Card wording: recording rate (every fifth step, 4 Hz), plain sentences instead of semicolons and colons

README.md

@@ -35,27 +35,27 @@ University of California, Irvine · IEEE/CVF WACV 2027
 
 </div>
 
-This dataset contains driving recorded in CARLA under three explicit style instructions: **normal (neutral)**, **aggressive** and **conservative**. It forms the per-style retrieval databases and the style fine-tuning data of [PersonaDrive](https://huggingface.co/PervasiveAutonomyLab/PersonaDrive).
+This dataset contains driving recorded in CARLA under three explicit style instructions, **normal (neutral)**, **aggressive** and **conservative**. It forms the per-style retrieval databases and the style fine-tuning data of [PersonaDrive](https://huggingface.co/PervasiveAutonomyLab/PersonaDrive).
 
 PersonaDrive retrieves frames of the selected style as in-context demonstrations, so switching style only means searching a different part of this dataset.
 
-> **Style names.** The folders use the code's names:
-> - **`default` is normal, everyday driving** (the neutral instruction);
-> - `fast` is aggressive;
+> **Style names.** The folders use the code's names.
+> - **`default` is normal, everyday driving** (the neutral instruction).
+> - `fast` is aggressive.
 > - `slow` is conservative.
 >
-> The code also has a retrieval profile called `normal`. It is not part of this dataset: it is the style-agnostic index built from SimLingo's PDM-lite data.
+> The code also has a retrieval profile called `normal`. It is not part of this dataset, because it is the style-agnostic index built from SimLingo's PDM-lite data.
 
 ## At a glance
 
 | Folder | Style | Frames |
 |---|---|---:|
-| `default` | **Neutral: normal driving** | 9,613 |
+| `default` | **Neutral (normal driving)** | 9,613 |
 | `fast` | Aggressive | 7,143 |
 | `slow` | Conservative | 13,021 |
 | **Total** | | **29,777** |
 
-These are the frames the 15 participants drove (`driver1`–`driver15`). The `fast` and `slow` folders also contain `lb1_split`: routes of SimLingo's Leaderboard 1.0 split, recorded in the aggressive and conservative style. They are not part of the participant data and are not counted above. Stage-3 fine-tuning uses data from all three styles.
+These are the frames the 15 participants drove (`driver1`–`driver15`). The `fast` and `slow` folders also contain `lb1_split`, with routes of SimLingo's Leaderboard 1.0 split recorded in the aggressive and conservative style. They are not part of the participant data and are not counted above. Stage-3 fine-tuning uses data from all three styles.
 
 Each frame has a front camera image, the ego state and controls, the navigation targets and the surrounding objects. Most routes also have per-frame commentary and VQA annotations.
 
@@ -65,13 +65,13 @@ Every zip unpacks into the same directory tree, so extract the zips you need int
 
 | File | Size | Files | Contents |
 |---|---:|---:|---|
-| `data_default.zip` | 2.37 GB | 19,394 | Normal (neutral) driving: camera frames, per-frame measurements, surrounding objects. |
+| `data_default.zip` | 2.37 GB | 19,394 | Normal (neutral) driving, with camera frames, per-frame measurements and surrounding objects. |
 | `data_fast.zip` | 11.69 GB | 81,033 | Aggressive driving (`fast/driver*`), plus the `fast/lb1_split` fine-tuning routes with their retrieved context points (`icl_examples.json.gz`). |
 | `data_slow.zip` | 18.84 GB | 130,004 | Conservative driving (`slow/driver*`), plus the `slow/lb1_split` fine-tuning routes, organized like `data_fast.zip`. |
 | `commentary.zip` | 3.2 MB | 786 | Per-route `commentary.json` for all three styles. |
 | `drivelm.zip` | 27.8 MB | 775 | Per-route `vqa.json` (DriveLM-style question–answer pairs) for all three styles. |
 
-Total: 32.94 GB. `SHA256SUMS` lists the checksum of each zip.
+Total size 32.94 GB. `SHA256SUMS` lists the checksum of each zip.
 
 ```bash
 # everything
@@ -84,7 +84,7 @@ cd personadrive_data && sha256sum -c SHA256SUMS --ignore-missing
 for z in *.zip; do unzip -q "$z" -d "$PERSONADRIVE_DATA_ROOT"; done
 ```
 
-**Which zips you need:**
+**Which zips you need**
 
 | Task | Zips |
 |---|---|
@@ -99,24 +99,24 @@ Evaluation with retrieval opens the camera frames of the retrieved demonstration
 
 <img src="assets/hardware_rig.jpg" width="45%" align="right" alt="Driver-in-the-loop rig">
 
-15 participants drove CARLA Leaderboard routes at a driver-in-the-loop rig with a real wheel and pedals. The recording code is released with PersonaDrive ([`human_data_collection/`](https://github.com/PervasiveAutonomyLab/PersonaDrive/tree/main/human_data_collection)). Each participant drove each of their routes three times, each time under a different instruction:
+15 participants drove CARLA Leaderboard routes at a driver-in-the-loop rig with a real wheel and pedals. The recording code is released with PersonaDrive ([`human_data_collection/`](https://github.com/PervasiveAutonomyLab/PersonaDrive/tree/main/human_data_collection)). Each participant drove each of their routes three times, each time under a different instruction.
 
