交通信息与安全2024，Vol.42Issue(1)：84-93,10.DOI:10.3963/j.jssn.1674-4861.2024.01.010
基于强化学习的交叉口智能网联车多目标通行控制方法
A Multi-objective Traffic Control Method for Connected and Automated Vehicle at Signalized Intersection Based on Reinforcement Learning
摘要
Abstract
To address the issue of high energy consumption and low efficiency of connected and autonomous vehi-cles(CAV)in dynamic traffic environments under traditional control methods,a reinforcement learning-based con-trol approach for CAV is proposed,aiming at reducing energy consumption,improving travel efficiency,and enhanc-ing driving comfort.By considering the interactions between CAV and traffic signal control systems,as well as physical environmental factors,we collect signal phase and timing(SPaT),preceding vehicle speed and position,and other information to establish the state space of the reinforcement learning framework.Furthermore,an energy consumption model is established with the limit of battery energy recovery,and a multi-objective weighted reward function is designed based on key performance indicators such as energy consumption per unit time,travel distance,and acceleration change rate.The optimal weights for each performance indicator are determined using the analytic hierarchy process,and the model is trained using a deep deterministic policy gradient algorithm,with the algorithm parameters optimized through gradient descent.Simulation experiments were carried out using the SUMO platform the results demonstrate that the proposed algorithm achieves the most balanced travel performance,with a 9.22%re-duction in energy consumption and an 18.77%reduction in change rate of acceleration compared to the DQN algo-rithm,as well as an 8.39%reduction in travel time compared to the Krauss car-following model.In conclusion,the results validate the feasibility and effectiveness of the proposed CAV control approach in reducing energy consump-tion,improving travel efficiency,and enhancing driving comfort.关键词
交通工程/智能网联车辆/车辆控制/强化学习/信号交叉口Key words
traffic engineering/connected autonomous vehicles/vehicle control/reinforcement learning/signalized intersection分类
交通工程引用本文复制引用
姜涵,张健,张海燕,郝威,马昌喜..基于强化学习的交叉口智能网联车多目标通行控制方法[J].交通信息与安全,2024,42(1):84-93,10.基金项目
国家重点研发计划项目(2021YFB1600504)资助 （2021YFB1600504）
