Lane Keeping Assist for an Autonomous Vehicle Based on Deep Reinforcement Learning

Lane keeping assist (LKA) is an autonomous driving technique that enables vehicles to travel along a desired line of lanes by adjusting the front steering angle. Reinforcement learning (RL) is one kind of machine learning. Agents or machines are not told how to act but instead learn from interaction with the environment. It also frees us from coding complex policies manually. But it has not yet been successfully applied to autonomous driving. Two control strategies using different deep reinforcement learning (DRL) algorithms have been proposed and used in the lane keeping assist scenario in this paper. Deep Q-network (DQN) algorithm with discrete action space and deep deterministic policy gradient (DDPG) algorithm with continuous action space have been implemented, respectively. Based on MATLAB/Simulink, deep neural networks representing the control policy are designed. The environment as well as the vehicle dynamics are also modelled in Simulink. By integrating the proposed control method and a vehicle dynamics model, the lane keeping assist simulation is performed. Experimental results demonstrate that the vehicle travel along the centerline of the path and the controller reaches a steady state after a short time, validating the effectiveness of the proposed control method.