V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure
  Cooperative Perception and Forecasting
Haibao Yu Wenxian Yang Hongzhi Ruan Zhenwei Yang Yingjuan Tang Xu Gao Xin Hao Yifeng Shi Yifeng Pan Ning Sun Juan Song Jirui Yuan Ping Luo Zaiqing Nie
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}.