Automatic parking path optimization is a key point for automatic parking. However, it is difficult to obtain the smooth, accurate and optimal parking path by using traditional automatic parking optimization algorithms. So, based on the automatic parking path optimization model for cubic spline interpolation, an improved automatic parking path optimization based on the immune moth flame algorithm is proposed for intelligent vehicles. Firstly, to enhance the global optimization performance, an automatic parking path optimization model for cubic spline interpolation is designed by using shortest parking path as optimization target. Secondly, an improved immune moth flame algorithm (IIMFO) based on the immune mechanism, Gaussian mutation mechanism and opposition-based learning strategy is proposed, and an adaptive decreasing inertia weight coefficient is integrated into the moth flame algorithm so that these strategies can improve the balance quality between global search and local development effectively. Finally, the optimization results on the several common test functions show that the IIMFO algorithm proposed in this paper has higher optimization precision. Furthermore, the simulation and semi-automatic experiment results of automatic parking path optimization practical cases show that the improved automatic parking path optimization based on the immune moth flame algorithm for intelligent vehicles has a better optimization effect than that of the traditional automatic parking optimization algorithms.

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.