V2X-DSI: A Density-Sensitive Infrastructure LiDAR Benchmark for Economic Vehicle-to-Everything Cooperative Perception

Recent research has demonstrated that the Vehicle-to-Everything (V2X) communication techniques can fundamentally improve the perception system for autonomous driving by collaborating between vehicle and infrastructure sensors. LiDAR is the commonly-used sensor for V2X autonomous driving due to its robustness in challenging scenarios. However, the LiDAR sensor is expensive, so the cost of equipping LiDAR sensors to a large number of infrastructures on the large-scale roadway network is extremely high, which has limited the wide deployment of the V2X cooperative perception system. How to discover an economic V2X cooperative perception system is never been well studied before. Inspired by the cost difference of the various point cloud densities of LiDAR, we propose the first Density-Sensitive Infrastructure LiDAR benchmark for economic V2X cooperative perception, named V2X-DSI, in this paper. Using the proposed V2X-DSI benchmark, we analyze the effect of cooperative perception performance under different beam infrastructure LiDAR. We specifically assess three state-of-the-art methods, i.e., OPV2V, V2X-ViT, and CoBEVT, using our V2X-DSI dataset. The results indicate that varying beam infrastructure LiDAR sensors play a crucial role in influencing cooperative perception performance.