Datasets:
File size: 1,227 Bytes
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license: cc-by-nc-4.0
task_categories:
- text-generation
pretty_name: AutoMoT PDM-Lite BEV Encoder Indexes
---
# AutoMoT PDM-Lite BEV Encoder Indexes
This dataset provides the prepared PDM-Lite JSONL indexes for AutoMoT training.
## Files
- `pdm_lite_2hz_2tp_train_bev_encoder.jsonl`
- `pdm_lite_2hz_2tp_val_bev_encoder.jsonl`
Each row contains four historical front-camera paths in `image`, the current
front-camera path in `front`, trajectory and route supervision, future-speed
supervision, and a reference to the precomputed current-frame BEV feature:
- `bev_encoder_feature`
- `bev_encoder_feature_frame`
The prompt contains four `<image>` tokens for Qwen3-VL reasoning and one
`<bev>` token for the action branch. The BEV feature path is relative to
`PDM_DATA_DIR` and points to:
```text
<PDM_DATA_DIR>/<scenario>/<route>/bev_encoder_feature/route_features.pt
```
AutoMoT reads 64 spatial BEV tokens (`8 x 8`) from this file. If the cache is
unavailable, `front` identifies the RGB frame used by the online BEV encoder
fallback together with the corresponding LiDAR BEV input.
Training instructions are available in the
[AutoMoT repository](https://github.com/OscarHuangWind/AutoMoT/tree/release-training).
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