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 …

Advanced driver assistance systems have gained popularity as a safe technology that helps
people avoid traffic accidents. To improve system reliability, a lot of research on driving …

To further improve the learning efficiency and performance of reinforcement learning (RL), in
this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework …

Driver Behavior Modeling (DBM) aims to predict and model human driving behaviors, which
is typically incorporated into the Advanced Driver Assistance System to enhance …

Modeling driving behavior plays a pivotal role in advancing the development of human-like
autonomous driving. In light of this, this paper proposes a car-following behavior modeling …

We consider the crucial task of estimating an expert's discount factor in Inverse
Reinforcement Learning (IRL) to facilitate a better synthesis towards the resulting optimal …

There has been extensive research on active safety systems in the ITS community in recent
years that has significantly contributed to reducing traffic accidents. However, further …

Traffic simulation has the potential to facilitate the development and testing of autonomous
vehicles, as a supplement to road testing. Since autonomous vehicles will coexist with …

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which
can be extended to solve some complex and realistic decision-making problems …

Decision making in dense traffic uncertainty scenarios is challenging for autonomous
vehicles. Compare with costly manually designed driving policy, deep Reinforcement …