A transportable neural-network approach to autonomous vehicle following

This paper presents the development and testing of a neural-network module for autonomous vehicle following. Autonomous vehicle following is defined as a vehicle changing its own steering and speed while following a lead vehicle. The strength of the developed controller is that no characterization of the vehicle dynamics is needed to achieve autonomous operation. As a result, it can be transported to any vehicle regardless of the nonlinear and often unobservable dynamics. Data for the range and heading angle of the lead vehicle were collected for various paths while a human driver performed 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 collected under manual control-one network for speed control and the other for steering control. After training, live vehicle following runs were done under the neural-network control. The results obtained indicate that it is feasible to employ neural networks to perform autonomous vehicle following.