 | Folder | Style | What the driver was told |
 |---|---|---|
-| `default` | **Neutral: normal driving** | *"Follow traffic laws, navigate safely, and drive at a reasonable speed."* |
+| `default` | **Neutral (normal driving)** | *"Follow traffic laws, navigate safely, and drive at a reasonable speed."* |
 | `fast` | Aggressive | *"Drive as fast as possible without crashing; engage in aggressive behaviors such as tailgating and cutting off merging vehicles."* |
 | `slow` | Conservative | *"Target 10–15 mph on surface streets and 25–35 mph on the highway; yield to other vehicles whenever possible."* |
 
 **Rig.**
-- **Hardware:** a Next Level Racing GTrack bucket seat, three 50-inch monitors covering about 130° of view, and a Logitech G923 wheel and pedals.
-- **Simulator:** CARLA 0.9.15 ran synchronously at 20 Hz. The ego vehicle was the only human-controlled actor, and CARLA's Traffic Manager populated the surrounding traffic.
-- **Display:** a Pygame window showed the first-person view, the speed, the active navigation instruction and scenario, and a bird's-eye view of the surrounding vehicles (for manoeuvres such as merges).
+- **Hardware.** A Next Level Racing GTrack bucket seat, three 50-inch monitors covering about 130° of view, and a Logitech G923 wheel and pedals.
+- **Simulator.** CARLA 0.9.15 ran synchronously at 20 Hz. The ego vehicle was the only human-controlled actor, and CARLA's Traffic Manager populated the surrounding traffic.
+- **Display.** A Pygame window showed the first-person view, the speed, the active navigation instruction and scenario, and a bird's-eye view of the surrounding vehicles (for manoeuvres such as merges).
 
 **Protocol.**
-- **Disjoint from evaluation:** routes were drawn at random from the CARLA Leaderboard route sets, and none of them is a Bench2Drive evaluation route. Their scenario types also appear in Bench2Drive, so a retrieved demonstration can be relevant to an evaluation situation, but no evaluation episode is in the dataset.
-- **Three passes:** each participant drove each of their routes once per instruction, in randomized order, so that style is not confounded with route familiarity.
-- **Supervision:** participants were supervised to make sure they followed the instructions.
-- **Anonymity:** `driver<N>` is an anonymous participant index. The same index refers to the same participant in all three styles.
+- **Disjoint from evaluation.** Routes were drawn at random from the CARLA Leaderboard route sets, and none of them is a Bench2Drive evaluation route. Their scenario types also appear in Bench2Drive, so a retrieved demonstration can be relevant to an evaluation situation, but no evaluation episode is in the dataset.
+- **Three passes.** Each participant drove each of their routes once per instruction, in randomized order, so that style is not confounded with route familiarity.
+- **Supervision.** Participants were supervised to make sure they followed the instructions.
+- **Anonymity.** `driver<N>` is an anonymous participant index. The same index refers to the same participant in all three styles.
 
 | Style | Mean speed (mph) | In motion (mph) | Std. dev. | Stopped (%) |
 |---|---:|---:|---:|---:|
@@ -126,22 +126,22 @@ Evaluation with retrieval opens the camera frames of the retrieved demonstration
 
 <sub>Speed statistics computed from the recorded ego state, route-matched over the 55 routes driven under all three instructions. The instructions separate before any model is involved.</sub>
 
-**What is recorded.** Each route stores, at 4 Hz:
-- the front camera image, plus a shifted-camera copy used for augmentation;
-- the ego state from CARLA (speed, pose), which follows the participant's driving;
-- the controls `throttle`, `steer` and `brake` in `measurements.json`;
-- the navigation command and the next GPS target points from CARLA's route planner;
-- the objects around the ego vehicle: vehicles, pedestrians, traffic signs, traffic-light states.
+**What is recorded.** CARLA runs at 20 Hz and every fifth step is stored, so each route has the following at 4 Hz.
+- The front camera image, plus a shifted-camera copy used for augmentation.
+- The ego state from CARLA (speed, pose), which follows the participant's driving.
+- The controls `throttle`, `steer` and `brake` in `measurements.json`.
+- The navigation command and the next GPS target points from CARLA's route planner.
+- The surrounding vehicles, pedestrians, traffic signs and traffic-light states.
 
-### `commentary.zip`: what the car is doing, in words
+### `commentary.zip` (what the car is doing, in words)
 
-- **Contents:** one sentence per frame that says what the car does and why, together with the object that causes it. For example: *"Follow the route. Accelerate to drive with the target speed."*
-- **How it was made:** SimLingo's commentary generator produced it automatically after recording, from the recorded simulator state (route, hazards, surrounding objects). Participants did not write it.
+- **Contents.** One sentence per frame that says what the car does and why, together with the object that causes it. An example is *"Follow the route. Accelerate to drive with the target speed."*
+- **How it was made.** SimLingo's commentary generator produced it automatically after recording, from the recorded simulator state (route, hazards, surrounding objects). Participants did not write it.
 
