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21 July 2023 End-to-end lane-changing decision-making control method for unmanned driving based on proximal optimization algorithm
Shuqing Wang, Zhiqing Huang, Chenyang Zhang
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Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170Q (2023) https://doi.org/10.1117/12.2684751
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China
Abstract
End-to-end driving behavior decision-making is a research hotspot in the field of driverless driving. This paper studies the end-to-end lane change decision control based on the PPO (Proximal Policy Optimization) deep reinforcement learning algorithm. First, an end-to-end decision control model based on PPO algorithm is established. The model uses the information perceived from the environment as the input state and outputs the control quantity (acceleration, braking, steering). Training and verification in the driving environment under the TORCS (The Open Racing Car Simulator) platform show that the model can achieve end-to-end lane change driving behavior decision-making. Finally, compared with the DDPG (Deep Deterministic Policy Gradient) model, which is also a deep reinforcement learning method, the experimental results show that the PPO lane change decision control model has a faster convergence rate.
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Shuqing Wang, Zhiqing Huang, and Chenyang Zhang "End-to-end lane-changing decision-making control method for unmanned driving based on proximal optimization algorithm", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170Q (21 July 2023); https://doi.org/10.1117/12.2684751
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KEYWORDS
Autonomous vehicles
Unmanned vehicles
Decision making
Autonomous driving
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