SwissCheese: Fine-Grained Channel-Spatial Feature Filtering for Communication-Efficient Cooperative Perception

Qi Xie, Xiaobo Zhou, Tianyu Hong, Tie Qiu, Wenyu Qu

Abstract: Cooperative perception is an effective way for connected autonomous vehicles (CAVs) to surpass their sensing limitations, by sharing information like intermediate features extracted from images or point clouds with each other. To reduce bandwidth consumption, feature filtering is adopted by existing methods to share only the most valuable information. However, these methods assume that the features on the same channel across all spatial regions or those in the same spatial regions across all the channels are equally important. This assumption results in coarse-grained feature filtering, which greatly decreases the cooperative perception performance. To solve this problem, this paper proposes a fine-grained channel-spatial feature filtering scheme, named SwissCheese, for communication-efficient cooperative perception. The key idea of SwissCheese is to exploit the disparity in semantic information on features between different spatial regions on different channels. Specifically, a fine-grained collaborative attention module is developed to jointly learn fine-grained attention along the channel-spatial dimensions. Moreover, a dual-dimensional feature selection strategy that selects sparse features for transmission based on the current available bandwidth is designed to achieve optimal perception performance. Experiment results show that SwissCheese significantly reduces the transmission data size by 90% with a subtle loss in perception performance.