Personalized Adaptive Cruise Control with Deep Reinforcement Learning

Abstract: With the increasing of vehicle intelligence, how to integrate driving style into the autonomous driving decision-making strategies and enhance the driver's trust in the autonomous driving system has become a hot topic. In this paper, a new personalized adaptive cruise control algorithm taking the consideration of driver car-following style is designed. By filtering and reconstructing the driving data in the NGSIM database, indicators characterizing the car-following style are extracted, and K-means is used to cluster the car-following style into three categories: aggressive, general and conservative. A classification identification model is established to realize the online identification of the car-following style. The adaptive cruise controller is designed based on the Dueling Double Deep Q-Network algorithm, and driver car-following style is integrated into the reward function. Corresponding weight coefficients are set according to different working conditions, and the fuzzy rule is used to adjust the weight coefficients of the reward function in real time. The simulation platform is built based on Carsim and Matlab/Simulink to verify the performance of the proposed algorithm. The simulation results showed that the personalized adaptive cruise control algorithm can achieve accurate identification of the driver's car-following style and achieve stable control that incorporates the driver's car-following style. The research can provide reference for the subsequent implementation of more advanced personalized autonomous driving functions.

Keywords: Personalized Adaptive Cruise Control, Car, following Style, Style Classification, Deep Reinforcement Learning

DOI: 10.54941/ahfe1003421

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