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

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.

Automated summary

This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which is capable of generating trajectories that are both safe and efficient.