A Personalized Human Drivers' Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control
Artikel i vetenskaplig tidskrift, 2020

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.

Adaptive cruise control

expensive control

driving sensitive characteristic

linear exponential-of-quadratic Gaussian

stochastic optimal control algorithm


Jiwan Jiang

Southeast University

University of Wisconsin Madison

Fan Ding

Southeast University

Yang Zhou

University of Wisconsin Madison

Jiaming Wu

Chalmers, Elektroteknik, System- och reglerteknik

Huachun Tan

Southeast University

IEEE Access

2169-3536 (ISSN) 21693536 (eISSN)

Vol. 8 145056-145066 9163110



Robotteknik och automation




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