| ## Why do we need to process Waymo files? |
| - GPUDrive works with a subset of information given in a Waymo scene, in json format. |
| - Processing script deserializes Waymo tfrecords according to their protobuf format and generates json files compatible with GPUDrive. |
|
|
| ## What exactly happens when processing a Waymo tfrecord? |
| - Each Waymo tfrecord contains about 500 scenes, with all assiciated information regarding road objects, vehicles, traffic lights, etc. |
| - [process_waymo_files.py](process_waymo_files.py) generates a json file for each scene in the tfrecord in parallel. |
| - During processing, we mark certain vehicles as "expert" meaning they cannot be controlled in the sim. |
|
|
| ## Why do we mark some vehicles as experts? |
| - In the ground truth Waymo vehicle trajectories, a small minority of them involve crossing a road edge entity. |
| - GPUDrive interprets this as a collision and subsequently going offroad. Hence these vehicles fail to make it to their goals. |
| - This leads to inaccuracies in evaluating policies with respect to rate of reaching goals. |
| - Marking them as "expert" makes them uncontrolled in the sim, and hence are not considered in evaluation. |
|
|
| ## How do we check if a vehicle should be marked expert? |
| - For each Waymo scene we process, we construct all road edges, and all vehicle trajectories. |
| - For each vehicle trajectory, if it intersects with a road edge, we set an expert flag in the json. |
| - Then when a scene is loaded into the sim, it checks for this flag and behaves accordingly. |
|
|
| ## How many of these experts even exist in the first place? |
| - In the entire training set (>100k scenes), we found 31837 vehicles (roughly 0.35% of all vehicles) marked expert. |
|  |
|
|
| ## NuScenes adapter |
|
|
| GPUDrive still consumes the same JSON schema as the Waymo converter. For |
| NuScenes, use the adapter to convert keyframe annotations and map layers into |
| that schema: |
|
|
| ```bash |
| python data_utils/process_nuscenes_files.py \ |
| --dataroot /path/to/nuscenes \ |
| --version v1.0-mini \ |
| --split mini_train \ |
| --output-dir data/processed/nuscenes |
| ``` |
|
|
| The converter synthesizes an ego vehicle from NuScenes ego poses, maps supported |
| dynamic categories to `vehicle`, `pedestrian`, and `cyclist`, and interpolates |
| 2 Hz keyframe tracks to GPUDrive's default 10 Hz, 91-step trajectory format. The |
| default output prefix is `tfrecord-nuscenes`, so `SceneDataLoader` can discover |
| the files with its default `file_prefix="tfrecord"`. |
|
|
