西华大学学报（自然科学版）2025，Vol.44Issue(4)：40-48,9.DOI:10.12198/j.issn.1673-159X.5129
基于多智能体强化学习的交叉道路车辆协同控制
Cooperative Control of Cross-road Vehicles Using Multi-agent Reinforcement Learning
摘要
Abstract
To enhance the fast response and safe passage ability of self-driving vehicles in urban con-gested intersections,we proposed a multi-agent reinforcement learning vehicle cooperative control strategy based on the MAPPO-RCNN algorithm.The MAPPO algorithm was used to achieve cooperative control between vehicles using raw,unprocessed RGB images collected by vehicle sensors as input.It outputs vehicle actions,which considering the influence of mutual positions of vehicles on the traffic flow of the passing task to optimize vehicle passing time and safety.We designed the generation algorithm for strategy and the optimization objective function.To prevent the strategy from falling into the local optimum,we used the Nash equilibrium to judge the strategy's convergence.We employed the CARLA simulation plat-form for experimental simulation.Experimental results demonstrate that the vehicle cooperative control strategy moderately enhances the traffic flow effect of self-driving vehicles at intersections and ensures the stability of the whole control system.关键词
车辆协同控制/MAPPO-RCNN算法/端到端/多智能体系统/策略生成算法/交通流优化/CARLA仿真Key words
cooperative vehicle control/MAPPO-RCNN algorithms/end-to-end/multi-intelligent body systems/policy generation algorithms/traffic flow optimization/CARLA simulation分类
交通运输引用本文复制引用
申元霞,谢悦,张学锋,汤亚玲,储岳中..基于多智能体强化学习的交叉道路车辆协同控制[J].西华大学学报（自然科学版）,2025,44(4):40-48,9.基金项目
安徽高校自然科学研究项目(2022AH050290) （2022AH050290）
特种重载机器人安徽省重点实验室项目(TZJQR007-2023). （TZJQR007-2023）
