Estimation of personal driving style via deep inverse reinforcement learning

When applying autonomous driving technology in human-crewed vehicles, it is essential to consider the personal driving style with ensuring not only safety but also the driver's preference. Reinforcement learning (RL) has attracted much attention in the field of autonomous driving; however, it requires a finely tuned reward function. A method for tasks that are difficult to design reward functions, such as reproducing a personal driving style, is inverse reinforcement learning (IRL). Although IRL is commonly applied to the estimation of human and animal intentions, most previous methods require high computational costs to compute inner loop RL. For the problem of inner loop RL, Logistic Regression-Based IRL (LogReg-IRL), which does not require RL for reward estimation, is available. Moreover, LogReg-IRL can compute a value function as well as a reward function of the driver's own. Therefore, this paper proposes a method to estimate the latent driving preferences (called driving style) of a driver using the rewards and values obtained by applying LogReg-IRL. Several experimental results show that the proposed method could reproduce the original trajectory and quantify the driver's implicit preference.