Fuzzy Based Adaptive Dimension Parking Algorithm Including Obstacle Avoidance for Autonomous Vehicle Parking

Abstract

Autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention in recent times for autonomous vehicle-related solutions. Existing autonomous vehicle parking algorithms generally fail to mimic the human-like tendency to adapt naturally, and most of these designs are practically fixed. They do not preserve adaptive nature with machine dynamics, especially vehicles related. In this paper, a novel fuzzy-based adaptive dimension parking algorithm (FADPA) is proposed that integrates obstacle avoidance capabilities to a standalone parking controller that is made adaptive to vehicle dimensions in order to provide human-like intelligence for parking problems. This algorithm adopts fuzzy membership thresholds with respect to vehicle dimensions to enhance the vehicle's path during parking with taking care of obstacles. It is generalized for all segments of cars, and different simulation results are presented to show the effectiveness of the proposed algorithm.