  Cooperative perception for connected and automated vehicles is traditionally
achieved through the fusion of feature maps from two or more vehicles. However,
the absence of feature maps shared from other vehicles can lead to a
significant decline in object detection performance for cooperative perception
models compared to standalone 3D detection models. This drawback impedes the
adoption of cooperative perception as vehicle resources are often insufficient
to concurrently employ two perception models. To tackle this issue, we present
Simultaneous Individual and Cooperative Perception (SiCP), a generic framework
that supports a wide range of the state-of-the-art standalone perception
backbones and enhances them with a novel Dual-Perception Network (DP-Net)
designed to facilitate both individual and cooperative perception. In addition
to its lightweight nature with only 0.13M parameters, DP-Net is robust and
retains crucial gradient information during feature map fusion. As demonstrated
in a comprehensive evaluation on the OPV2V dataset, thanks to DP-Net, SiCP
surpasses state-of-the-art cooperative perception solutions while preserving
the performance of standalone perception solutions.
