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

Pedro Bautista-Camino, Alejandro I. Barranco-Gutiérrez, Ilse Cervantes, Martin Rodríguez-Licea, Juan Prado-Olivarez and Francisco J. Pérez-Pinal
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Pedro Bautista-Camino: Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
Alejandro I. Barranco-Gutiérrez: Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
Ilse Cervantes: Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Unidad Querétaro, Querétaro 76090, Mexico
Martin Rodríguez-Licea: Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
Juan Prado-Olivarez: Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico
Francisco J. Pérez-Pinal: Laboratorio de Transporte Sostenible, Instituto Tecnologico de Celaya, Tecnologico Nacional de México, Celaya 38010, Mexico

Energies, 2022, vol. 15, issue 5, 1-23

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.

Keywords: local path planning; autonomous vehicles; obstacles avoidance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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