湖南工业大学学报2024，Vol.38Issue(1)：62-69,8.DOI:10.3969/j.issn.1673-9833.2024.01.009
自动驾驶路径优化的RF-DDPG车辆控制算法研究
Research on RF-DDPG Vehicle Control Algorithm for Autonomous Driving Path Optimization
摘要
Abstract
In view of such flaws as low accuracy and poor robustness of target path tracking for autonomous vehicles in motion,a reward function-deep deterministic policy gradient(RF-DDPG)path tracking algorithm has thus been proposed.Based on the deep reinforcement learning DDPG,the algorithm designs the reward function of the DDPG algorithm for an optimization of the DDPG parameters so as to achieve the required tracking accuracy and stability.A simulation experiment has been conducted on the original DDPG algorithm and the improved RF-DDPG path tracking control algorithm based on the aopllo autonomous driving simulation platform.The results show that the proposed RF-DDPG algorithm is characterized with an adavantage over the DDPG algorithm in path tracking accuracy and robust performance.关键词
自动驾驶/路径跟踪/深度强化学习/路径控制/DDPG算法Key words
automonous driving/path tracking/deep reinforcement learning/path control/deep deterministic policy gradient(DDPG)algorithm分类
计算机与自动化引用本文复制引用
焦龙飞,谷志茹,舒小华,袁鹏,王建斌..自动驾驶路径优化的RF-DDPG车辆控制算法研究[J].湖南工业大学学报,2024,38(1):62-69,8.基金项目
国家自然科学基金区域联合基金资助重点项目(U23A20385) （U23A20385）
湖南省自然科学基金资助项目(2022JJ50005) （2022JJ50005）
湖南省研究生科研创新基金资助项目(QL20230261) （QL20230261）
湖南省教育厅科研基金资助项目(23C0182) （23C0182）
