A new reinforcement learning vehicle control architecture for vision-based road following

Abstract

A new dynamic control architecture based on reinforcement learning (RL) has been developed and applied to the problem of high-speed road following of high-curvature roads. Through RL, the control system indirectly learns the vehicle-road interaction dynamics, knowledge which is essential to stay on the road in high-speed road tracking. First, computer simulation has been carried out in order to test stability and performance of the proposed RL controller before actual use. The proposed controller exhibited a good road tracking performance, especially on high-curvature roads. Then, the actual autonomous driving experiments successfully verified the control performance on campus roads in which there were shadows from the trees, noisy and/or broken lane markings, different road curvatures, and also different times of the day reflecting a range of lighting conditions. The proposed three-stage image processing algorithm and the use of all six strips of edges have been capable of handling most of the uncertainties arising from the nonideal road conditions.