Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

Chaoyue Zu; Chao Yang; Jian Wang; Wenbin Gao; Dongpu Cao; Fei-Yue Wang

doi:10.1109/JAS.2020.1003246

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): 1045-1063

Chaoyue Zu, Chao Yang, Jian Wang, Wenbin Gao, Dongpu Cao and Fei-Yue Wang, "Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045-1063, July 2020. doi: 10.1109/JAS.2020.1003246

Citation:

Chaoyue Zu, Chao Yang, Jian Wang, Wenbin Gao, Dongpu Cao and Fei-Yue Wang, "Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045-1063, July 2020. doi: 10.1109/JAS.2020.1003246

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Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

doi: 10.1109/JAS.2020.1003246

Chaoyue Zu^1
,,
Chao Yang^2
,,
Jian Wang^{3, 4
,},
Wenbin Gao^5
,,
Dongpu Cao^6
,,
Fei-Yue Wang^7
,

1.
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2.
Jiangsu XCMG Construction Machinery Research Institute Ltd., Xuzhou 221004, China
3.
College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012
4.
Chongqing Xibu Automobile Proving Ground Management Co. Ltd., Chongqing 404100, China
5.
State Grid JIBEI Electric Power Company Limited Management Training Center, China
6.
Advanced Vehicle Engineering Center, Cranfield University, Cranfield MK43 0AL, UK
7.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Funds:

the National Natural Science Foundation of China 61572229

the National Natural Science Foundation of China 6171101066

the Key Scientific and Technological Projects for Jilin Province Development Plan 20170204074GX

the Key Scientific and Technological Projects for Jilin Province Development Plan 20180201068GX

Jilin Provincial International Cooperation Foundation 20180414015GH

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Author Bio:

Chaoyue Zu received the B.E. degree in computer science and technology from Jilin University, in 2016. He is now seeking his master degree at the College of Computer Science and Technology, Jilin University. His research interests include the path planning of intelligent vehicle and vehicular networks.e-mail: zucy0316@gmail.com

Chao Yang received the B.E. degree from Shandong University of Technology, China, in 2006. He received the M.Sc. degree from University of Science and Technology, in 2012. He is currently employed in Jiangsu XCMG Construction Machinery Research Institute Ltd., China. His main research interest is vehicle engineering.e-mail: yangc@xcmg.com

Jian Wang received the B.Sc, M.Sc, and Ph.D. degrees in computer science and technology from jilin University in 2004, 2007, and 2011, respectively. He is interested in topics related to vehicular networks, especially for network security and privacy protection. He has published over 20 articles on international journals. Currently, he is a Professor at Jilin University

Wenbin Gao received the B.Sc and M.Sc degrees in computer science and technology from Jilin University in 2008 and 2011, respectively. His research interests include computer network and security. He is IT Supervisor of Science and Technology Information Department, Management Training Center, State Grid JIBEI Electric Power Company Limited, China.e-mail: jlgaowenbin@163.com

Dongpu Cao (M'13) received the Ph. D. degree from Concordia University, Canada, in 2008. He is currently a Senior Lecturer at the Advanced Vehicle Engineering Center, Cranfield University, UK. His research focuses on vehicle dynamics/control and intelligence, automated driving and parallel driving, where he has contributed more than 120 publications and 1 US patent. He received the ASME AVTT 2010 Best Paper Award and 2012 SAE Arch T. Colwell Merit Award. Dr. Cao serves as an Associate Editor for IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology and IEEE Transactions on Industrial Electronics. He has been a Guest Editor for Vehicle System Dynamics, IEEE/ASME Transactions on Mechatronics and IEEE Transactions on Human-Machine Systems. He has been serving on the SAE International Vehicle Dynamics Standards Committee and a few ASME, SAE, IEEE technical committees.e-mail: d.cao@cranfield.ac.uk

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 Laboratory (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, and in 2002, was appointed as the Director of the Key Laboratory 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 of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of IEEE Transactions on Computational Social Systems, Founding EiC of IEEE/CAA Journal of Automatica Sinica, and Chinese Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, and ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he has been the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang has been elected as Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China and was awarded the Outstanding Scientist by ACM for his research contributions in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009, 2011 and 2015, and IEEE SMC Norbert Wiener Award in 2014.e-mail: feiyue@ieee.org
Corresponding author:
Jian Wang, e-mail: wangjian591@jlu.edu.cn
Recommended by Associate Editor Xiaoou Li.
Received Date: 2018-05-07
Revised Date: 2018-07-02

Abstract

Abstract

A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance (MVCA) algorithm is proposed by extending the reciprocal ${ n}$-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently, without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore, MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay (${ < 100}$ ms) and low packet loss (${ < 5\%}$) can bring little influence to those trajectory planning algorithms that only depend on V2V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.
- Collision avoidance,
- intelligent vehicles,
- inter-vehicle communication,
- simulation,
- testing,
- trajectory planning

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Recommended by Associate Editor Xiaoou Li.

References(29)

References

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A distributed real-time multiple vehicles collision avoidance (MVCA) algorithm is proposed by extending the reciprocal n-body collision avoidance method and enables the intelligent vehicles to choose their destinations and control inputs independently. MVCA behaves safely and reliably by the simulation.
The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain and the results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent.
The MVCA was tested by a real intelligent vehicle, the information on obstacles and the latitude and longitude of the vehicle were input into the algorithm, MVCA outputted the control quantity, and finally made the vehicle arrive at the destination. The test results prove the operability of MVCA.

Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

doi: 10.1109/JAS.2020.1003246

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