vis_vad.mp4
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Bo Jiang1*, Shaoyu Chen1*, Qing Xu2, Bencheng Liao1, Jiajie Chen2, Helong Zhou2, Qian Zhang2, Wenyu Liu1, Chang Huang2, Xinggang Wang1,†
1 Huazhong University of Science and Technology, 2 Horizon Robotics
*: equal contribution, †: corresponding author.
21 Mar, 2023
: We release the VAD paper on Arxiv. Code/Models are coming soon. Please stay tuned! ☕️
VAD is a vectorized paradigm for end-to-end autonomous driving.
- We propose VAD, an end-to-end unified vectorized paradigm for autonomous driving. VAD models the driving scene as a fully vectorized representation, getting rid of computationally intensive dense rasterized representation and hand-designed post-processing steps.
- VAD implicitly and explicitly utilizes the vectorized scene information to improve planning safety, via query interaction and vectorized planning constraints.
- VAD achieves SOTA end-to-end planning performance, outperforming previous methods by a large margin. Not only that, because of the vectorized scene representation and our concise model design, VAD greatly improves the inference speed, which is critical for the real-world deployment of an autonomous driving system.
Method | L2 (m) 1s | L2 (m) 2s | L2 (m) 3s | Col. (%) 1s | Col. (%) 2s | Col. (%) 3s | FPS |
---|---|---|---|---|---|---|---|
ST-P3 | 1.33 | 2.11 | 2.90 | 0.23 | 0.62 | 1.27 | 1.6 |
UniAD | 0.48 | 0.96 | 1.65 | 0.05 | 0.17 | 0.71 | 1.8 |
VAD-Tiny | 0.46 | 0.76 | 1.12 | 0.21 | 0.35 | 0.58 | 16.8 |
VAD-Base | 0.41 | 0.70 | 1.05 | 0.07 | 0.17 | 0.41 | 4.5 |
- Code & Checkpoints Release
- Initialization
If you have any questions or suggestions about this repo, please feel free to contact us ([email protected], [email protected]).
If you find VAD useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@article{jiang2023vad,
title={VAD: Vectorized Scene Representation for Efficient Autonomous Driving},
author={Jiang, Bo and Chen, Shaoyu and Xu, Qing and Liao, Bencheng and Chen, Jiajie and Zhou, Helong and Zhang, Qian and Liu, Wenyu and Huang, Chang and Wang, Xinggang},
journal={arXiv preprint arXiv:2303.12077},
year={2023}
}
All code in this repository is under the Apache License 2.0.
VAD is based on the following projects: mmdet3d, detr3d, BEVFormer and MapTR. Many thanks for their excellent contributions to the community.