Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
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2025
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| author | Hao, Ruiyang Yu, Haibao Zhong, Jiaru Wang, Chuanye Wang, Jiahao Kan, Yiming Yang, Wenxian Fan, Siqi Yin, Huilin Qiu, Jianing Mu, Yao Sun, Jiankai Chen, Li Zimmer, Walter Zhang, Dandan Zhang, Shanghang Schwager, Mac Luo, Ping Nie, Zaiqing |
| author_facet | Hao, Ruiyang Yu, Haibao Zhong, Jiaru Wang, Chuanye Wang, Jiahao Kan, Yiming Yang, Wenxian Fan, Siqi Yin, Huilin Qiu, Jianing Mu, Yao Sun, Jiankai Chen, Li Zimmer, Walter Zhang, Dandan Zhang, Shanghang Schwager, Mac Luo, Ping Nie, Zaiqing |
| contents | With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of sight. However, integrating multi-source sensor data from both ego-vehicles and infrastructure under real-world constraints, such as limited communication bandwidth and dynamic environments, presents significant technical challenges. To facilitate research in this area, we organized the End-to-End Autonomous Driving through V2X Cooperation Challenge, which features two tracks: cooperative temporal perception and cooperative end-to-end planning. Built on the UniV2X framework and the V2X-Seq-SPD dataset, the challenge attracted participation from over 30 teams worldwide and established a unified benchmark for evaluating cooperative driving systems. This paper describes the design and outcomes of the challenge, highlights key research problems including bandwidth-aware fusion, robust multi-agent planning, and heterogeneous sensor integration, and analyzes emerging technical trends among top-performing solutions. By addressing practical constraints in communication and data fusion, the challenge contributes to the development of scalable and reliable V2X-cooperative autonomous driving systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_21610 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition Hao, Ruiyang Yu, Haibao Zhong, Jiaru Wang, Chuanye Wang, Jiahao Kan, Yiming Yang, Wenxian Fan, Siqi Yin, Huilin Qiu, Jianing Mu, Yao Sun, Jiankai Chen, Li Zimmer, Walter Zhang, Dandan Zhang, Shanghang Schwager, Mac Luo, Ping Nie, Zaiqing Robotics Computer Vision and Pattern Recognition I.4.9 With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of sight. However, integrating multi-source sensor data from both ego-vehicles and infrastructure under real-world constraints, such as limited communication bandwidth and dynamic environments, presents significant technical challenges. To facilitate research in this area, we organized the End-to-End Autonomous Driving through V2X Cooperation Challenge, which features two tracks: cooperative temporal perception and cooperative end-to-end planning. Built on the UniV2X framework and the V2X-Seq-SPD dataset, the challenge attracted participation from over 30 teams worldwide and established a unified benchmark for evaluating cooperative driving systems. This paper describes the design and outcomes of the challenge, highlights key research problems including bandwidth-aware fusion, robust multi-agent planning, and heterogeneous sensor integration, and analyzes emerging technical trends among top-performing solutions. By addressing practical constraints in communication and data fusion, the challenge contributes to the development of scalable and reliable V2X-cooperative autonomous driving systems. |
| title | Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition |
| topic | Robotics Computer Vision and Pattern Recognition I.4.9 |
| url | https://arxiv.org/abs/2507.21610 |