Guidance Control for Parallel Parking Tasks

Jiyuan Tan; Chunling Xu; Li Li; Fei-Yue Wang; Dongpu Cao; Lingxi Li

doi:10.1109/JAS.2019.1911855

Volume 7 Issue 1

Jan. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(1): 301-306

Jiyuan Tan, Chunling Xu, Li Li, Fei-Yue Wang, Dongpu Cao and Lingxi Li, "Guidance Control for Parallel Parking Tasks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 301-306, Jan. 2020. doi: 10.1109/JAS.2019.1911855

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Jiyuan Tan, Chunling Xu, Li Li, Fei-Yue Wang, Dongpu Cao and Lingxi Li, "Guidance Control for Parallel Parking Tasks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 301-306, Jan. 2020. doi: 10.1109/JAS.2019.1911855

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Guidance Control for Parallel Parking Tasks

doi: 10.1109/JAS.2019.1911855

Funds: This work was supported in part by the National Key Research and Development Program of China (2018AAA0101400), the National Natural Science Foundation of China (61603005, 61790565), and the Joint Laboratory for Future Transport and Urban Computing of Amap

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Author Bio:
Jiyuan Tan (M’14) received the B.S. degree in automation from Northeastern University, Shenyang, China, in 2009 and the Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 2014. From 2014 to 2017, he was an Assistant Professor of North China University of Technology. Since 2018, he was an Associate Professor of North China University of Technology. His research interests incude traffic control, traffic big data, and driving safety

Chunling Xu received the B.S. degree in traffic engineering from Shandong JiaoTong University, Jinan, China, in 2015 and the M.S. degree in control engineering from North China University of Technology, Beijing, China, in 2018. Her research interests incude automatic parking methods, driving safety and driver’s psychology. From July, 2018, she is an Assistant Engineer of Beijing Transport Institute. Her main work interests include travel characteristics of residents based on mobile signaling data, travel characteristics of shared bikes

Li Li (S’05–M’06–SM’10–F’17) is currently an Associate professor with the Department of Automation, Tsinghua University, China. His research interests include complex and networked systems, intelligent control and sensing, intelligent transportation systems and intelligent vehicles. Dr. Li had published over 50 SCI indexed international journal papers and over 50 international conference papers as a first/corresponding author. He serves as an Associate Editor for IEEE Transactions on Intelligent Transportation Systems

Fei-Yue Wang (S’87–M’89–SM’94–F’03) received the Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Overseas Chinese Talents Program from the State Planning Council and “100 Talent Program” from CAS, and in 2002, was appointed as the Director of the Key Lab of Complex Systems and Intelligence Science, CAS. From 2006 to 2010, he was Vice President for Research, Education, and Academic Exchanges at the Institute of Automation, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory for Management and Control of Complex Systems. His current research focuses on methods and applications for parallel systems, social computing, parallel intelligence and knowledge automation

Dongpu Cao (M’08) received the Ph.D. degree from Concordia University, Canada, in 2008. He is currently an Associate Professor at University of Waterloo, Canada. He received the ASME AVTT2010 Best Paper Award and 2012 SAE Arch T. Colwell Merit Award. His research focuses on vehicle control and intelligence, automated driving and parallel driving, where he has contributed more than 130 publications and 1 US patent

Lingxi Li (S’06–M’08–SM’14) received the B.E. degree in automation from Tsinghua University, Beijing, China, in 2000; the M.S. degree in control theory and control engineering from Chinese Academy of Sciences, Beijing, in 2003; and the Ph.D. degree in electrical and computer engineering from University of Illinois at Urbana-Champaign, IL, USA, in 2008. Since August 2008, he has been with Indiana University-Purdue University Indianapolis, IN, USA, where he is currently an Associate Professor of electrical and computer engineering. His research interests include modeling, analysis, control, and optimization of complex systems, intelligent transportation systems and intelligent vehicles, discrete event systems, active safety systems, and human factors
Corresponding author: L. Li is with the Department of Automation, BNRist, Tsinghua University, Beijing 100084, China (e-mail: li-li@tsinghua.edu)
Received Date: 2019-08-26
Accepted Date: 2019-10-17

Available Online: 2019-11-13

Abstract

Abstract

Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies (called parking trajectory planning) designs a detailed reference trajectory that links the start and ending points of a special parking task and let the vehicle track this reference trajectory so as to park into the berth. The second kind of strategies (called guidance control) just characterizes several regimes of driving actions as well as the important switching points in certain rule style and let the vehicle follows the pre-selected series of actions so as to park into the berth. Parking guidance control is simpler than parking trajectory planning. However, no studies thoroughly validated parking guidance control before. In this paper, a new automatic parking method is presented, which could characterize the desired control actions directly. Then the feasibility is examined carefully. Tests show that a simple parking guidance control strategy can work in most parallel parking tasks, if the available parking berth is not too small. This finding helps to build more concise automatic parking systems that can efficiently guide human drivers.
- Automatic parking,
- guidance control,
- trajectory planning

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References

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Guidance Control for Parallel Parking Tasks

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