Integration of Dimension-adaptive Obstacle Avoidance Algorithm in Fuzzy-based Autonomous Vehicle Parking

Abstract

Sensor-based obstacle avoidance and Autonomous vehicle parking have been immensely researched in recent times. An integration of both will increase the usability of autonomous parking systems in dynamic and uncertain environments. The fuzzy logic theory is widely used to learn expert human skills for machines. However, existing fuzzy-based expert systems generally fail to mimic the natural adaptive skills of humans. The expert driver has a natural tendency to adapt to machine dynamics, especially vehicle-related. This paper proposes a novel non-holonomic dimension-based obstacle avoidance parking algorithm that integrates obstacle avoidance capabilities to a standalone parking controller. This algorithm is developed based on adaptive fuzzy membership inferences concerning passenger cars' different sizes and segments. It is tested for various vehicles in simulation results to show the effectiveness of the algorithm.