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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9163110/ |
| _version_ | 1819276420316135424 |
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| author | Jiwan Jiang Fan Ding Yang Zhou Jiaming Wu Huachun Tan |
| author_facet | Jiwan Jiang Fan Ding Yang Zhou Jiaming Wu Huachun Tan |
| author_sort | Jiwan Jiang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T23:39:56Z |
| format | Article |
| id | doaj.art-be080a9a0206449db8d94c969b85762a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:39:56Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-be080a9a0206449db8d94c969b85762a2022-12-21T17:25:43ZengIEEEIEEE Access2169-35362020-01-01814505614506610.1109/ACCESS.2020.30153499163110A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise ControlJiwan Jiang0https://orcid.org/0000-0003-4414-1959Fan Ding1https://orcid.org/0000-0001-5482-8290Yang Zhou2https://orcid.org/0000-0001-5366-5389Jiaming Wu3Huachun Tan4https://orcid.org/0000-0001-6881-0550School of Transportation, Southeast University, Nanjing, ChinaSchool of Transportation, Southeast University, Nanjing, ChinaDepartment of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, USADepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SwedenSchool of Transportation, Southeast University, Nanjing, ChinaThis 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.https://ieeexplore.ieee.org/document/9163110/Adaptive cruise controldriving sensitive characteristicexpensive controllinear exponential-of-quadratic Gaussianstochastic optimal control algorithm |
| spellingShingle | Jiwan Jiang Fan Ding Yang Zhou Jiaming Wu Huachun Tan A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control IEEE Access Adaptive cruise control driving sensitive characteristic expensive control linear exponential-of-quadratic Gaussian stochastic optimal control algorithm |
| title | A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control |
| title_full | A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control |
| title_fullStr | A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control |
| title_full_unstemmed | A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control |
| title_short | A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control |
| title_sort | personalized human drivers x2019 risk sensitive characteristics depicting stochastic optimal control algorithm for adaptive cruise control |
| topic | Adaptive cruise control driving sensitive characteristic expensive control linear exponential-of-quadratic Gaussian stochastic optimal control algorithm |
| url | https://ieeexplore.ieee.org/document/9163110/ |
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