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

Song Fu

Abstract:

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 3D 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 V2V4Real and OPV2V
datasets, thanks to DP-Net, SiCP surpasses state-of-the-art cooperative
perception solutions while preserving the performance
of standalone perception solutions.