重庆大学学报2025，Vol.48Issue(10)：68-80,13.DOI:10.11835/j.issn.1000-582X.2025.10.007
基于ARMA车速预测的智能车交叉口强化学习决策研究
Research on reinforcement learning-based autonomous vehicle decision-making at intersections using an ARMA speed forecasting model
摘要
Abstract
To address the challenge of autonomous vehicle decision-making and control at unsignalized intersections,this study investigates the merging behavior of two vehicles at a two-way single-lane intersection.Reinforcement learning is used to establish a mapping between the vehicle state space and action space for autonomous decision-making.To overcome the limitations of overly simplified speed settings in existing studies,real-world trajectory data of surrounding vehicles are used to construct an environmental traffic model.The autoregressive moving average(ARMA)model is applied to predict the speeds of surrounding vehicles.By integrating the predicted speed profiles with the autonomous vehicle's motion parameters,a forward decision-making model is established to calculate reference speeds.These reference speeds are incorporated into the reinforcement learning reward function to accelerate training convergence.Experimental results show that the proposed model achieves rapid convergence,and the trained agent can safely navigate the intersection while interacting with surrounding vehicles exhibiting diverse driving behaviors.This work provides a reference framework for improving the safety and efficiency of autonomous vehicle decision-making at unsignalized intersections.关键词
交叉口/自动驾驶/自回归滑动平均模型/强化学习Key words
intersections/autonomous vehicles/autoregressive moving average model(ARMA)/reinforcement learning分类
交通工程引用本文复制引用
喻志成,赵俊鹏,刘永刚,夏甫根,叶明..基于ARMA车速预测的智能车交叉口强化学习决策研究[J].重庆大学学报,2025,48(10):68-80,13.基金项目
重庆市技术创新与应用发展专项重大项目(CSTB2023TIAD-STX0035) （CSTB2023TIAD-STX0035）
四川省科技计划项目(2019YFG0528).Supported by Chongqing Municipal Technological Innovation and Application Development Special Program(CSTB2023TIAD-STX0035)and Sichuan Science and Technology Funding Project(2019YFG0528). （2019YFG0528）
