A review on reinforcement learning-based highway autonomous vehicle control  

作　　者：Ali Irshayyid Jun Chen Guojiang Xiong 

机构地区：[1]Oakland University,USA Guizhou University,China

出　　处：《Green Energy and Intelligent Transportation》2024年第4期72-90,共19页新能源与智能载运（英文）

基　　金：SECS Faculty Startup Fund at Oakland University and in party by National Science Foundation through Award#2237317.

摘　　要：Autonomous driving is an active area of research in artificial intelligence and robotics.Recent advances in deep reinforcement learning(DRL)show promise for training autonomous vehicles to handle complex real-world driving tasks.This paper reviews recent advancement on the application of DRL to highway lane change,ramp merge,and platoon coordination.In particular,similarities,differences,limitations,and best practices regarding the DRL formulations,DRL training algorithms,simulations,and metrics are reviewed and discussed.The paper starts by reviewing different traffic scenarios that are discussed by the literature,followed by a thorough review on the DRL technology such as the state representation methods that capture interactive dynamics critical for safe and efficient merging and the reward formulations that manage key metrics like safety,efficiency,comfort,and adaptability.Insights from this review can guide future research toward realizing the potential of DRL for automated driving in complex traffic under uncertainty.

关 键 词：Autonomous vehicles Connected and automated vehicles Deep reinforcement learning Platoon 

分 类 号：U49[交通运输工程—交通运输规划与管理]