| Summary: | Autonomous driving plays a crucial role in reducing traffic congestion and improving driving comfort.It remains of significant 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 reinforcement learning models based on the proximal policy optimization(PPO) algorithm,focusing on analyzing following behaviors in autonomous driving scenarios.Firstly,a large dataset of vehicle trajectories on German highways(HDD) is analyzed.The driving behaviors 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 function 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 resultsde-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 behaviors.
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