Risk-Guided Scheduling for Spatio-Temporal Collaborative Perception in Vehicular Networks

摘要

Collaborative perception enables Intelligent Connected Vehicles (ICVs) to overcome the limitations of individual sensors–such as occlusions and restricted sensing range–by enabling the exchange of perceptual data over vehicular networks. However, high-frequency V2V communication can saturate wireless bandwidth, cause channel contention, and degrade the freshness of safety-critical information. To address the fundamental challenge of determining the optimal timing, target, and content of V2V transmissions, we propose a Spatio-Temporal Dynamic Risk–based Collaborative Perception (STDR-CP) framework. This framework integrates Iterative Closest Point (ICP) registration with an Unscented Kalman Filter (UKF) to estimate the utility of sensed measurements under dynamic motion conditions. It further constructs spatiotemporal graphs with coupled interaction fields to forecast localized collision risks among surrounding vehicles. Guided by these insights, an adaptive scheduler selectively prioritizes and compresses high-value, high-risk data for broadcast, significantly reducing redundancy while preserving critical information. Extensive V2V simulations show that STDR-CP maintains end-to-end latency around 80ms and achieves over 92% demand coverage, while mitigating channel load and improving the timeliness of shared perception. These results show that risk-aware, content-adaptive scheduling can enable scalable, safety-critical perception in bandwidth-limited vehicular networks.