| Study on Intelligent Vehicle Trajectory Planning and Tracking Control Based on Improved APF and MPC |
| Qiping Chen1, Binghao Yu1, Shilong Min1, Lu Gan1, Chagen Luo2, Dequan Zeng1, Yiming Hu1, Qin Liu3 |
1Key Laboratory of Conveyance and Equipment Ministry of Education, East China Jiaotong University, Nanchang, 330013, China
2Nanchang Automotive Institute of Intelligence and New Energy, Tongji University, Shanghai, 330052, China
3Jiangling Motor Co., Ltd, Nanchang County, No. 2111, Yingbin Middle Avenue, Nanchang City, 330001, Jiangxi Province, China |
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Received: March 24, 2023; Revised: December 13, 2023 Accepted: September 10, 2024. Published online: November 25, 2024. |
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| ABSTRACT |
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Aiming at the problem of low accuracy and poor stability of trajectory planning and tracking control in the field of intelligent driving technology, this paper proposes an intelligent vehicle trajectory planning and tracking control method based on improved APF and MPC algorithms. First, based on the traditional artificial potential field algorithm, different potential fields and road boundary potential fields are established by comprehensively considering the changing trend of the vehicle traveling road, the dynamically traveling vehicle and the road boundary, etc., and a kind of improved APF algorithm which is more in line with the actual driving environment of the vehicle is designed. Second, a three-degree-of-freedom vehicle dynamics model is used to establish the MPC trajectory tracking controller, and dynamics constraints are added as well as the optimization objective function is designed to improve the precision and lateral stability of the vehicle tracking reference trajectory. Finally, the effectiveness of the proposed method is verified by Simulink/Carsim joint simulation experiments. The results show that the trajectory planning and tracking control method proposed in this paper performs well in terms of safety, real-time and stability, and has good applicability to different road adhesion coefficients and different vehicle speed conditions. |
| Key Words:
Intelligent vehicle · Improved APF · MPC · Trajectory planning · Tracking control |
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Corresponding Author.
Qiping Chen , Email. 
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