Model-Free Reinforcement Learning based Lateral Control for Lane Keeping

In this paper, the lateral control strategy for lane keeping task, which is an important module in the advanced assistant driver systems, is proposed based on the model-free reinforcement learning. Different from the model-based methods, our method only requires the generated data rather than the accurate system model. Furthermore, the lateral control strategy for driver model lane keeping is given, where driver controller and direct yaw controller (DYC) are working at the same time to maintain the vehicle stability. Note that the dynamic game theory is considered for this task, where the steering wheel controller for driver and the DYC compensated controller are obtained based on Nash game theory. Finally, we give simulation examples to prove the validity of the proposed schemes.