Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation  

作　　者：Xiaolong Chen Biao Xu Manjiang Hu Yougang Bian Yang Li Xin Xu 

机构地区：[1]State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,the College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China [2]State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,the College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082 [3]Wuxi Intelligent Control Research Institute(WICRI)of Hunan University,Wuxi 214115,China [4]College of Intelligence Science and Technology,Institute of Unmanned Systems,National University of Defense Technology,Changsha 410073,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第9期2011-2026,共16页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (52102394,52172384);Hunan Provincial Natural Science Foundation of China (2023JJ10008);Young Elite Scientists Sponsorship Program by CAST (2022QNRC001)。

摘　　要：Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently.This paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and efficiently.The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections.The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem.The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency.Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions.The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

关 键 词：Autonomous driving DECISION-MAKING reinforcement learning(RL) unsignalized intersection 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U463.6[自动化与计算机技术—控制科学与工程]