Comfortable Driving by using Deep Inverse Reinforcement Learning

Abstract

Passenger comfort and their safety are pre-requisites to realizing autonomous driving vehicles. Herein, we define “comfortable driving” by considering “comfortability”, with which less physical and mental burden for passengers. Deep reinforcement learning, which has several applications in the autonomous driving domain, is an effective approach to achieve the comfortable driving. Generally, reward function in deep reinforcement learning is expressed quantitatively. However, because obtaining a quantitative expression for comfortable driving is difficult, there is no guarantee that a reward function can satisfy “comfortable driving” conditions. Therefore, we propose an approach to identify reward function that can realize comfortable driving, using LogReg-IRL, a deep inverse reinforcement learning method in linearly solvable Markov decision process. With the constraint that the maximum lateral acceleration does not exceed a certain threshold value, we could experimentally achieve “comfortable driving”. Additionally, by calculating the gradient for the state input of the state-dependent reward function, we could analyze important states.