Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization

Abstract

Parking path optimization is the principal problem of automatic vertical parking (AVP); however, it is difficult to determine a collision avoiding, smooth, and accurate optimized parking path using traditional parking reference trajectory optimization methods. In order to implement high-performance automatic parking reference trajectory optimization, we establish an automatic parking reference trajectory optimization model using cubic spline interpolation, and we propose an improved immune shark smell optimization (IISSO) to solve it. Firstly, we take the length of the parking reference trajectory as the optimization objective, and we introduce an intelligent automatic parking path optimization model using cubic spline interpolation. Secondly, the improved immune shark optimization algorithm combines the immune, refraction, and Gaussian variation mechanisms, thus effectively improving its global optimization ability. The simulation results for the parking path optimization experiments indicate that the proposed IISSO has a higher optimization accuracy and faster calculation speed; hence, it can obtain a parking path with higher optimization performance.