Communication-Efficient and Context-Adaptive Collaborative Perception

Abstract

Collaborative perception is pivotal for the large-scale deployment of autonomous driving, yet it has long grappled with the trade-off between perception accuracy and bandwidth consumption. Existing methods fail to analyze the fine-grained characteristics of Field of View (FoV), leading to inefficient bandwidth utilization. To address this, we propose a Context-adaptive Collaborative Perception framework, termed CaCP. This method optimizes bandwidth usage by employing distinct collaboration strategies for FoV under varying contexts, thereby reducing communication overhead while maintaining perception accuracy.Additionally, CaCP introduces a novel spatial fusion of intermediate and late fusion strategies, yielding a more flexible collaborative scheme. Extensive experiments across multiple datasets encompassing both simulated (OPV2V) and real-world (V2V4Real) scenarios demonstrate that CaCP establishes a new state-of-the-art trade-off between accuracy and bandwidth. Notably, it reduces bandwidth consumption by up to 17% compared to previous works while achieving competitive or superior perception performance.