- This repo contains the training, open-loop evaluation, and closed-loop evaluation code for BEVFormer, UniAD , VAD in Bench2Drive.
- We merge multiple dependencies of UniAD and VAD including mmcv, mmseg, mmdet, and mmdet3d (v0.17.1) into a single library. As a result, it could support latest pytorch and advanced frameworks like deepspeed for acceleration.
- Use "git checkout tcp/admlp" to obtain their corresponding training and evaluation code.
- Installation
- Prepare Dataset
- Train and Open-Loop Eval
- Closed-Loop Eval in CARLA
- Convert Codes from Nuscenes to Bench2Drive
As stated in the news at 2024/08/27, there are several fixed bugs and changed protocols. Thus, the old version of closed-loop performance is deprecated.
| Method | L2 (m) 2s | Driving Score | Success Rate(%) | Config | Download | Eval Json |
|---|---|---|---|---|---|---|
| UniAD-Tiny | 0.80 | 40.73 (deprecated 32.00) | 13.18 (deprecated 9.54) | config | Hugging Face/Baidu Cloud | New Version |
| UniAD-Base | 0.73 | 45.81 (deprecated 37.72) | 16.36 (deprecated 9.54) | config | Hugging Face/Baidu Cloud | New Version |
| VAD | 0.91 | 42.35 (deprecated 39.42) | 15.00 (deprecated 10.00) | config | Hugging Face/Baidu Cloud | New Version |
| Method | mAP | NDS | Config | Download |
|---|---|---|---|---|
| BEVFormer-Tiny | 0.37 | 0.43 | config | Hugging Face/Baidu Cloud |
| BEVFormer-Base | 0.63 | 0.67 | config | Hugging Face/Baidu Cloud |
We provide some visualization videos and qualitatively analysis for TCP-traj, UniAD-Base, VAD-Base at here. You may refer to https://github.com/Thinklab-SJTU/Bench2DriveZoo/blob/uniad/vad/team_code/vad_b2d_agent_visualize.py to write your own visualization code.