Autonomous Driving Vehicles with Unmatched Disturbance Compensation using Deep Neural Networks

In this paper, we propose an algorithm compensating for an unmatched disturbance which is difficult to thoroughly remove. In order to compensate for the disturbance of an autonomous driving vehicle, we exploit Deep Neural Network(DNN). It is well-known that we cannot compute the control command that completely compensates for any unmatched disturbance. In vehicle lateral dynamics using a lane keeping system with a look-ahead distance, there always exists the disturbance caused by a curvature of road. Thus, it is inevitable that the lane keeping system only with a feedback control has steady-state error in the lane center on a curved road. To resolve this problem, we utilize the feedback control system with DNN. The proposed method is unique in compensating for the unmatched disturbance in lane keeping system. From a simulation study, we observed that the proposed method could reduce lateral tracking error up to a half on a curved road.