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Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm

Author

Listed:
  • Pengwei Wang

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Song Gao

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Liang Li

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Haidian District, Beijing 100084, China)

  • Binbin Sun

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Shuo Cheng

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Haidian District, Beijing 100084, China)

Abstract

Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are also verified.

Suggested Citation

  • Pengwei Wang & Song Gao & Liang Li & Binbin Sun & Shuo Cheng, 2019. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm," Energies, MDPI, vol. 12(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2342-:d:241021
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    References listed on IDEAS

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    1. Jiwei Feng & Chunjiang Bao & Jian Wu & Shuo Cheng & Guangfei Xu & Shifu Liu, 2018. "Research on Methods of Active Steering Control Based on Receding Horizon Control," Energies, MDPI, vol. 11(9), pages 1-15, August.
    2. Jian Wu & Shuo Cheng & Binhao Liu & Congzhi Liu, 2017. "A Human-Machine-Cooperative-Driving Controller Based on AFS and DYC for Vehicle Dynamic Stability," Energies, MDPI, vol. 10(11), pages 1-18, October.
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    Citations

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    Cited by:

    1. Sara Abdallaoui & El-Hassane Aglzim & Ahmed Chaibet & Ali Kribèche, 2022. "Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques," Energies, MDPI, vol. 15(4), pages 1-19, February.
    2. Leon Prochowski & Mateusz Ziubiński & Patryk Szwajkowski & Mirosław Gidlewski & Tomasz Pusty & Tomasz Lech Stańczyk, 2021. "Impact of Control System Model Parameters on the Obstacle Avoidance by an Autonomous Car-Trailer Unit: Research Results," Energies, MDPI, vol. 14(10), pages 1-31, May.
    3. Pier Giuseppe Anselma, 2021. "Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort," Energies, MDPI, vol. 14(10), pages 1-28, May.
    4. Sorin Liviu Jurj & Dominik Grundt & Tino Werner & Philipp Borchers & Karina Rothemann & Eike Möhlmann, 2021. "Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning," Energies, MDPI, vol. 14(22), pages 1-19, November.
    5. Tao Wang & Dayi Qu & Hui Song & Shouchen Dai, 2023. "A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    6. Canan G. Corlu & Rocio de la Torre & Adrian Serrano-Hernandez & Angel A. Juan & Javier Faulin, 2020. "Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities," Energies, MDPI, vol. 13(5), pages 1-33, March.
    7. Pedro Bautista-Camino & Alejandro I. Barranco-Gutiérrez & Ilse Cervantes & Martin Rodríguez-Licea & Juan Prado-Olivarez & Francisco J. Pérez-Pinal, 2022. "Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion," Energies, MDPI, vol. 15(5), pages 1-23, February.
    8. Kazuki Nonoyama & Ziang Liu & Tomofumi Fujiwara & Md Moktadir Alam & Tatsushi Nishi, 2022. "Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization," Energies, MDPI, vol. 15(6), pages 1-20, March.

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