Parallel Distance: A New Paradigm of Measurement for Parallel Driving

Teng Liu; Hong Wang; Bin Tian; Yunfeng Ai; Long Chen

doi:10.1109/JAS.2019.1911633

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): 1169-1178

Teng Liu, Hong Wang, Bin Tian, Yunfeng Ai and Long Chen, "Parallel Distance: A New Paradigm of Measurement for Parallel Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1169-1178, July 2020. doi: 10.1109/JAS.2019.1911633

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Teng Liu, Hong Wang, Bin Tian, Yunfeng Ai and Long Chen, "Parallel Distance: A New Paradigm of Measurement for Parallel Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1169-1178, July 2020. doi: 10.1109/JAS.2019.1911633

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Parallel Distance: A New Paradigm of Measurement for Parallel Driving

doi: 10.1109/JAS.2019.1911633

Funds: The work was supported in part by the National Natural Science Foundation of China (61533019, 91720000), Beijing Municipal Science and Technology Commission (Z181100008918007), and the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV)

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Author Bio:
Teng Liu (M’18) received the B.S. degree in mathematics from Beijing Institute of Technology, 2011. He received the Ph.D. degree in automotive engineering from Beijing Institute of Technology (BIT), in 2017. His Ph.D. dissertation, under the supervision of Pro. Fengchun Sun, was entitled “Reinforcement learning-based energy management for hybrid electric vehicles”. He worked as a research fellow at the Vehicle Intelligence Pioneers Ltd. for one year. Now, he is a member of IEEE VTS, IEEE ITS, IEEE IES, IEEE TEC.Dr. Liu is now a Postdoctoral Fellow in the Department of Mechanical and Mechatronics Engineering, University of Waterloo, Canada. Dr. Liu has more than 8 years’ research and working experience in renewable vehicle and connected autonomous vehicle. His current research focuses on reinforcement learning (RL)-based energy management in hybrid electric vehicles, RL-based decision making for autonomous vehicles, and CPSS-based parallel driving. He has published over 30 SCI papers and 10 conference papers in these areas. He received the Merit Student of Beijing in 2011, the Teli Xu Scholarship (Highest Honor) of Beijing Institute of Technology in 2015, “Top 10” in 2018 IEEE VTS Motor Vehicle Challenge and sole outstanding winner in 2018 ABB Intelligent Technology Competition

Hong Wang is currently a Research Associate of Mechanical and Mechatronics Engineering with the University of Waterloo. She received the Ph.D. degree at Beijing Institute of Technology in China in 2015. Her research focuses on the path planning control and ethical decision making for autonomous vehicles and component sizing, modeling of hybrid powertrains and power management control strategies design for hybrid electric vehicles, intelligent control theory and application

Bin Tian received the B.S. degree from Shandong University, in 2009 and the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, in 2014. He is currently an Associate Professor of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His current research interests include computer vision, machine learning, and automated driving

Yunfeng Ai received the B.S. degree from Shandong University, in 2001 and the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, in 2006. He was a visiting scholar from December 2015 to October 2016 and postdoctoral researcher from November 2016 to April 2017 at Carnegie Mellon University. He is currently a research scientist of University of Chinese Academy of Sciences. His current research interests include computer vision, machine learning, parallel robots, and automated driving

Long Chen received the B.Sc. degree in communication engineering and the Ph.D. degree in signal and information processing from Wuhan University, in 2007 and in 2013, respectively. From October 2010 to November 2012, he was co-trained Ph.D. Student at National University of Singapore. He is currently an Associate Professor with the School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. His research interests include autonomous driving, robotics, artificial intelligence where he has contributed more than 50 publications. He serves as an Associate Editor for IEEE Technical Committee on Cyber-Physical Systems newsletter
Corresponding author: B. Tian is with the Vehicle Intelligence Pioneers Inc., Qingdao 266109, and also with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China (e-mail: bin.tian@ia.ac.cn)
Received Date: 2018-07-05
Revised Date: 2018-10-10
Accepted Date: 2018-12-12

Available Online: 2020-02-19

Abstract

Abstract

In this paper, a new paradigm named parallel dis-tance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then, Markov chain (MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.
- Artificial and physical system,
- parallel distance,
- para-llel driving 3.0,
- parallel system,
- rotational and accelerator signal

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References

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Parallel driving 3.0 system as potential autonomous driving system is essentially discussed.
Parallel distance framework is presented to measure real and artificial world.
Techniques related to multiple distance calculation are quantified and compared.
Practical applications of parallel distance framework is introduced and outlined.

Parallel Distance: A New Paradigm of Measurement for Parallel Driving

doi: 10.1109/JAS.2019.1911633

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