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HqH1111/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… See the full description on the dataset page: https://huggingface.co/datasets/HqH1111/AutoMoT-PDM-Lite-BEV-Encoder-Indexes.

sourceHugging Facecc-by-nc-4.0updated 2mo agoView on Hugging Face

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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.