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Path Planning Method for Perpendicular Parking Based on Vehicle Kinematics Model Using MPC Optimization
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 29, 2022 by SAE International in United States
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In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC). The final path is easier to be followed, and thereby improving the accuracy of parking results. Several simulations and experiments were carried out for the scenarios of one step parking and multiple step parking respectively. The simulation results verify the effectiveness and real-time performance of the proposed method. The parking path is smooth and the vehicle can effectively follow it, thus the vehicle can be accurately parked at the desired location.
CitationLi, Z., Xiong, L., Leng, B., Fu, Z. et al., "Path Planning Method for Perpendicular Parking Based on Vehicle Kinematics Model Using MPC Optimization," SAE Technical Paper 2022-01-0085, 2022, https://doi.org/10.4271/2022-01-0085.
- Dubins , L.E. On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents American Journal of Mathematics 79 3 1957 497 516
- Reeds , J.A. and Shepp , L.A. Optimal Paths for a Car that Goes Both Forwards and Backwards Pacific Journal of Mathematics 145 2 1990 367 393
- Min , K. and Choi , J. Design and Implementation of an Intelligent Vehicle System for Autonomous Valet Parking Service 2015 10th Asian Control Conference (ASCC) 2015 1 6 10.1109/ASCC.2015.7244802
- Klaudt , S. , Zlocki , A. , and Eckstein , L. A-Priori Map Information and Path Planning for Automated Valet-Parking 2017 IEEE Intelligent Vehicles Symposium (IV) 2017 1770 1775
- Kwon , H. and Chung , W. Performance Analysis of path Planners for Car-Like Vehicles Toward Automatic Parking Control Intelligent Service Robotics 7 1 2014 15 23
- Liyang , S. , Yu , H. , Xuezhi , C. , Changhao , J. , and Miaohua , H. Path Planning Based on Clothoid for Autonomous Valet Parking 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) 2020 389 393
- Jeong , Y.W. , Heo , H.W. and Chung , C.C. A Local Path Planning for Perpendicular Parking in Limited Parking Spaces 2020 20th International Conference on Control, Automation and Systems (ICCAS) 2020 38 40
- Zou , R. , Wang , S. , Wang , Z. , Zhao , P. , and Zhou , P. A Reverse Planning Method of Autonomous Parking Path 2020 5th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) 2020 92 98 10.1109/ACIRS49895.2020.9162616
- Zhang , J. , Chen , H. , Song , S. , and Hu , F. Reinforcement Learning-Based Motion Planning for Automatic Parking System IEEE Access 8 2020 154485 154501
- Yang , K. and Sukkarieh , S. 3D Smooth Path Planning for a UAV in Cluttered Natural Environment Intelligent Robots and Systems, Iros, IEEE/RSJ International Conference on Nice IEEE 2008 794 800
- Yu , Z. , Gao , Z. , Chen , H. , and Huang , Y. SPFCN: Select and Prune the Fully Convolutional Networks for Real-Time Parking Slot Detection IEEE Intelligent Vehicles Symposium (IV) 2020 2020 445 450