Model Predictive Adaptive Cruise Control of Intelligent Electric Vehicles Based on Deep Reinforcement Learning Algorithm FWOR Driver Characteristics

• Published in: International journal of automotive technology Vol. 24; no. 4; pp. 1175 - 1187
• Main Authors: Guo, Jinghua, Li, WenChang, Luo, Yugong, Li, Keqiang
• Format: Journal Article
• Language: English
• Published: Seoul The Korean Society of Automotive Engineers 01.08.2023; Springer Nature B.V; 한국자동차공학회
• Subjects: Acceleration; Adaptive control; Algorithms; Automotive Engineering; Car following; Control systems design; Correlation coefficients; Cruise control; Deep learning; Driver behavior; Electric vehicles; Engineering; Headways; Machine learning; Optimization; Predictive control; 자동차공학; Deep reinforcement learning algorithm; Intelligent electric vehicles; Adaptive cruise control; Naturalistic driving data; Driver model
• ISSN: 1229-9138; 1976-3832
• DOI: 10.1007/s12239-023-0096-4

This paper presents a novel model predictive adaptive cruise control strategy of intelligent electric vehicles based on deep reinforcement learning algorithm for driver characteristics. Firstly, the influence mechanism of factors such as inter-vehicle distance, relative speed and time headway (THW) on the driver’s behavior in the process of car following is analyzed by the correlation coefficient method. Then, the driver behavior in the process of car following is learned from the natural driving data, the car following model is established by the deep deterministic policy gradient (DDPG) algorithm, and the output acceleration of the DDPG model is used as the reference trajectory of the ego vehicle’s acceleration. Next, in order to reflect the driver behavior and achieve multi performance objective optimization of adaptive cruise control of intelligent electric vehicles, the model predictive controller (MPC) is designed and used for tracking the desired acceleration produced by the car following DDPG model. Finally, the performance of the proposed adaptive cruise control strategy is evaluated by the experimental tests, and the results demonstrate the effectiveness of proposed control strategy.