{"ID":2889110,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21610","arxiv_id":"2507.21610","title":"Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition","abstract":"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.","short_abstract":"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 infra...","url_abs":"https://arxiv.org/abs/2507.21610","url_pdf":"https://arxiv.org/pdf/2507.21610v2","authors":"[\"Ruiyang Hao\",\"Haibao Yu\",\"Jiaru Zhong\",\"Chuanye Wang\",\"Jiahao Wang\",\"Yiming Kan\",\"Wenxian Yang\",\"Siqi Fan\",\"Huilin Yin\",\"Jianing Qiu\",\"Yao Mu\",\"Jiankai Sun\",\"Li Chen\",\"Walter Zimmer\",\"Dandan Zhang\",\"Shanghang Zhang\",\"Mac Schwager\",\"Ping Luo\",\"Zaiqing Nie\"]","published":"2025-07-29T09:06:40Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}