V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
This dataset addresses a key bottleneck for autonomous driving by enabling research into cooperative perception to overcome occlusions and range limitations, though it is incremental as it builds on existing methods with new data.
The authors tackled the lack of real-world datasets for vehicle-to-vehicle cooperative perception by introducing V2V4Real, a large-scale multi-modal dataset covering 410 km of driving with 20K LiDAR frames and 240K annotated 3D boxes, and they provided benchmarks for three perception tasks.
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/.