… reinforcement learning with the proposed structural assumption to driving behavior modeling
from naturalistic highway driving … of the proposed method in driving behavior modeling both …

… Abstract: High-level driving behavior decision-making is an … reinforcement learning based
high-level driving behavior … , and a deep reinforcement learning network that learns the optimal …

… driver behaviors in modeling car-following behavior. Some researchers proposed data-driven
methods to learn driver behaviors … However, these driver behavior simulation approaches …

… to express driver behavior model based on the reward which recovered with IRL method
in this large-scale state space. In this paper, we focus on driving behavior modeling with IRL …

… Hence, to describe the influence of both discrete and continuous decisions, we propose a
hierarchical inverse reinforcement learning (IRL) framework in this paper, to learn trajectory-…

… , we proposed a behavior adaptation function which enables changing desired driving behavior
online … This eliminates the requirement to generate new models for different driving style …

… model this behavior. We show that during the training of deep reinforcement learning, our …
driving behavior. This can eventually help promote the quality of the learned lane change …

… In section III, we introduce our approach to inverse reinforcement learning of cost functions,
before we describe the application of the approach in section IV. Finally, we evaluate our …

… reinforcement learning-based driver behavior model is designed to learn a cost function
distribution from driver-… This paper presents a novel driver behavior learning approach that …

… Moreover, IL methods struggle with the complexity of capturing the nuances of human
driving behavior, which can lead to suboptimal or even unsafe driving decisions in unexpected …