Before revision:  In this paper, an internal reward function-based driving model that emulates the human's internal decision-making mechanism is proposed. Besides, a sampling-based inverse reinforcement learning (IRL) algorithm that learns the reward function from human naturalistic driving data is also developed.
After revision:  In this paper, an internal reward function-based driving model that emulates the human's internal decision-making mechanism is utilized. To infer the reward function from naturalistic human driving data, we propose a structural assumption about human driving behavior that focuses on discrete latent driving intentions. It converts the continuous behavior modeling problem to a discrete setting and thus makes maximum entropy inverse reinforcement learning (IRL) algorithm that learns the reward function from human naturalistic driving data is also developed.