Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy

Jing Huang; Yimin Chen; Xiaoyan Peng; Lin Hu; Dongpu Cao

doi:10.1109/JAS.2020.1003261

Volume 7 Issue 4

Jun. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(4): 1107-1115

Jing Huang, Yimin Chen, Xiaoyan Peng, Lin Hu and Dongpu Cao, "Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1107-1115, July 2020. doi: 10.1109/JAS.2020.1003261

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Jing Huang, Yimin Chen, Xiaoyan Peng, Lin Hu and Dongpu Cao, "Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1107-1115, July 2020. doi: 10.1109/JAS.2020.1003261

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Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy

doi: 10.1109/JAS.2020.1003261

Funds: This work was supported by the National Natural Science Foundation of China (51775178, 51875049), and Hunan Province Natural Science Outstanding Youth Fund (2019JJ20017)

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Author Bio:
Jing Huang received the B.S. degree in mechanical manufacture and automation, the M.S. degree in mechatronics engineering, and the Ph.D. degree in mechanical engineering from Hunan University, in 2003, 2006, and 2009, respectively. She is currently an Associate Professor at the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University. Her research interests include vehicle and traffic safety and intelligent vehicle

Yimin Chen is currently a Postdoctoral Fellow at the Department of Mechanical and Mechatronics Engineering, University of Waterloo, Canada. He received the B.S. degree in mechanical design from Central South University in 2012, and the M.S. degree in mechanical engineering from Xi’an Jiaotong University in 2015. He obtained the Ph.D. degree in the Walker Department of Mechanical Engineering at the University of Texas at Austin, USA, in 2019. His research interests include automated vehicles, vehicle dynamics and control, and personalized vehicle control systems

Xiaoyan Peng received the B.S. degree in mechanical engineering, the M.S. degree in mechatronic engineering, and the Ph.D. degree in control theory and control engineering from Hunan University, in 1986, 1989, and 2013, respectively. Since 1989, she has been engaged in the teaching and research of electromechanical control at the College of Mechanical and Vehicle Engineering, Hunan University. She hosted and participated in many national and provincial research projects, funded by The Natural Science Foundation of China, Natural Science Foundation of Hunan Province, Hunan Province 2011 Green Car Collaborative Innovation Center open fund, and other fundings. Her research interests include the technology of brake by wire in electric vehicle, intelligent vehicle routing and autonomous decision-making methods, complex industrial process modeling and optimal control, etc. She is currently a Professor with the College of Mechanical & Vehicle Engineering, Hunan University

Lin Hu (M’19) received the Ph.D. degree in mechanical engineering from Hunan University in 2008. He is currently a Professor with the School of Automotive and Mechanical Engineering, Changsha University of Science and Technology. He has authored more than 80 articles, his research interests include intelligent vehicles, vehicle dynamics, vehicles and traffic safety. Dr. Hu is the Winner of Hunan Outstanding Youth Fund in 2019. He won the second prize for scientific and technological progress in Hunan Province in February 2017 and February 2019.He serves on the Council of China Society of Automotive Engineering, a Member of Editorial Board for the Highway and Automobile Transportation

Dongpu Cao received the Ph.D. degree in mechanical engineering from Concordia University, Canada, in 2008. He is currently an Associate Professor of vehicular electronics and systems with the University of Waterloo, Canada. He has authored more than 150 publications, one book, and one U.S. patent in area of his research interests, which include vehicle dynamics, control and intelligence. Dr. Cao serves on the Society of Automotive Engineers (SAE) International Vehicle Dynamics Standards Committee and a few American Society of Mechanical Engineers (ASME), SAE, IEEE technical committees. He was the Recipient of the ASME AVTT Best Paper Award in 2010 and SAE Arch T. Colwell Merit Award in 2012. He serves as an Associate Editor for the IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, and ASME Journal of Dynamic Systems, Measurement, and Control
Corresponding author: L. Hu is with the School of Automotive and Mechanical Engineering, and Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China (e-mail: hulin@csust.edu.cn)
Received Date: 2019-12-25
Revised Date: 2020-03-04
Accepted Date: 2020-04-02

Available Online: 2020-04-23

Abstract

Abstract

This paper presents a fusion control strategy of adaptive cruise control (ACC) and collision avoidance (CA), which takes into account a driver’s behavioral style. First, a questionnaire survey was performed to identify driver type, and the corresponding driving behavioral data were collected via driving simulator experiments, which served as the template data for the online identification of driver type. Then, the driver-adaptive ACC/CA fusion control strategy was designed, and its effect was verified by virtual experiments. The results indicate that the proposed control strategy could achieve the fusion control of ACC and CA successfully and improve driver adaptability and comfort.
- Adaptive cruise control,
- collision avoidance,
- driving simulator experiment,
- driving style,
- fusion control

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References

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Highlights

A driver-adaptive fusion control strategy of Adaptive Cruise Control and Collision Avoidance was proposed.
Different styles of divers’ driving behavioural data were collected via driving simulator experiments, corresponding driving behaviour characteristics were extracted and used in the driver-adaptive control.
Real-time recognition of driving style was achieved based on fuzzy reasoning rule.
The effect of the fusion control strategy was validated by virtual experiments.

Study on the Driving Style Adaptive Vehicle Longitudinal Control Strategy

doi: 10.1109/JAS.2020.1003261

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通讯作者: 陈斌, bchen63@163.com

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