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Cooperative perception for connected and automated vehicles is traditionallyachieved through the fusion of feature maps from two or more vehicles. However,the absence of feature maps shared from other vehicles can lead to asignificant decline in object detection performance for cooperative perceptionmodels compared to standalone 3D detection models. This drawback impedes theadoption of cooperative perception as vehicle resources are often insufficientto concurrently employ two perception models. To tackle this issue, we presentSimultaneous Individual and Cooperative Perception (SiCP), a generic frameworkthat supports a wide range of the state-of-the-art standalone perceptionbackbones and enhances them with a novel Dual-Perception Network (DP-Net)designed to facilitate both individual and cooperative perception. In additionto its lightweight nature with only 0.13M parameters, DP-Net is robust andretains crucial gradient information during feature map fusion. As demonstratedin a comprehensive evaluation on the OPV2V dataset, thanks to DP-Net, SiCPsurpasses state-of-the-art cooperative perception solutions while preservingthe performance of standalone perception solutions.

SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles