]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_107/group_001_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_001", "scene_id": "scene_107", "ground_truth": "[[4.623, -0.142], [6.977, -0.274], [9.327, -0.409], [11.545, -0.518]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.092, 0.467], [-9.693, 0.335], [-6.258, 0.177], [-2.913, 0.055], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [2.152, -0.015]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_117/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_117", "ground_truth": "[[3.503, -0.026], [4.523, -0.046], [5.44, -0.062], [6.264, -0.076]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-3.493, 0.129], [-2.398, 0.067], [-1.43, 0.02], [-0.604, 0.003], [0.0, -0.0]]\nKnown first future position [x1, y1]: [0.58, -0.001]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_122/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_122", "ground_truth": "[[1.019, -0.001], [1.276, -0.001], [1.367, -0.007], [1.356, -0.014]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.101, 0.02], [0.019, 0.012], [0.005, 0.007], [0.0, 0.003], [0.0, 0.0]]\nKnown first future position [x1, y1]: [0.0, -0.003]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_132/group_006_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_006", "scene_id": "scene_132", "ground_truth": "[[0.0, -0.005], [0.0, -0.007], [0.0, -0.009], [0.0, -0.011]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-2.339, 0.072], [-1.891, 0.04], [-1.343, 0.021], [-0.679, 0.007], [0.0, 0.0]]\nKnown first future position [x1, y1]: [0.809, 0.002]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_135/group_006_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_006", "scene_id": "scene_135", "ground_truth": "[[2.111, 0.032], [3.294, 0.084], [4.534, 0.162], [5.791, 0.245]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-20.882, 0.106], [-15.323, 0.078], [-9.798, 0.049], [-4.35, 0.024], [0.0, 0.0]]\nKnown first future position [x1, y1]: [5.391, -0.048]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_145/group_005_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_005", "scene_id": "scene_145", "ground_truth": "[[10.631, -0.116], [15.665, -0.206], [20.031, -0.28], [25.239, -0.364]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-11.026, 0.107], [-8.454, 0.067], [-5.705, 0.037], [-2.884, 0.018], [0.0, 0.0]]\nKnown first future position [x1, y1]: [2.888, -0.012]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_163/group_006_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_006", "scene_id": "scene_163", "ground_truth": "[[5.712, -0.017], [8.363, -0.019], [10.936, -0.007], [13.363, 0.011]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.082, 0.1], [-10.032, 0.062], [-6.786, 0.029], [-3.29, 0.013], [0.0, 0.0]]\nKnown first future position [x1, y1]: [3.83, -0.012]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_182/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_182", "ground_truth": "[[7.818, -0.018], [11.854, -0.01], [16.091, 0.01], [20.547, 0.035]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-1.866, 0.001], [-1.724, -0.003], [-1.451, -0.004], [-0.821, -0.007], [0.0, 0.0]]\nKnown first future position [x1, y1]: [0.956, 0.017]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_206/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_206", "ground_truth": "[[1.911, 0.046], [2.727, 0.072], [3.493, 0.09], [4.094, 0.101]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-15.266, -0.068], [-11.363, -0.037], [-7.499, -0.019], [-3.713, 0.001], [0.0, -0.0]]\nKnown first future position [x1, y1]: [2.864, -0.012]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_213/group_006_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_006", "scene_id": "scene_213", "ground_truth": "[[6.027, -0.007], [9.763, 0.014], [13.746, 0.062], [17.555, 0.174]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-14.244, -0.831], [-10.508, -0.492], [-6.838, -0.223], [-3.293, -0.05], [0.0, -0.0]]\nKnown first future position [x1, y1]: [3.934, -0.06]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_240/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_240", "ground_truth": "[[7.214, -0.199], [10.775, -0.443], [14.355, -0.787], [18.115, -1.22]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-18.919, -0.762], [-14.323, -0.441], [-9.2, -0.194], [-4.861, -0.071], [0.0, 0.0]]\nKnown first future position [x1, y1]: [4.818, -0.042]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_242/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_242", "ground_truth": "[[9.686, -0.169], [15.078, -0.36], [20.093, -0.558], [25.155, -0.743]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-5.275, 1.08], [-4.38, 0.743], [-3.117, 0.375], [-1.785, 0.126], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [2.047, 0.153]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_243/group_001_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_001", "scene_id": "scene_243", "ground_truth": "[[4.262, 0.74], [6.511, 1.861], [8.686, 3.548], [10.663, 5.772]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-1.006, 0.016], [-0.393, 0.005], [-0.052, -0.001], [0.037, -0.002], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [-0.001, 0.001]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_247/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_247", "ground_truth": "[[0.138, 0.002], [0.455, 0.002], [1.215, -0.001], [2.321, -0.025]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-13.618, 0.333], [-10.121, 0.227], [-6.636, 0.135], [-3.289, 0.05], [0.0, 0.0]]\nKnown first future position [x1, y1]: [3.033, -0.031]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_272/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_272", "ground_truth": "[[5.858, -0.033], [8.583, 0.008], [11.148, 0.056], [13.573, 0.103]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-17.369, 0.098], [-12.89, 0.075], [-8.451, 0.044], [-4.166, 0.022], [0.0, 0.0]]\nKnown first future position [x1, y1]: [4.099, -0.039]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_300/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_300", "ground_truth": "[[8.167, -0.07], [12.263, -0.11], [16.36, -0.148], [20.548, -0.202]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.656, 0.01], [-0.646, 0.009], [-0.531, 0.007], [-0.384, 0.004], [0.0, 0.0]]\nKnown first future position [x1, y1]: [0.514, 0.005]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_304/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_304", "ground_truth": "[[1.167, 0.017], [1.864, 0.04], [2.387, 0.072], [3.184, 0.142]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.366, 0.015], [-0.117, 0.011], [-0.004, 0.007], [0.007, 0.004], [0.0, -0.0]]\nKnown first future position [x1, y1]: [-0.001, -0.003]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_310/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_310", "ground_truth": "[[-0.001, -0.005], [-0.001, -0.007], [-0.001, -0.009], [-0.001, -0.01]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-8.922, 0.176], [-7.121, 0.135], [-4.993, 0.087], [-2.502, 0.041], [0.0, -0.0]]\nKnown first future position [x1, y1]: [3.138, -0.03]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_313/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_313", "ground_truth": "[[6.618, 0.008], [10.656, 0.054], [14.602, 0.099], [18.82, 0.163]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-4.001, -0.03], [-2.954, -0.0], [-1.938, 0.01], [-0.946, 0.006], [0.0, -0.0]]\nKnown first future position [x1, y1]: [1.045, -0.003]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_316/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_316", "ground_truth": "[[1.873, 0.002], [2.897, 0.013], [3.876, 0.021], [4.868, 0.022]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-8.475, 0.063], [-6.029, 0.061], [-3.726, 0.047], [-1.627, 0.022], [0.0, -0.0]]\nKnown first future position [x1, y1]: [1.98, -0.002]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_341/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_341", "ground_truth": "[[3.948, 0.028], [5.927, 0.071], [7.691, 0.129], [9.457, 0.208]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]\nKnown first future position [x1, y1]: [-0.0, -0.0]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_362/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_362", "ground_truth": "[[-0.0, -0.0], [-0.0, -0.0], [-0.0, -0.0], [-0.0, -0.0]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.0, 0.0], [-0.0, 0.0], [0.0, -0.0], [0.0, 0.0], [0.0, 0.0]]\nKnown first future position [x1, y1]: [-0.0, 0.0]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_402/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_402", "ground_truth": "[[-0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [-0.0, 0.0]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-5.368, -0.298], [-3.948, -0.21], [-2.6, -0.108], [-1.402, -0.028], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [1.764, -0.071]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_413/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_413", "ground_truth": "[[3.813, -0.279], [6.316, -0.644], [8.658, -1.023], [11.811, -1.571]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-9.046, -0.262], [-6.975, -0.075], [-4.887, -0.011], [-2.586, 0.01], [0.0, 0.0]]\nKnown first future position [x1, y1]: [2.768, -0.037]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_421/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_421", "ground_truth": "[[5.725, -0.121], [8.728, -0.244], [12.215, -0.394], [15.481, -0.522]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-12.804, 0.12], [-9.622, 0.077], [-6.429, 0.049], [-3.182, 0.024], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [2.874, -0.023]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_433/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_433", "ground_truth": "[[6.092, -0.038], [9.314, -0.038], [12.55, -0.055], [16.089, -0.036]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.928, -0.115], [-8.291, -0.056], [-5.591, -0.014], [-2.612, 0.009], [0.0, -0.0]]\nKnown first future position [x1, y1]: [2.64, -0.024]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_504/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_504", "ground_truth": "[[5.246, -0.067], [7.906, -0.163], [10.546, -0.272], [13.35, -0.368]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.282, 0.006], [-7.486, 0.03], [-4.81, 0.021], [-2.295, 0.016], [0.0, 0.0]]\nKnown first future position [x1, y1]: [1.97, -0.031]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_511/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_511", "ground_truth": "[[3.611, -0.047], [4.952, -0.068], [6.057, -0.077], [7.054, -0.084]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-8.906, -1.582], [-6.268, -0.916], [-3.791, -0.37], [-2.036, -0.101], [0.0, 0.0]]\nKnown first future position [x1, y1]: [1.821, -0.167]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_525/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_525", "ground_truth": "[[3.701, -0.636], [5.518, -1.467], [7.193, -2.688], [8.718, -4.358]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.258, 0.503], [-7.373, 0.296], [-4.77, 0.15], [-2.349, 0.048], [0.0, 0.0]]\nKnown first future position [x1, y1]: [2.535, 0.007]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_540/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_540", "ground_truth": "[[5.412, 0.077], [8.597, 0.212], [11.978, 0.429], [15.562, 0.695]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-10.623, -2.697], [-8.64, -1.687], [-5.833, -0.717], [-2.842, -0.161], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [3.273, -0.165]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_542/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_542", "ground_truth": "[[6.608, -0.642], [10.053, -1.423], [13.586, -2.456], [17.261, -3.704]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.07, -0.0], [-0.07, -0.0], [-0.07, -0.0], [-0.07, -0.0], [0.0, 0.0]]\nKnown first future position [x1, y1]: [0.366, 0.001]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_580/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_580", "ground_truth": "[[1.097, 0.006], [2.191, 0.018], [3.606, 0.03], [5.254, 0.043]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-18.968, 0.25], [-14.278, 0.167], [-9.515, 0.103], [-4.751, 0.051], [0.0, 0.0]]\nKnown first future position [x1, y1]: [4.677, -0.038]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_605/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_605", "ground_truth": "[[9.28, -0.058], [13.79, -0.08], [18.42, -0.079], [23.079, -0.089]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-18.969, 0.23], [-14.223, 0.169], [-9.443, 0.097], [-4.675, 0.053], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [4.639, -0.038]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_611/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_611", "ground_truth": "[[9.296, -0.077], [13.882, -0.115], [18.495, -0.152], [23.113, -0.202]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-20.126, 0.168], [-15.2, 0.133], [-10.707, 0.096], [-4.539, 0.033], [0.0, -0.0]]\nKnown first future position [x1, y1]: [4.512, -0.051]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_612/group_007_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_007", "scene_id": "scene_612", "ground_truth": "[[9.011, -0.118], [12.592, -0.209], [16.58, -0.337], [21.44, -0.514]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-7.571, 0.181], [-5.399, 0.111], [-3.372, 0.055], [-1.544, 0.017], [0.0, 0.0]]\nKnown first future position [x1, y1]: [1.337, -0.014]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_621/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_621", "ground_truth": "[[2.339, -0.02], [3.133, -0.026], [3.576, -0.036], [3.769, -0.043]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-0.0, 0.0], [-0.0, 0.0], [-0.0, 0.0], [0.0, 0.0], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [-0.0, -0.0]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_632/group_003_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_003", "scene_id": "scene_632", "ground_truth": "[[-0.0, -0.0], [-0.0, -0.0], [0.0, -0.0], [-0.0, -0.0]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-5.582, -1.033], [-3.986, -0.461], [-2.529, -0.15], [-1.251, -0.018], [0.0, 0.0]]\nKnown first future position [x1, y1]: [1.395, -0.075]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_702/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_702", "ground_truth": "[[2.965, -0.255], [4.789, -0.564], [6.863, -1.029], [9.177, -1.664]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-20.242, -0.904], [-15.097, -0.553], [-10.008, -0.244], [-4.981, -0.065], [0.0, 0.0]]\nKnown first future position [x1, y1]: [5.004, -0.114]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_731/group_004_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_004", "scene_id": "scene_731", "ground_truth": "[[10.011, -0.386], [14.969, -0.858], [19.842, -1.466], [24.538, -2.145]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-2.941, 0.005], [-1.964, 0.006], [-1.236, 0.007], [-0.572, 0.004], [0.0, -0.0]]\nKnown first future position [x1, y1]: [0.659, -0.003]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_800/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_800", "ground_truth": "[[1.531, -0.015], [2.69, -0.04], [3.854, -0.082], [4.909, -0.135]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, use the following past ego trajectory in the ego-centric coordinate system anchored at the 5th observed frame.\nPast observed ego positions (oldest to current, where the current frame is [0.0, 0.0]): [[-7.11, 0.294], [-5.317, 0.145], [-3.762, 0.066], [-2.132, 0.025], [-0.0, 0.0]]\nKnown first future position [x1, y1]: [2.844, -0.012]\nPredict only the following 4 future ego positions in the same coordinate system. 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]].", "task": "trajectory-prediction", "source_group_file": "scene_804/group_002_vehicle_annotations.json", "id": "TRJ-6", "group_id": "group_002", "scene_id": "scene_804", "ground_truth": "[[6.215, -0.0], [10.057, 0.056], [14.364, 0.162], [19.093, 0.305]]", "question_format": "OEQ", "scoring_type": "trajectory_waypoints" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_002/group_007_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_007", "scene_id": "scene_002", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, maintaining a steady speed and ending in the front-center region of the intersection.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_016/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_016", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a steady or slightly increasing speed, as the road ahead is clear and there are no visible braking cues or traffic signals.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_058/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_058", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly increasing speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_065/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_065", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a maintaining roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_080/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_080", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly decelerating speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_085/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_085", "ground_truth": "The ego vehicle is most likely to continue straight ahead, moving forward along the road toward the intersection, maintaining a roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_100/group_003_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_003", "scene_id": "scene_100", "ground_truth": "The ego vehicle will likely continue straight in its current lane, crossing the raised zebra crossing/speed hump and ending just beyond it on the same road. Speed should remain low with a slight deceleration or steady slow roll due to the crossing/hump.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_101/group_006_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_006", "scene_id": "scene_101", "ground_truth": "The ego vehicle will likely continue straight ahead in its current lane, staying centered and following the tree-lined road. It should end farther down the same lane with no lane change or turn, maintaining a steady speed or slightly accelerating.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_114/group_002_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_002", "scene_id": "scene_114", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a constant or slightly decreasing speed, as it approaches a crosswalk and intersection with visible pedestrians and traffic signs indicating caution.