  Autonomous driving faces great safety challenges for a lack of global
perspective and the limitation of long-range perception capabilities. It has
been widely agreed that vehicle-infrastructure cooperation is required to
achieve Level 5 autonomy. However, there is still NO dataset from real
scenarios available for computer vision researchers to work on
vehicle-infrastructure cooperation-related problems. To accelerate computer
vision research and innovation for Vehicle-Infrastructure Cooperative
Autonomous Driving (VICAD), we release DAIR-V2X Dataset, which is the first
large-scale, multi-modality, multi-view dataset from real scenarios for VICAD.
DAIR-V2X comprises 71254 LiDAR frames and 71254 Camera frames, and all frames
are captured from real scenes with 3D annotations. The Vehicle-Infrastructure
Cooperative 3D Object Detection problem (VIC3D) is introduced, formulating the
problem of collaboratively locating and identifying 3D objects using sensory
inputs from both vehicle and infrastructure. In addition to solving traditional
3D object detection problems, the solution of VIC3D needs to consider the
temporal asynchrony problem between vehicle and infrastructure sensors and the
data transmission cost between them. Furthermore, we propose Time Compensation
Late Fusion (TCLF), a late fusion framework for the VIC3D task as a benchmark
based on DAIR-V2X. Find data, code, and more up-to-date information at
https://thudair.baai.ac.cn/index and https://github.com/AIR-THU/DAIR-V2X.
