Classic and Modern Methods of Automatic Parking Control of Self-driving Cars

The problem of automatic parking control of an unmanned car is considered. The formulation and formalization of the problem of the car parking control taking into account the restrictions that ensure the safety of the parking maneuver are given. Classical and modern methods of the automatic parking control of unmanned cars are analyzed. Based on the Dubins and Reeds-Shepp motion models, optimal algorithms developed for the car parking control are synthesized. A fast-growing random tree algorithm RRT is used to construct a path between two points. Based on the method of machine learning involving reinforcement, a car parking control algorithm is synthesized. The algorithm convergence is investigated, and the optimal values of the training parameters are determined. The results of the computer testing of synthesized parking algorithms implemented in Python using mathematical libraries Matplotlib and NumPy are presented.