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_153/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_153", "ground_truth": "The ego vehicle will continue forward along the road, making a slight rightward adjustment to stay centered in the lane, and will end near the front-center region of the scene, maintaining a steady, low speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_162/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_162", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly decelerating speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_182/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_182", "ground_truth": "The ego vehicle will likely continue moving straight ahead through the intersection, ending near the far side/center of the junction. It appears to be rolling at low speed with a slight, steady acceleration rather than stopping or turning sharply.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_211/group_002_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_002", "scene_id": "scene_211", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly increasing speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_220/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_220", "ground_truth": "The ego vehicle is most likely to slow to near stop over the next 5 future frames, ending in the near the current position with a stopping / nearly stopped.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_246/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_246", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly decelerating speed as it approaches the intersection.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_268/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_268", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly decelerating speed as it approaches the intersection.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_281/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_281", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly increasing speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_360/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_360", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a stopping / nearly stopped.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_404/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_404", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, maintaining a steady speed and remaining in the center of the road.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_416/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_416", "ground_truth": "The ego vehicle is likely to continue straight through the intersection, maintaining its current lane and proceeding past the crosswalk/stop-line area. Speed appears low and relatively constant, with no clear indication of a right turn or significant lateral movement.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_470/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_470", "ground_truth": "The ego vehicle is most likely to continue straight through the intersection, maintaining a steady speed, and will end in the front-center region after passing the crosswalk.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_489/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_489", "ground_truth": "The ego vehicle is most likely to slow to near stop over the next 5 future frames, ending in the near the current position with a stopping / nearly stopped.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_493/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_493", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly decelerating speed as it approaches the intersection.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_499/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_499", "ground_truth": "The ego vehicle is most likely to slow to near stop over the next 5 future frames, ending in the near the current position with a stopping / nearly stopped.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_531/group_002_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_002", "scene_id": "scene_531", "ground_truth": "The ego vehicle is most likely to continue straight through the intersection, maintaining a steady speed, and will be in the center of the intersection by the end of the next 5 frames.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_534/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_534", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a stopping / nearly stopped.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_589/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_589", "ground_truth": "The ego vehicle is most likely to continue moving forward at a steady pace, maintaining its current lane and proceeding through the intersection, as traffic signals ahead are green and no immediate stopping conditions are visible.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_595/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_595", "ground_truth": "The ego vehicle is most likely to continue straight ahead, maintaining its current lane position, and will end in the center region of the road with a roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_599/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_599", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a decelerating.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_628/group_007_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_007", "scene_id": "scene_628", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a maintaining roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_644/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_644", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, maintaining a steady speed and approaching the intersection ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_667/group_007_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_007", "scene_id": "scene_667", "ground_truth": "The ego vehicle is most likely to continue straight ahead at a steady speed over the next 5 future frames, maintaining its current lane and approaching the intersection ahead without stopping.