Parallel Planning: A New Motion Planning Framework for Autonomous Driving

Long Chen; Xuemin Hu; Wei Tian; Hong Wang; Dongpu Cao; Fei-Yue Wang

doi:10.1109/JAS.2018.7511186

Volume 6 Issue 1

Jan. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(1): 236-246

Long Chen, Xuemin Hu, Wei Tian, Hong Wang, Dongpu Cao and Fei-Yue Wang, "Parallel Planning: A New Motion Planning Framework for Autonomous Driving," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 236-246, Jan. 2019. doi: 10.1109/JAS.2018.7511186

Citation:

Long Chen, Xuemin Hu, Wei Tian, Hong Wang, Dongpu Cao and Fei-Yue Wang, "Parallel Planning: A New Motion Planning Framework for Autonomous Driving," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 236-246, Jan. 2019. doi: 10.1109/JAS.2018.7511186

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Parallel Planning: A New Motion Planning Framework for Autonomous Driving

doi: 10.1109/JAS.2018.7511186

Long Chen^1
,,
Xuemin Hu^{2
,
,},
Wei Tian^3
,,
Hong Wang^4
,,
Dongpu Cao^4
,,
Fei-Yue Wang^5
,

1.
School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510275, China
2.
School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China
3.
Institute of Measurement and Control Systems, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany
4.
Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L3G1, Canada
5.
State Key Laboratory of Management and Control for Complex Systems. Institute of Automation, Chinese Academy of Sciences, China, and also with the Research Center for Military Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha 410073, China

Funds:

the National Natural Science Foundation of China 61773414

the National Natural Science Foundation of China 61806076

Hubei Provincial Natural Science Foundation of China 2018CFB158

More Information

Author Bio:
Long Chen (SM'18) received the Ph.D. degree in signal and information processing from Wuhan University, Wuhan China, in 2013. 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 School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. He received the IEEE Vehicular Technology Society 2018 Best Land Transportation Paper Award, IEEE Intelligent Vehicle Symposium 2018 Best Student Paper Award and Best Workshop Paper Award. His 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 and Guest Editor for IEEE Transactions on Intelligent Vehicles. e-mail: chenl46@mail.sysu.edu.cn

Xuemin Hu received the B.Eng. degree in biomedical engineering from Huazhong University of Science and Technology and the Ph.D. degree in signal and information processing from Wuhan University in 2007 and in 2012, respectively. He was a visiting scholar in the University of Rhode Island, Kingston, RI, US from November 2015 to May 2016. He is currently an Associate Professor of School of Computer Science and Information Engineering, Hubei University, Wuhan China. His research interests include motion planning, computer vision and autonomous driving. e-mail: huxuemin2012@hubu.edu.cn

Wei Tian received the B.Sc degree in mechatronics engineering from Tongji University, Shanghai, China, in 2010. From October 2010, he was with the Department of Electrical Engineering and Information Technology at KIT, Karlsruhe, Germany, and received the M.Sc. degree in May 2013. He is currently working toward the Ph.D. degree at the Institute of Measurement and Control Systems at KIT. His research interests include robust object detection and tracking. e-mail: wei.tian@kit.edu

Hong Wang received the Ph.D. degree in Beijing Institute of Technology in China in 2015. Her research focuses on the component sizing, modeling of hybrid powertrains and power management control strategies design for hybrid electric vehicles; intelligent control theory and application; path planning for autonomous vehicles. e-mail: hong.wang@uwaterloo.ca

Dongpu Cao (M'08) received the Ph.D. degree from Concordia University, Canada, in 2008. He is currently a Senior Lecturer at Advanced Vehicle Engineering Center, Cranfield University, UK. His research focuses on vehicle dynamics, control and intelligence, where he has contributed more than 90 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, IEEE Transactions on Industrial Electronics and ASME Journal of Dynamic Systems, Measurement, and Control. He has been a Guest Editor for IEEE/ASME Transactions on Mechatronics, Vehicle System Dynamics, and IEEE Transactions on Human-Machine Systems. He serves on the SAE International Vehicle Dynamics Standards Committee and a few ASME, SAE, IEEE technical committees. e-mail: dongpu@uwaterloo.ca

Fei-Yue Wang (S'87-M'89-SM'94-F'03) received his Ph.D. 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 of Management and Control for Complex Systems. Dr. Wangs 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.wang@ia.ac.cn
Corresponding author: Xuemin Hu, e-mail: huxuemin2012@hubu.edu.cn
Received Date: 2017-11-21
Revised Date: 2018-03-08
Accepted Date: 2018-04-06

Abstract

Abstract

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as "parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality. A deep planning model which combines a convolutional neural network (CNN) with the Long Short-Term Memory module (LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human drivers. Moreover, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder (VAE) and a generative adversarial network (GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.
- Autonomous driving,
- artificial traffic scene,
- deep learning,
- emergencies,
- motion planning,
- parallel planning

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References(37)

References

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Parallel Planning: A New Motion Planning Framework for Autonomous Driving

doi: 10.1109/JAS.2018.7511186

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