  Sharing collective perception messages (CPM) between vehicles is investigated
to decrease occlusions so as to improve the perception accuracy and safety of
autonomous driving. However, highly accurate data sharing and low communication
overhead is a big challenge for collective perception, especially when
real-time communication is required among connected and automated vehicles. In
this paper, we propose an efficient and effective keypoints-based deep feature
fusion framework built on the 3D object detector PV-RCNN, called Fusion PV-RCNN
(FPV-RCNN for short), for collective perception. We introduce a
high-performance bounding box proposal matching module and a keypoints
selection strategy to compress the CPM size and solve the multi-vehicle data
fusion problem. Besides, we also propose an effective localization error
correction module based on the maximum consensus principle to increase the
robustness of the data fusion. Compared to a bird's-eye view (BEV) keypoints
feature fusion, FPV-RCNN achieves improved detection accuracy by about 9% at a
high evaluation criterion (IoU 0.7) on the synthetic dataset COMAP dedicated to
collective perception. In addition, its performance is comparable to two raw
data fusion baselines that have no data loss in sharing. Moreover, our method
also significantly decreases the CPM size to less than 0.3 KB, and is thus
about 50 times smaller than the BEV feature map sharing used in previous works.
Even with further decreased CPM feature channels, i.e., from 128 to 32, the
detection performance does not show apparent drops. The code of our method is
available at https://github.com/YuanYunshuang/FPV_RCNN.
