A transportable neural network controller for autonomous vehicle following

Abstract

This paper presents the development and testing of a neural network controller for autonomous vehicle following. Autonomous vehicle following is defined as a vehicle controlling its own steering and speed while following a lead vehicle. The strength of the developed controller is that no characterization of vehicle dynamics is needed. As a result it can be transported to any vehicle regardless of its nonlinear and often unobservable dynamics. Data for the range and heading angle of the lead vehicle were collected for various paths with a human driver performing the vehicle following control function. The data was collected for different driving maneuvers including straight paths, lane changing and right/left turns. Two time-delay backpropagation neural networks were then trained based on the data obtained under manual control, one network for speed control and the other for steering control. After training, the vehicle following was done using the trained neural network controller. The results obtained and presented on a video tape indicate that it is feasible to employ a neural network to satisfactorily perform autonomous vehicle following.