Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

Wen Hu; Zejian Deng; Dongpu Cao; Bangji Zhang; Amir Khajepour; Lei Zeng; Yang Wu

doi:10.1109/JAS.2022.106049

Volume 9 Issue 12

Dec. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(12): 2161-2173

W. Hu, Z. J. Deng, D. P. Cao, B. J. Zhang, A. Khajepour, L. Zeng, and Y. Wu, “Probabilistic lane-change decision-making and planning for autonomous heavy vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2161–2173, Dec. 2022. doi: 10.1109/JAS.2022.106049

Citation:

W. Hu, Z. J. Deng, D. P. Cao, B. J. Zhang, A. Khajepour, L. Zeng, and Y. Wu, “Probabilistic lane-change decision-making and planning for autonomous heavy vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2161–2173, Dec. 2022. doi: 10.1109/JAS.2022.106049

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Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

doi: 10.1109/JAS.2022.106049

Wen Hu^1
,,
Zejian Deng^{2
,
,},
Dongpu Cao^3
,,
Bangji Zhang^{1
,
,},
Amir Khajepour^2
,,
Lei Zeng^1
,,
Yang Wu^3
,

1.
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
2.
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo ON N2L3G1, Canada
3.
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

Funds: This work was supported by the National Natural Science Foundation of China (5187051675)

More Information

Author Bio:
Wen Hu received the B.S. and M.S. degrees from Hunan University, in 2015 and 2018, respectively. He is currently pursuing Ph.D. degree in mechanical engineering with the College of Mechanical and Vehicle Engineering, Hunan University. He is also a visiting Ph.D. student with the Department of Mechanical and Mechatronics Engineering, University of Waterloo. His research interests include decision making and trajectory planning, vehicle dynamics, and control in intelligent vehicle

Zejian Deng received the M.S. degree in transportation engineering from Wuhan University of Technology in 2018 and is currently working toward the Ph.D. degree with the Department of Mechanical and Mechatronics Engineering, the University of Waterloo, Canada. His research interests include driver model and decision making in automated driving

Dongpu Cao (Member, IEEE) received the Ph.D. degree from Concordia University, Canada, in 2008. He is a Professor at Tsinghua University. His current research focuses on driver cognition, automated driving and cognitive autonomous driving. He has contributed more than 200 papers and 3 books. He received the SAE Arch T. Colwell Merit Award in 2012, IEEE VTS 2020 Best Vehicular Electronics Paper Award and 6 Best Paper Awards from international conferences. Prof. Cao has served as Deputy Editor-in-Chief for IET Intelligent Transport Systems Journal, and an Associate Editor for IEEE Transactions on Vehicular Technology, IEEE Transactions on Intelligent Transportation Systems, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, IEEE/CAA Journal of Automatica Sinica, IEEE Transactions on Computational Social Systems, and ASME Journal of Dynamic Systems, Measurement and Control. Prof. Cao is an IEEE VTS Distinguished Lecturer

Bangji Zhang received the Ph.D. degree in 2010 from Hunan University. He was a Visitor Scholar in University of Technology, Sydney, from 2013 to 2014. He is currently Professor of College of Mechanical and Research Fellow in State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body. His research interests include vehicle dynamics and control in intelligent vehicle

Amir Khajepour (Member, IEEE) is a Professor of Department of Mechanical and Mechatronics Engineering at the University of Waterloo. He holds the Canada Research Chair in Mechatronic Vehicle Systems, and NSERC/General Motors Industrial Research Chair in Holistic Vehicle Control. His research has resulted in many patents, technology transfers and over 500 journal and conference publications, as well as several books. He is a Fellow of the Engineering Institute of Canada, the American Society of Mechanical Engineers, and the Canadian Society of Mechanical Engineering

Lei Zeng received the B.S. degree from Nanchang University. He is now working toward the M.S. degree with the College of Mechanical and Vehicle Engineering, Hunan University. His research interests include reinforcement learning, decision-making, and planning in automated driving

Yang Wu received the B.E. degree in aircraft design and engineering from Xi’an Jiaotong University, in 2011. He received the Ph.D. degree in mechanical engineering from the College of Mechanical and Vehicle Engineering, Hunan University, in 2021. He is currently working as a Post-Doctoral Research Fellow at Tsinghua University. His research interests include nonlinear system control, active vehicle suspensions, autonomous vehicle control, and modelling of driver-vehicle system
Corresponding author: Zejian Deng, e-mail: z49deng@uwaterloo.ca; Bangji Zhang, e-mail: bangjizhang@hnu.edu.cn
Received Date: 2022-03-22
Accepted Date: 2022-06-30

Available Online: 2022-10-12

Abstract

Abstract

To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index (AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.
- Autonomous heavy truck,
- decision-making,
- driving aggressiveness,
- risk assessment,
- trajectory planning

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References(34)

References

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A hierarchical probabilistic decision-making and trajectory planning frame-work is developed for enhancing the safety and stability of the autonomous heavy trucks
An aggressiveness index (AI) is proposed to quantify the asymmetrical risks to the road user with the vehicle size and mass considered
To plan the dynamically feasible and more truck-friendly lane-change trajectory, the lateral and roll dynamics stabilities are considered in the trajectory planning module

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

doi: 10.1109/JAS.2022.106049

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