| --- |
| library_name: pytorch |
| tags: |
| - autonomous-driving |
| - flow-matching |
| - occupancy-prediction |
| - trajectory-prediction |
| - bench2drive |
| --- |
| |
| # OCCWAM B2D v9 step18000 |
|
|
| This repository contains the deployed OCCWAM joint flow-matching checkpoint |
| used by the v9 Bench2Drive closed-loop agent. |
|
|
| ## Checkpoint |
|
|
| | Field | Value | |
| | --- | --- | |
| | File | `v9_step18000/v9step18000_FULL_merged.pt` | |
| | Size | 1,656,918,530 bytes | |
| | SHA-256 | `43d914c7afa8c892b03023c96a6f3619b1aa37d874e103d28a12824a1c42316f` | |
| | Envelope | `occwam-fullmot-best-model-v1` | |
| | Global step | 18000 | |
| | Config SHA-256 | `7948cd560c83dc2b519be5ff882c5ff68f43daf3456e4fb774d4ae659017651b` | |
|
|
| Training and inference code: <https://github.com/HQH111/OCCWAM> |
|
|
| The inference graph is scene-frame joint OCC/trajectory flow matching from |
| white noise, followed by a hard scene-to-future-ego warp, VAE re-encoding and |
| one velocity-parameterised ego-rollout refinement. |
|
|
| ## Required contract |
|
|
| ```bash |
| export AEC_TRAJ1_CTX=2 |
| export AEC_TRAJ2_VFM=1 |
| export OCCWAM_TP_NORM_UNIFIED=1 |
| ``` |
|
|
| The unified target-point normalisation flag must be set at both training and |
| inference. Omitting it silently changes the input conditioning. |
|
|
| This checkpoint does not embed the frozen Qwen3-VL, Stage-3 carrier, OCC VAE, |
| TransFuser++ and Profile-B assets. Their hashes and roles are documented in the |
| GitHub repository's runtime and training configs. |
|
|
|
|
