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--- |
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license: mit |
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task_categories: |
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- robotics |
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- image-to-text |
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tags: |
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- autonomous-driving |
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- carla |
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- simlingo |
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- behavioral-cloning |
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size_categories: |
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- 100K<n<1M |
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--- |
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# SimLingo CARLA Dataset (Raw, 4Hz) |
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Raw driving data from CARLA simulator. No transformations or derived fields - all original measurements preserved as-is. |
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## Dataset Summary |
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- **Source**: [SimLingo](https://huggingface.co/datasets/RenzKa/simlingo) (CVPR 2025) |
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- **Scale**: 228,757 frames (23 shards) |
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- **Frame Rate**: 4 FPS |
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- **Resolution**: 1024x512 RGB |
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- **Routes**: Complete driving episodes (routes never split across shards) |
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## Column Schema |
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### Core Fields |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `route_id` | string | Route identifier | |
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| `frame_idx` | int32 | Frame index within route | |
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| `image` | bytes | Original JPEG image bytes | |
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### Control Signals (Raw) |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `steer` | float32 | Steering [-1, 1] | |
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| `throttle` | float32 | Throttle [0, 1] | |
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| `brake` | bool | Brake applied | |
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### Vehicle State |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `speed` | float32 | Current speed (m/s) | |
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| `target_speed` | float32 | Target speed | |
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| `speed_limit` | float32 | Speed limit | |
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| `theta` | float32 | Heading angle | |
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| `angle` | float32 | Angle to target | |
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### Navigation |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `command` | int32 | Navigation command | |
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| `next_command` | int32 | Next navigation command | |
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| `pos_global` | string (JSON) | Global position [x, y] | |
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| `target_point` | string (JSON) | Target point | |
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| `target_point_next` | string (JSON) | Next target point | |
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| `aim_wp` | string (JSON) | Aim waypoint | |
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| `route` | string (JSON) | Planned route waypoints | |
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| `route_original` | string (JSON) | Original route waypoints | |
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| `changed_route` | bool | Route was changed | |
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### Hazards & Environment |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `junction` | bool | In junction | |
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| `vehicle_hazard` | bool | Vehicle hazard detected | |
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| `vehicle_affecting_id` | int32 | ID of affecting vehicle | |
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| `walker_hazard` | bool | Pedestrian hazard | |
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| `walker_affecting_id` | int32 | ID of affecting pedestrian | |
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| `light_hazard` | bool | Traffic light hazard | |
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| `stop_sign_hazard` | bool | Stop sign hazard | |
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| `stop_sign_close` | bool | Stop sign nearby | |
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| `walker_close` | bool | Pedestrian nearby | |
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| `walker_close_id` | int32 | ID of nearby pedestrian | |
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| `speed_reduced_by_obj_type` | string | Object type causing speed reduction | |
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| `speed_reduced_by_obj_id` | int32 | Object ID causing speed reduction | |
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| `speed_reduced_by_obj_distance` | float32 | Distance to speed-reducing object | |
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| `control_brake` | bool | Control brake applied | |
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### Augmentation (from SimLingo) |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `augmentation_translation` | float32 | Translation augmentation | |
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| `augmentation_rotation` | float32 | Rotation augmentation | |
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### Transforms |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `ego_matrix` | string (JSON) | 4x4 ego vehicle transform matrix | |
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| `boxes` | string (JSON) | 3D bounding boxes for all objects | |
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### Commentary (Optional) |
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| Column | Type | Description | |
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|--------|------|-------------| |
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| `commentary` | string | Natural language commentary | |
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| `commentary_data` | string (JSON) | Full commentary object with metadata | |
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## Usage |
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```python |
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from datasets import load_dataset |
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import json |
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ds = load_dataset("TESS-Computer/carla-simlingo-raw", split="train") |
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sample = ds[0] |
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print(sample['route_id']) |
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print(sample['steer'], sample['throttle'], sample['brake']) |
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print(sample['speed']) |
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# Parse JSON fields |
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pos = json.loads(sample['pos_global']) |
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boxes = json.loads(sample['boxes']) if sample['boxes'] else [] |
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``` |
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## Data Collection |
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- **Simulator**: CARLA 0.9.15 (Leaderboard 2.0) |
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- **Expert**: PDM-Lite (rule-based, 100% route completion) |
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- **Scenarios**: Single-scenario routes with random weather |
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- **Towns**: Towns 1-13 |
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## Citation |
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```bibtex |
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@inproceedings{renz2025simlingo, |
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title={SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment}, |
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author={Renz, Katrin and Chen, Long and Arani, Elahe and Sinavski, Oleg}, |
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booktitle={CVPR}, |
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year={2025}, |
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} |
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``` |
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## License |
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MIT (dataset processing code). Original data subject to [SimLingo](https://huggingface.co/datasets/RenzKa/simlingo) and [CARLA](https://carla.org/) licenses. |
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