A Distributed Game-Based Traffic Control Model for Unsignalized Intersections in a Connected Vehicle Environment

Abstract

In a connected vehicle environment, connected and autonomous vehicles (CAVs) can coordinate with each other to navigate intersections without the need for traffic signals. To enhance the traffic efficiency of CAVs at unsignalized intersections, this paper proposes a distributed strategy equilibrium algorithm based on game-theoretic grid coordination. First, the intersection is divided into multiple grids, with vehicles in each grid forming a game group. These groups are connected through virtual logic lines, creating a virtual logical network. Each game group locally optimizes its strategy while engaging in cross-grid games with adjacent groups, ultimately forming a distributed coordination game strategy that optimizes utility functions toward Nash equilibrium. Subsequently, a genetic algorithm is employed to search for the optimal strategy set in a multi-objective optimization problem. This study utilizes Python and SUMO for co-simulation of the game strategies, with comparative experiments set up to verify the model and algorithm’s effectiveness. The results demonstrate that, compared with the traditional First Come, First Serve algorithm and the Mixed Integer Linear Programming algorithm, the proposed algorithm significantly improves average delay time, vehicle passing speed, and energy consumption. Specifically, the proposed algorithm reduces the average delay time by 46.94%, increases vehicle passing speed by 15.5%, and lowers energy consumption by 20%, highlighting its potential to enhance traffic efficiency and reduce delays at unsignalized intersections.