An end-to-end lane keeping algorithm based on the Proximal Policy Optimization algorithm
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.
Autonomous drivingReinforcement learningNear end strategy optimizationLong and short term memory network
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维普
