# Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization
## 5 Conclusions  
AVP has a wide range of practical applications in commercial, civil, military, etiquette, and other fields; however, its trajectory optimization is a multi-objective, non-linear, and extremely complex optimization problem. Hence, it is challenge to determine the ideal reference trajectory with collision avoidance and a smooth and short enough path length. In order to improve the optimization performance of AVP reference trajectories, an optimization model of AVP reference trajectories was constructed based on cubic spline interpolation, and an IISSO was proposed to solve the reference trajectory issues. Compared with the traditional AVP trajectory optimization algorithm, its superiority can be described as follows:  
Firstly, a novel, reasonable, and feasible reference trajectory optimization model for AVP was established using the cubic spline interpolation method, which is helpful to solve the four practical problems of the poor obstacle avoidance performance of the reference trajectory, the low smoothness of the trajectory, the long path, and the large tilt degree of parking. Secondly, in order to effectively heighten the global optimization performance of the shark optimization algorithm, refraction, Gaussian variation, and immune mechanisms were integrated into the shark optimization algorithm to effectively balance the computational intensity of the global exploration and local exploitation of the algorithm; hence, its global optimization performance was greatly improved. In order to confirm the effectiveness of the proposed optimization algorithm IISSO, the AVP of real vehicles was conducted. The results of the real vehicle experiments indicate that the IISSO obtained significantly better AVP reference trajectories than the other optimization performance algorithms, and its actual tracking control effect was also significantly better. However, there were several limitations to this study. 1. Real-world parking scenes include dynamic obstacles (e.g., pedestrians and other vehicles). When there are sudden obstacles during parking, the AVP system will trigger the stop command; however, it does not have the ability to continue tracking control and avoid obstacles. 2. Our research was limited to ordinary vehicles; hence, it is not applicable to special vehicles, such as trucks, heavy-duty vehicles, large vehicles, and small vehicles. 3. There is still room for further improvement of the results, although compared with the existing results, our research results were improved. 4. The research presented was still in the experimental stage, which required the configuration of a relatively complex and expensive AVP system; hence, we remain far from having a product that can be mass-produced. Conceptualization, investigation, resources, Y.C. ; writing--original draft preparation, writing--review, Y.C. and L.W. ; editing, supervision, language polishing, G.L., L.W. and B.X. ; funding, Y.C., G.L. and L.W. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors declare that this study received funding from the National Natural Science Foundation of China (62341313); the Key Projects of Natural Science Research in Universities of Anhui Province, grant numbers (KJ2021A1138, 2023 AH052354); the Chizhou University Nature Key Project, grant number (CZ2022 ZKZ08); the Chizhou University Nature Transverse Project, grant numbers (2023 HX0060), 2024 HX0044); the General Project of the Natural Science Foundation of Inner Mongolia (2023 MS06013); basic scientific research business expresses of colleges and universities directly under the Inner Mongolia Autonomous Region's Plan to Improve the Scientific Research and Innovation Ability of Young Teachers (GXKY22125); the Inner Mongolia Minzu University Doctoral Research Initiation Fund Project (BS416); the Liaoning Provincial Department of Transportation Scientific Research Project, grant number (202344); and the Liaoning Provincial Department of Education Scientific Research Project, grant number (JYTMS20230038).