Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information  

作　　者：Qifang Liu Dazhen Sun Haowen Chen Dongzi Li Ping Wang 

机构地区：[1]Department of Control Science and Engineering,Jilin University,Changchun,130022,Jilin,China [2]State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun,130022,Jilin,China

出　　处：《Control Theory and Technology》2025年第1期74-90,共17页控制理论与技术(英文版)

基　　金：supported in part by the National Natural Science Foundation of China under Grant No.62073152;in part by the Jilin Province Science and Technology Development Plan,China under Grant No.20220201034GX.

摘　　要：Eco-driving has always been an ongoing topic.In urban driving conditions,traffic regulations,other vehicle behaviors,and special driving scenarios will have a major impact on the energy consumption of autonomous vehicles.As a representative algorithm of artificial intelligence,reinforcement learning has the ability to perform well under complex tasks.This paper uses deep reinforcement learning algorithms to design the economical driving strategies of autonomous vehicles in three driving scenarios:driving at signalized intersection under free traffic flow,car-following on ramps,and driving at signalized intersection considering queue effects.In the above three driving scenarios,the driving strategy proposed in this paper achieves economical driving performance while satisfying the driving scenario requirements.

关 键 词：Eco-driving strategy Reinforcement learning Signalized intersection CAR-FOLLOWING 

分 类 号：U461[机械工程—车辆工程]