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