Efficient and Robust Collaborative Perception via Cross-Vehicle Spatio-Temporal Feature Selecting

Jingyu Zhang, Yilei Wang, Kun Yang, Hanqi Wang, Qiang Fu, Zhiyong Chen, Peng Sun, Liang Song

Abstract: Collaborative perception systems enhance the perception capabilities of individual vehicles by facilitating information exchange between neighbouring vehicles. This approach effectively addresses challenges like occlusions and long-range perceptions that single vehicle cannot manage alone. However, practical applications often face difficulties due to constraints in wireless communication resources and reliability, which limit the effectiveness of latency-sensitive collaborative perception. To overcome these barriers, we introduce CERCP, a Communication Efficient and Robust Collaborative Perception framework. CERCP comprises two core modules: a cross-vehicle spatio-temporal feature selection module, which minimizes communication by transmitting only essential sensor regions with spatio-temporal complementarity, and a global-aware feature synchronization module, which mitigates data delays due to communication latency. To our knowledge, CERCP is the first general collaborative perception framework designed for efficient communication and is applicable across various tasks and modalities. We comprehensively evaluate CERCP on three datasets from real-world and simulated scenarios, using two sensor modalities (LiDAR and camera) and two perception tasks (3D object detection and BEV semantic segmentation). Extensive experiments demonstrate the superior performance of our method.

External IDs:doi:10.1109/tits.2025.3611530