A Novel Communication-Efficient Cooperative Perception Framework Based on Infrastructure-Side Critical Feature Extraction

Cooperative perception via Vehicle-to-Everything (V2X) wireless communication technology shares complementary perception information among autonomous vehicles and infrastructure, significantly enhancing perception capabilities by addressing limitations such as restricted range and occlusions inherent in on-board sensing. However, the limited bandwidth of V2X communication poses challenges for real-time transmission of the large data volumes required for early-and mid-level cooperative perception, necessitating consideration of communication efficiency alongside perception performance. To overcome this challenge, we propose a novel communication-efficient cooperative perception framework that achieves a better trade-off between perception performance and communication cost. By exploiting the static deployment of infrastructure sensors and the heterogeneity of the perception region, the framework performs high-precision foreground segmentation on static infrastructure-side point clouds. A feature importance map is then constructed to extract sparse yet critical features for sharing, significantly reducing communication overhead. Additionally, we design a Multi-scale Weighted Attention Feature Fusion (MWAF) module to effectively integrate vehicle-and infrastructure-side features. The experimental results demonstrate that the proposed framework outperforms existing cooperative perception methods in terms of perception performance, while reducing communication traffic by approximately 60 times and 90 times on the DAIR-V2X and DZGSet dataset, respectively, without considering additional feature compression.