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Research on Automatic Driving Trajectory Planning and Tracking Control Based on Improvement of the Artificial Potential Field Method

Author

Listed:
  • Yongyi Li

    (College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China)

  • Wei Yang

    (College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China)

  • Xiaorui Zhang

    (College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China)

  • Xi Kang

    (College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China)

  • Mengfei Li

    (College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China)

Abstract

With the continuous increase in motor vehicle ownership in recent times, traditional transportation has been unable to meet people’s travel needs. Research on autonomous driving technology will help solve a series of problems associated with driving, such as traffic accidents, traffic congestion, energy consumption, and environmental pollution. In this paper, an improved artificial potential field method is proposed to complete the planning of automatic driving trajectories by adding the distance adjustment factor, dynamic road repulsive field, velocity repulsive field, and acceleration repulsive field. The invasive weed algorithm is introduced to solve the defects associated with the traditional artificial potential field method. The prediction model—for which corresponding constraint variables were set and an optimal objective function was established to build up the MPC model controller to achieve the goal of trajectory tracking—was linearized and discretized from a vehicle dynamics model. Finally, co-simulation based on MATLAB and CarSim was used to verify the practicability of the model.

Suggested Citation

  • Yongyi Li & Wei Yang & Xiaorui Zhang & Xi Kang & Mengfei Li, 2022. "Research on Automatic Driving Trajectory Planning and Tracking Control Based on Improvement of the Artificial Potential Field Method," Sustainability, MDPI, vol. 14(19), pages 1-28, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12131-:d:924828
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    References listed on IDEAS

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    1. Ci, Yusheng & Wu, Lina & Zhao, Jiafa & Sun, Yichen & Zhang, Guohui, 2019. "V2I-based car-following modeling and simulation of signalized intersection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 672-679.
    2. Wu, Lina & Ci, Yusheng & Wang, Yunpeng & Chen, Peng, 2020. "Fuel consumption at the oversaturated signalized intersection considering queue effects: A case study in Harbin, China," Energy, Elsevier, vol. 192(C).
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