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

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...

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Vydané v:IEEE access Ročník 8; s. 1
Hlavní autori: Jiang, Jiwan, Ding, Fan, Zhou, Yang, Wu, Jiaming, Tan, Huachun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Acceleration
Adaptive algorithms
Adaptive control
Adaptive cruise control
Automatic control
Car following
Control algorithms
Control systems design
Control theory
Cost function
Cruise control
Customization
Deviation
Drivers
driving sensitive characteristic
expensive control
Heterogeneity
linear exponential-of-quadratic Gaussian
Optimal control
Parameter sensitivity
stochastic optimal control algorithm
Trajectory control
Uncertainty
Vehicles
ISSN:2169-3536, 2169-3536
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3015349