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Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion

Author

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  • Pedro Bautista-Camino

    (Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
    Current address: Antonio García Cubas 1200, Colonia Antonio Buenfil, Celaya 38010, Mexico.
    These authors contributed equally to this work.)

  • Alejandro I. Barranco-Gutiérrez

    (Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
    Current address: Antonio García Cubas 1200, Colonia Antonio Buenfil, Celaya 38010, Mexico.
    These authors contributed equally to this work.)

  • Ilse Cervantes

    (Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Unidad Querétaro, Querétaro 76090, Mexico
    These authors contributed equally to this work.)

  • Martin Rodríguez-Licea

    (Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
    Current address: Antonio García Cubas 1200, Colonia Antonio Buenfil, Celaya 38010, Mexico.
    These authors contributed equally to this work.)

  • Juan Prado-Olivarez

    (Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
    Current address: Antonio García Cubas 1200, Colonia Antonio Buenfil, Celaya 38010, Mexico.
    These authors contributed equally to this work.)

  • Francisco J. Pérez-Pinal

    (Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
    Current address: Antonio García Cubas 1200, Colonia Antonio Buenfil, Celaya 38010, Mexico.
    These authors contributed equally to this work.)

Abstract

Local path planning is a key task for the motion planners of autonomous vehicles since it commands the vehicle across its environment while avoiding any obstacles. To perform this task, the local path planner generates a trajectory and a velocity profile, which are then sent to the vehicle’s actuators. This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which was inspired by the behavior of movement of living beings, along with an algorithm that takes into account four acceleration policies, the ST dynamic vehicle model, and several constraints regarding the comfort and security. The original functions that define the ADA were modified in order to adapt it to the non-holonomic vehicle’s constraints and to improve its response when an impact scenario is detected. The present approach is validated in a well-known simulator for autonomous vehicles under three representative cases of study where the vehicle was capable of generating local paths that ensure the security of the vehicle in such cases. The results show that the approach proposed in this paper is a promising tool for the local path planning of autonomous vehicles since it is able to generate trajectories that are both safe and efficient.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1769-:d:760308
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    References listed on IDEAS

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    1. 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.
    2. Alessia Musa & Michele Pipicelli & Matteo Spano & Francesco Tufano & Francesco De Nola & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul & Gianluca Toscano, 2021. "A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
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    Cited by:

    1. Carlos Escobar & Francisco J. Vargas & Andrés A. Peters & Gonzalo Carvajal, 2023. "A Cooperative Control Algorithm for Line and Predecessor Following Platoons Subject to Unreliable Distance Measurements," Mathematics, MDPI, vol. 11(4), pages 1-18, February.

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