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

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Názov: A Personalized Human Drivers' Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control
Autori: Jiang, Jiwan, Ding, Fan, Zhou, Yang, Wu, Jiaming, 1989, Tan, Huachun
Zdroj: IEEE Access. 8:145056-145066
Predmety: Adaptive cruise control, expensive control, driving sensitive characteristic, linear exponential-of-quadratic Gaussian, stochastic optimal control algorithm
Popis: 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.
Popis súboru: electronic
Prístupová URL adresa: https://research.chalmers.se/publication/518880
https://research.chalmers.se/publication/518880/file/518880_Fulltext.pdf
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Personalized Human Drivers' Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Jiwan%22">Jiang, Jiwan</searchLink><br /><searchLink fieldCode="AR" term="%22Ding%2C+Fan%22">Ding, Fan</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Yang%22">Zhou, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Jiaming%22">Wu, Jiaming</searchLink>, 1989<br /><searchLink fieldCode="AR" term="%22Tan%2C+Huachun%22">Tan, Huachun</searchLink>
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  Label: Source
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  Data: <i>IEEE Access</i>. 8:145056-145066
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+cruise+control%22">Adaptive cruise control</searchLink><br /><searchLink fieldCode="DE" term="%22expensive+control%22">expensive control</searchLink><br /><searchLink fieldCode="DE" term="%22driving+sensitive+characteristic%22">driving sensitive characteristic</searchLink><br /><searchLink fieldCode="DE" term="%22linear+exponential-of-quadratic+Gaussian%22">linear exponential-of-quadratic Gaussian</searchLink><br /><searchLink fieldCode="DE" term="%22stochastic+optimal+control+algorithm%22">stochastic optimal control algorithm</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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.
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        Value: 10.1109/ACCESS.2020.3015349
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      – Text: English
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      – SubjectFull: Adaptive cruise control
        Type: general
      – SubjectFull: expensive control
        Type: general
      – SubjectFull: driving sensitive characteristic
        Type: general
      – SubjectFull: linear exponential-of-quadratic Gaussian
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      – SubjectFull: stochastic optimal control algorithm
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      – TitleFull: A Personalized Human Drivers' Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control
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            NameFull: Jiang, Jiwan
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            NameFull: Ding, Fan
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            NameFull: Wu, Jiaming
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            NameFull: Tan, Huachun
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              Type: published
              Y: 2020
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