聚焦车联网协同自动驾驶，破解感知与规划难题
Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
- 基于UniV2X框架与V2X-Seq-SPD数据集，设计双赛道挑战赛
- 超30支队伍参与，验证了带宽感知融合与多智能体规划的有效性
- 适合研究车联网协同感知与决策的学者和工程师
随着自动驾驶技术快速发展，车联网（V2X）通信成为拓展感知范围、提升行车安全的关键。然而，在通信带宽受限与动态环境等实际约束下，如何融合车辆与基础设施的多源传感器数据仍面临重大挑战。为此，我们组织了端到端车联网协同自动驾驶挑战赛，包含协同时序感知与协同端到端规划两个赛道。基于UniV2X框架与V2X-Seq-SPD数据集，该挑战吸引了全球30余支团队参与，建立了统一的评估基准。本文介绍了挑战赛的设计与成果，揭示了带宽感知融合、鲁棒多智能体规划与异构传感器集成等关键问题，并分析了顶尖方案中的技术趋势。通过应对真实场景下的通信与数据融合约束，挑战赛推动了可扩展、可靠的车联网协同自动驾驶系统发展。
原文摘要 · Abstract (English)
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.
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