hustvl/VAD

VAD: Vectorized Scene Representation for Efficient Autonomous Driving

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 Wang^1,†

¹ Huazhong University of Science and Technology, ² Horizon Robotics

*: equal contribution, ^†: corresponding author.

arXiv Paper, ICCV 2023

News

• 14 July, 2023: VAD is accepted by ICCV 2023🎉! Code and models will be open source in July!
• 21 Mar, 2023: We release the VAD paper on arXiv. Code/Models are coming soon. Please stay tuned! ☕️

Introduction

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.

Results

• Open-loop planning results on nuScenes. See the paper for more details.

Catalog

• Code & Checkpoints Release
• Initialization

Contact

If you have any questions or suggestions about this repo, please feel free to contact us ([email protected], [email protected]).

Citation

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={ICCV},
  year={2023}
}

License

All code in this repository is under the Apache License 2.0.

Acknowledgement

VAD is based on the following projects: mmdet3d, detr3d, BEVFormer and MapTR. Many thanks for their excellent contributions to the community.