Study on Following Car Model with Different Driving Styles Based on Proximal Policy Optimization Algorithm
Autonomous driving plays a crucial role in reducing traffic congestion and improving driving comfort.It remains of sig-nificant research importance to enhance public acceptance of autonomous driving technology.Customizing different driving styles for diverse user needs can aid drivers in understanding autonomous driving behavior,enhancing the overall driving experience,and reducing psychological resistance to using autonomous driving systems.This study proposes a design approach for deep reinforce-ment learning models based on the proximal policy optimization(PPO)algorithm,focusing on analyzing following behaviors in au-tonomous driving scenarios.Firstly,a large dataset of vehicle trajectories on German highways(HDD)is analyzed.The driving be-haviors are classified based on features such as time headway(THW),distance headway(DHW),vehicle acceleration,and follo-wing speed.Characteristic data for aggressive and conservative driving styles are extracted.On this basis,an encoded reward func-tion reflecting driver styles is developed.Through iterative learning,different driving style deep reinforcement learning models are generated using the PPO algorithm.Simulations are conducted on the highway environment platform.Experimental results de-monstrate that the PPO-based driving models with different styles possess the capability to achieve task objectives.Moreover,when compared to traditional intelligent driver model(IDM),these models accurately reflect distinct driving styles in driving be-haviors.
万方数据
维普
