Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this issue, we introduce V2X-Seq, the first large-scale sequential V2X dataset, which includes data frames, trajectories, vector maps, and traffic lights captured from natural scenery. V2X-Seq comprises two parts: the sequential perception dataset, which includes more than 15,000 frames captured from 95 scenarios, and the trajectory forecasting dataset, which contains about 80,000 infrastructure-view scenarios, 80,000 vehicle-view scenarios, and 50,000 cooperative-view scenarios captured from 28 intersections' areas, covering 672 hours of data. Based on V2X-Seq, we introduce three new tasks for vehicle-infrastructure cooperative (VIC) autonomous driving: VIC3D Tracking, Online-VIC Forecasting, and Offline-VIC Forecasting. We also provide benchmarks for the introduced tasks. Find data, code, and more up-to-date information at \href{https://github.com/AIR-THU/DAIR-V2X-Seq}{https://github.com/AIR-THU/DAIR-V2X-Seq}.

翻译：利用基础设施和车辆侧信息跟踪与预测周围交通参与者的行为，可显著提升自动驾驶的决策能力和安全性。然而，真实世界序列数据集的匮乏限制了该领域的研究进展。针对这一问题，我们提出了V2X-Seq——首个大规模序列车路协同数据集，包含从自然场景中采集的数据帧、轨迹、矢量地图及交通灯信息。V2X-Seq由两部分组成：序列感知数据集（涵盖95个场景的15,000余帧数据）和轨迹预测数据集（包含从28个路口区域采集的约80,000个基础设施视角场景、80,000个车辆视角场景及50,000个协同视角场景，覆盖672小时数据）。基于V2X-Seq，我们提出了车路协同自动驾驶的三项新任务：VIC3D跟踪、在线VIC预测和离线VIC预测，并提供了相应基准。数据和代码及更多最新信息详见 \href{https://github.com/AIR-THU/DAIR-V2X-Seq}{https://github.com/AIR-THU/DAIR-V2X-Seq}。