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_674/group_002_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_002", "scene_id": "scene_674", "ground_truth": "The ego vehicle is most likely to continue driving straight at a moderate speed over the next 5 future frames, ending in the front-center region without stopping.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_677/group_007_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_007", "scene_id": "scene_677", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a decelerating.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_680/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_680", "ground_truth": "The ego vehicle will continue forward on the current residential road, following the gentle leftward bend and ending farther into the shaded curve ahead. Speed appears low and steady, with a slight deceleration likely for the bend.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_684/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_684", "ground_truth": "The ego vehicle will continue forward along the gentle left-hand bend in the residential street, maintaining its lane. It will progress farther along the curved road toward the houses and gates ahead, moving at a low, steady speed with slight caution.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_688/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_688", "ground_truth": "The ego vehicle will likely continue straight in its current lane, following the road toward the vehicles ahead. It should end farther down the same lane behind the lead car, with speed steady to slightly decreasing due to traffic ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_689/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_689", "ground_truth": "The ego vehicle will likely continue straight ahead in its current lane, proceeding along the tree-lined road and ending farther down the lane behind the vehicle ahead. Speed appears steady to slightly increasing, with no lane change or turn indicated.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_696/group_007_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_007", "scene_id": "scene_696", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a constant or slightly decreasing speed, as the road ahead is clear and no lane changes or turns are indicated.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_697/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_697", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a maintaining roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_698/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_698", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a maintaining roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_699/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_699", "ground_truth": "The ego vehicle is most likely to continue straight ahead, turning slightly left to navigate the curved road, ending in the left-center region with a maintaining roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_765/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_765", "ground_truth": "The ego vehicle is most likely to continue moving straight at a moderate speed, passing through the intersection and proceeding toward the front-center region without stopping.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_768/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_768", "ground_truth": "The ego vehicle will continue straight ahead along the current road, maintaining a steady, low-to-moderate speed, and will end in the front-center region near the upcoming illuminated streetlights. There is no evidence of a leftward bend in the road ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_769/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_769", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a maintaining roughly constant speed.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_781/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_781", "ground_truth": "The ego vehicle is most likely to continue moving forward along the curved road, maintaining a steady speed, and will reach the intersection ahead in the front-center region without stopping.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_797/group_005_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_005", "scene_id": "scene_797", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a constant or slightly decreasing speed, as the road ahead is straight and no lane changes or turns are indicated.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_798/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_798", "ground_truth": "The ego vehicle is most likely to go straight over the next 5 future frames, ending in the front-center region with a constant or slightly decreasing speed, as the road ahead is wet and illuminated by oncoming headlights, suggesting cautious driving.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_799/group_004_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_004", "scene_id": "scene_799", "ground_truth": "The ego vehicle is most likely to continue straight ahead along the curved road, maintaining a steady speed, and will remain in the center of the lane, approaching the intersection ahead.", "question_format": "OEQ", "scoring_type": "open_oeq_text" }, { "question": "Based on the past 5 consecutive 360-degree multi-camera frames, briefly describe the ego vehicle's likely future path over the next 5 future frames (approximately 2.5 seconds). Mention the overall maneuver, endpoint region, and speed trend in 1-2 sentences.", "task": "trajectory-prediction", "source_group_file": "scene_810/group_001_vehicle_annotations.json", "id": "TRJ-7", "group_id": "group_001", "scene_id": "scene_810", "ground_truth": "The ego vehicle is most likely to continue straight ahead over the next 5 future frames, ending in the front-center region with a steady or slightly decelerating speed, as it approaches an intersection with red traffic lights and wet road conditions.", "question_format": "OEQ", "scoring_type": "open_oeq_text" } ] }