Game-Theory-Inspired Hierarchical Distributed Control Strategy for Cooperative Intersection Considering Priority Negotiation

Priority and mutual interaction among vehicles are crucial for the efficiency and safety control of unsignalized intersections. However, most of the approaches for intersection cooperation are based on first-come-first-served (FCFS) rules, neglecting the influence caused by different priorities. Focusing on the priority and mutual interaction between connected and automated vehicles (CAVs), this paper proposes a game-based hierarchical control strategy to improve the traveling efficiency at unsignalized intersections. The three-layered hierarchical strategy includes the priority negotiation layer (PNL), strategy bargaining layer (SBL) and strategy optimization layer (SOL), respectively. In the PNL, a game-theoretic strategy is designed for priority negotiation, in which the most efficient crossing order is sought by the Pareto-optimal set. In the SBL, with the assigned negotiated priority, a bargaining game is designed for further interaction between CAVs. In the SOL, model predictive control is applied for strategy optimization considering the comfort of drivers and executability of vehicles. Two cases of cooperative intersection, in which fixed priority with comparison of the Nash game method and greedy method, and negotiated priority with comparison of fixed priority are carried out in a hardware-in-the-loop (HiL) test. The results demonstrate that the traffic efficiency is improved with safety guaranteed by the proposed hierarchical strategy.