In the recent research of autonomous vehicle (AV) control methods, machine learning techniques are gaining more and more popularity due to their possible performance advantages over classical model based methods. As lane keeping or trajectory tracking through steering intervention is one of the key feature of the autonomous vehicle, several methods had been proposed and utilized both with model and machine learning based controllers. Present paper focuses on the integration of a robust Linear Parameter Varying (LPV) and a Reinforcement Learning (RL) controller in a supervisor structure, with the aim to guarantee stability against disturbances not considered during the training process of the RL agent. Hence, with the supervision of the proposed LPV controller, noisy or faulty GPS signals can be handled safely, without compromising the trajectory tracking performance of the RL agent during normal operation of the autonomous vehicle. For the demonstration of the proposed LPV supervised RL control method, highway simulations have been carried out and compared in CarSim simulation environment.

The paper presents a novel observer design method for estimating the front and rear wheel slips of the vehicle. The proposed observer design technique consists of two parts: A simple linear observer algorithm, which uses a reformulated lateral vehicle model to estimate the tire slips. The second part is based on an ultra-local model. The main goal of the ultra-local model is to eliminate the nonlinear, unmodeled, uncertain dynamics of the lateral vehicle model. In this way, the performance level of the linear observer can be significantly increased especially under critical circumstances such as high lateral acceleration maneuvers or driving on a low µ surface. The proposed observer algorithm is implemented in MATLAB/Simulink environment connected to the high-fidelity simulation software, CarMaker. The operation and the effectiveness of the proposed observer are demonstrated through several simulation examples.