Neural network architecture for trajectory generation and control of automated car parking

Abstract

This paper describes the development of a control system to support an automated parking mode in driving passenger cars. By using recent advances in the artificial neural network technology and a combination of linear feedback and nonlinear feedforward control, we propose a novel architecture for the parking motion controller. The paper presents the results of the controller design and analysis, including parking problem analysis, stability analysis for the feedback controller, formulation and optimal solution of the parking trajectory planning problem, and design of a parking motion planning architecture based on a radial basis function network. Two general cases of backward parking considered in this work are emulated using the proposed controller. The emulation results reveal high efficiency of the presented approach and demonstrate that the proposed system can be implemented on a typical passenger car.