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Employing Vehicle-to-Vehicle communication to enhance perception performancein self-driving technology has attracted considerable attention recently;however, the absence of a suitable open dataset for benchmarking algorithms hasmade it difficult to develop and assess cooperative perception technologies. Tothis end, we present the first large-scale open simulated dataset forVehicle-to-Vehicle perception. It contains over 70 interesting scenes, 11,464frames, and 232,913 annotated 3D vehicle bounding boxes, collected from 8 townsin CARLA and a digital town of Culver City, Los Angeles. We then construct acomprehensive benchmark with a total of 16 implemented models to evaluateseveral information fusion strategies~(i.e. early, late, and intermediatefusion) with state-of-the-art LiDAR detection algorithms. Moreover, we proposea new Attentive Intermediate Fusion pipeline to aggregate information frommultiple connected vehicles. Our experiments show that the proposed pipelinecan be easily integrated with existing 3D LiDAR detectors and achieveoutstanding performance even with large compression rates. To encourage moreresearchers to investigate Vehicle-to-Vehicle perception, we will release thedataset, benchmark methods, and all related codes inhttps://mobility-lab.seas.ucla.edu/opv2v/.

OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication