Lane Keeping Algorithm for Autonomous Driving via Safe Reinforcement Learning

Abstract

Autonomous driving can possibly facilitate the load and change the methods for transportation in our everyday life. Work is being done to create algorithms of decision making and motion control in Autonomous driving. Recently, reinforcement learning has been a predominant strategy applied for this purpose. But, problems of using reinforcement learning for autonomous driving is that the actions taken while exploration can be unsafe, and the convergence can be too slow. Therefore, before making an actual vehicle learn driving through reinforcement learning, there is an urgent need to solve the safety issue. The significance of this paper is that, it introduces Safe Reinforcement Learning (SRL) into the field of autonomous driving. Safe reinforcement learning is the method of adding constraints to ensure the safe exploration. This paper explores the Constrained Policy Optimization (CPO) algorithm. The principle is to introduce constraints in the cost function. CPO is based on the framework of the Actor-Critic algorithm where the space that is explored during the policy update process is enforced by setting tough constraints which reduces the size of policy update. A comparison is also made with typical reinforcement learning algorithms to prove its advantages in learning efficiency and safety.