A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

IEEE Access

Yang Zhou¹  Huachun Tan²  Jiaming Wu³  Jiwan Jiang⁴  Fan Ding⁴ 

[1] Department of Civil and Environmental Engineering, University of Wisconsin&x2013;Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden;Madison, Madison, WI, USA;School of Transportation, Southeast University, Nanjing, China;

关键词: Adaptive cruise control;    driving sensitive characteristic;    expensive control;    linear exponential-of-quadratic Gaussian;    stochastic optimal control algorithm;   

DOI  :  10.1109/ACCESS.2020.3015349

来源: DOAJ

【 摘 要 】

This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers' risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from the design car-following target. With the risk-sensitive parameter embedded in LEQG, the proposed method has the capability to characterize risk preference heterogeneity of each AV against uncertainties according to each human drivers' preference. Further, the established control theory can achieve both expensive control mode and non-expensive control mode via changing the weighting matrix of the cost function in LEQG to reveal different treatments on input. Simulation tests validate the proposed approach can characterize different driving behaviors and its effectiveness in terms of reducing the deviation from equilibrium state. The ability to produce different trajectories and generate smooth control of the proposed algorithm is also verified.

【 授权许可】

Unknown