Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information
Qifang Liu1
Dazhen Sun2
Haowen Chen2
Dongzi Li2
Ping Wang1
1.Department of Control Science and Engineering,Jilin University,Changchun 130022,Jilin,China;State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,Jilin,China2.Department of Control Science and Engineering,Jilin University,Changchun 130022,Jilin,China
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.
机标关键词：optimizationinformationstrategytransportpolicydesignbasedconsidering
论文发表日期：2025-02-04
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：17( 74-90 )
英文信息