VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
<|reference_start|>VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning: Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging. In this work, to cope with the uncertainty problem, we propose VADv2, an end-to-end driving model based on probabilistic planning. VADv2 takes multi-view image sequences as input in a streaming manner, transforms sensor data into environmental token embeddings, outputs the probabilistic distribution of action, and samples one action to control the vehicle. Only with camera sensors, VADv2 achieves state-of-the-art closed-loop performance on the CARLA Town05 benchmark, significantly outperforming all existing methods. It runs stably in a fully end-to-end manner, even without the rule-based wrapper. Closed-loop demos are presented at https://hgao-cv.github.io/VADv2.<|reference_end|>
@article{chen2024vadv2:,
    title={VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic
  Planning},
    author={Shaoyu Chen, Bo Jiang, Hao Gao, Bencheng Liao, Qing Xu, Qian Zhang,
  Chang Huang, Wenyu Liu, Xinggang Wang},
    journal={arXiv preprint arXiv:2402.13243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13243},
    primaryClass={cs.CV cs.RO}
}