6G-Enabled Autonomous Vehicle Clusters in Expressways: A Collaborative Perception Approach

With higher peak data rates, enhanced reliability, improved energy efficiency, and reduced radio latency, 6G enables cooperative perception in autonomous vehicle clusters. Existing research mainly focuses on establishing communication-connected and structurally stable clusters, while overlooking how members collaborate in perception. To address this gap, we propose a collaborative perception-based autonomous vehicle cluster modeling method for expressways, leveraging the capabilities of 6G networks. This method facilitates collaborative perception within vehicle clusters through the exchange of sensory information among member vehicles. First, we introduce a perception interaction mechanism among vehicles as a foundation for constructing clusters. We then present a primary vehicle selection method and analyze the cluster’s perception gain, collaborative efficiency, and collaborative reliability. Based on this, we develop a vehicle cluster model and solve it using a genetic algorithm. We present a vehicle cluster formation method that brings together vehicles achieving Pareto optimal solutions through specific interaction rules, thereby forming a cluster. The simulation results demonstrate that the proposed method outperforms existing methods in terms of perception gain (PG), collaborative efficiency (CE), and collaborative reliability (CR).