  Modern perception systems of autonomous vehicles are known to be sensitive to
occlusions and lack the capability of long perceiving range. It has been one of
the key bottlenecks that prevents Level 5 autonomy. Recent research has
demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception system
has great potential to revolutionize the autonomous driving industry. However,
the lack of a real-world dataset hinders the progress of this field. To
facilitate the development of cooperative perception, we present V2V4Real, the
first large-scale real-world multi-modal dataset for V2V perception. The data
is collected by two vehicles equipped with multi-modal sensors driving together
through diverse scenarios. Our V2V4Real dataset covers a driving area of 410
km, comprising 20K LiDAR frames, 40K RGB frames, 240K annotated 3D bounding
boxes for 5 classes, and HDMaps that cover all the driving routes. V2V4Real
introduces three perception tasks, including cooperative 3D object detection,
cooperative 3D object tracking, and Sim2Real domain adaptation for cooperative
perception. We provide comprehensive benchmarks of recent cooperative
perception algorithms on three tasks. The V2V4Real dataset can be found at
https://research.seas.ucla.edu/mobility-lab/v2v4real/.
