Unified End-to-End V2X Cooperative Autonomous Driving
<|reference_start|>Unified End-to-End V2X Cooperative Autonomous Driving: V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology. Current research primarily focuses on enhancing perception accuracy, often overlooking the systematic improvement of accident prediction accuracy through end-to-end learning, leading to insufficient attention to the safety issues of autonomous driving. To address this challenge, this paper introduces the UniE2EV2X framework, a V2X-integrated end-to-end autonomous driving system that consolidates key driving modules within a unified network. The framework employs a deformable attention-based data fusion strategy, effectively facilitating cooperation between vehicles and infrastructure. The main advantages include: 1) significantly enhancing agents' perception and motion prediction capabilities, thereby improving the accuracy of accident predictions; 2) ensuring high reliability in the data fusion process; 3) superior end-to-end perception compared to modular approaches. Furthermore, We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving.<|reference_end|>
@article{li2024unified,
    title={Unified End-to-End V2X Cooperative Autonomous Driving},
    author={Zhiwei Li, Bozhen Zhang, Lei Yang, Tianyu Shen, Nuo Xu, Ruosen Hao,
  Weiting Li, Tao Yan, Huaping Liu},
    journal={arXiv preprint arXiv:2405.03971},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03971},
    primaryClass={cs.CV cs.MA}
}