深度强化学习在无人驾驶路径规划中的应用  

Application of Deep Reinforcement Learning in Path Planning for Autonomous Driving

作　　者：陈江涛 CHEN Jiangtao(Tianjin Binhai Vocational Institute of Automotive Engineering,Tianjin,300352,China)

机构地区：[1]天津滨海汽车工程职业学院,天津300352

出　　处：《智能城市应用》2025年第4期98-100,共3页Smart City Application

摘　　要：随着人工智能技术的快速发展,深度强化学习(Deep Reinforcement Learning,DRL)在无人驾驶技术中的应用日益广泛。路径规划作为无人驾驶系统的核心技术之一,直接影响车辆行驶的安全性与效率。文中系统综述了深度强化学习在无人驾驶路径规划中的关键技术、典型算法及其应用现状,分析当前存在的挑战与不足,并探讨未来的发展方向。通过对比传统路径规划方法与基于DRL的方法,验证其在复杂动态环境下的优越性,旨在为无人驾驶技术的进一步发展提供理论依据与技术支持。With the rapid development of artificial intelligence technology,the application of Deep Reinforcement Learning(DRL)in autonomous driving technology is becoming increasingly widespread.Path planning,as one of the core technologies of autonomous driving systems,directly affects the safety and efficiency of vehicle operation.The article systematically summarizes the key technologies,typical algorithms,and application status of deep reinforcement learning in unmanned driving path planning,analyzes the current challenges and shortcomings,and explores future development directions.By comparing traditional path planning methods with DRL based methods,we aim to verify their superiority in complex dynamic environments and provide theoretical basis and technical support for the further development of autonomous driving technology.

关 键 词：深度强化学习 无人驾驶 路径规划 智能决策 自动驾驶算法 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]