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9897e20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ## 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"`.
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