通信与信息技术Issue(3)：92-97,6.
基于近端策略优化算法的端到端车道保持算法研究
An end-to-end lane keeping algorithm based on the Proximal Policy Optimization algorithm
摘要
Abstract
To improve the success rate of unmanned driving and enhance the navigation ability of unmanned vehicles,this paper proposes an end-to-end lane keeping algorithm based on an improved Proximal Policy Optimization(PPO)algorithm.This article cre-ates an end-to-end unmanned driving framework by replacing a hidden layer in the PPO algorithm with an LSTM network and rede-signing a reward function.The framework can combine algorithm strategies for training with simulators.The framework takes RGB im-ages,depth images,unmanned vehicle speed,lane departure values,and collision coefficients of the camera in front of the vehicle as in-puts,and takes throttle,brake The environment variables around unmanned vehicles such as steering wheel angle are outputs.Train and test on different maps on the Airsim simulation platform,and conduct comparative experiments with the original algorithm.The ex-perimental results demonstrate that the improved LSTM-PPO algorithm can train effective autonomous driving algorithms,and the im-proved algorithm can significantly reduce training time and increase the robustness of the algorithm.关键词
自动驾驶/强化学习/近端策略优化/长短期记忆网络Key words
Autonomous driving/Reinforcement learning/Near end strategy optimization/Long and short term memory network分类
信息技术与安全科学引用本文复制引用
宋建辉,崔永阔..基于近端策略优化算法的端到端车道保持算法研究[J].通信与信息技术,2024,(3):92-97,6.基金项目
辽宁省教育厅高等学校基本科研项目(项目编号:LJKZ0275) （项目编号:LJKZ0275）
沈阳市中青年科技创新人才支持计划项目(项目编号RC210247) （项目编号RC210247）
