A new reinforcement learning vehicle control architecture for vision-basedroad following

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 c arried out in order to test stability and performance of the proposed RL co ntroller before actual use. The proposed controller exhibited a good road t racking performance, especially on high-curvature roads, Then, the actual a utonomous driving experiments successfully verified the control performance on campus roads in which there were shadows from the trees, noisy and/or b roken lane markings, different road curvatures, and also different times of the day reflecting a range of lighting conditions. The proposed three-stag e image processing algorithm and the use of all six strips of edges have be en capable of handling most of the uncertainties arising from the nonideal road conditions.