Edge-computing-based operations for automated vehicles with different cooperation classes at stop-controlled intersections

Abstract

Cooperation classes have been defined by SAE International to differentiate the communication capabilities between vehicles and infrastructure. To advance understanding of the impact of cooperation classes on autonomous cooperative driving and optimize traffic operations, this article proposes an edge-computing-based operations framework for cooperative-automated driving system (C-ADS)-equipped vehicles at a stop-controlled intersection. First, a critical time points estimation component estimates a set of critical time points for each C-ADS-equipped vehicle. Second, a trajectory-smoothing component is called at each C-ADS-equipped vehicle in a decentralized manner to control C-ADS-equipped vehicle trajectories based on the estimated critical time points and its cooperation behavior. Notably, this study represents a first-time investigation of different cooperation classes for stop-controlled intersections. Simulation results show that the proposed framework can reduce stop-and-go traffic, yielding significant improvements in mobility and energy efficiency, as the cooperation class increases. Results also demonstrate that the proposed framework is suitable for real-time applications by distributing computational burden in different entities. Further, results verify that the proposed framework can handle varying speed errors without significant loss in performance.