-### `drivelm.zip`: questions and answers about each scene
+### `drivelm.zip` (questions and answers about each scene)
 
-- **Contents:** per-frame question–answer pairs about perception, prediction and planning, in the DriveLM format. For example: *Q: "What are the important objects in the scene?" A: "The important object in the scene is the black car to the front left of the ego vehicle."*
-- **How it was made:** a DriveLM-style generator produced them automatically from the recorded objects and ego state.
+- **Contents.** Per-frame question–answer pairs about perception, prediction and planning, in the DriveLM format. An example is *Q: "What are the important objects in the scene?" A: "The important object in the scene is the black car to the front left of the ego vehicle."*
+- **How it was made.** A DriveLM-style generator produced them automatically from the recorded objects and ego state.
 
 ## Layout
 
@@ -157,28 +157,28 @@ simlingo/
 └── drivelm/simlingo/<same relative paths>/vqa.json
 ```
 
-Each route folder (`<Town..>/`) contains:
+Each route folder (`<Town..>/`) contains these files.
 
 | File | Contents |
 |---|---|
 | `rgb/NNNN.jpg` | Front camera, one frame per dataset step (4 Hz). |
 | `rgb_augmented/NNNN.jpg` | Shifted-camera augmentation. |
-| `measurements.json` | Per-frame list: `speed`, `throttle`, `steer`, `brake`, `command`, `next_command`, `target_point(_next)`, `ego_matrix`, `route`, `aim_wp`, … |
+| `measurements.json` | Per-frame list of `speed`, `throttle`, `steer`, `brake`, `command`, `next_command`, `target_point(_next)`, `ego_matrix`, `route`, `aim_wp`, … |
 | `boxes.json` | Per-frame surrounding objects (`driver<N>` routes). |
 | `result.json`, `records.json.gz` | Simulator logs. |
-| `icl_examples.json.gz` | `lb1_split` routes only. Written by Stage 3a of the code: for every frame, the K = 2 most similar frames retrieved from the same style's database, i.e. the demonstrations the model was fine-tuned with (`rgb_path`, `speed_history`, `throttle_history`, `command`, `target_point(_next)`, `commentary`, `waypoints`). |
+| `icl_examples.json.gz` | `lb1_split` routes only. Written by Stage 3a of the code. For every frame it holds the K = 2 most similar frames retrieved from the same style's database, i.e. the demonstrations the model was fine-tuned with (`rgb_path`, `speed_history`, `throttle_history`, `command`, `target_point(_next)`, `commentary`, `waypoints`). |
 
-`commentary.json` and `vqa.json` are per-frame lists aligned with `measurements.json`; frames without an annotation are `null`.
+`commentary.json` and `vqa.json` are per-frame lists aligned with `measurements.json`, and frames without an annotation are `null`.
 
 ## Processing and privacy
 
-- **Anonymity:** participants are identified only by an index (`driver1`–`driver15`). The dataset contains no names or other personal information about participants.
-- **Frame paths in annotations:** the frame paths inside `commentary.json` and `vqa.json` were rewritten to paths relative to the data root (`simlingo/data/simlingo/...`).
-- **Paths in `icl_examples.json.gz`:** these keep the absolute paths of the machine they were built on. The PersonaDrive loader resolves both forms against `$PERSONADRIVE_DATA_ROOT`.
+- **Anonymity.** Participants are identified only by an index (`driver1`–`driver15`). The dataset contains no names or other personal information about participants.
+- **Frame paths in annotations.** The frame paths inside `commentary.json` and `vqa.json` were rewritten to paths relative to the data root (`simlingo/data/simlingo/...`).
+- **Paths in `icl_examples.json.gz`.** These keep the absolute paths of the machine they were built on. The PersonaDrive loader resolves both forms against `$PERSONADRIVE_DATA_ROOT`.
 
 ## Not included
 
-- **SimLingo's PDM-lite data** (`training_1_scenario` for Stage-3 fine-tuning; `training_1_scenario`, `training_3_scenarios` and SimLingo's own `lb1_split` for the `normal` index, see the code repository's [`datasets/README.md`](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/datasets/README.md)). It is the SimLingo dataset of Wayve Technologies Ltd and is distributed by its authors under their own licence; download it from [SimLingo](https://github.com/RenzKa/simlingo).
+- **SimLingo's PDM-lite data** (`training_1_scenario` for Stage-3 fine-tuning, and `training_1_scenario`, `training_3_scenarios` and SimLingo's own `lb1_split` for the `normal` index, see the code repository's [`datasets/README.md`](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/datasets/README.md)). It is the SimLingo dataset of Wayve Technologies Ltd and is distributed by its authors under their own licence. Download it from [SimLingo](https://github.com/RenzKa/simlingo).
 - **The FAISS indices and other retrieval artefacts.** Build them from this dataset with the [code repository](https://github.com/PervasiveAutonomyLab/PersonaDrive#3-build-the-retrieval-indices).
 
 ## Citation