In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …

It is a striking fact that the characteristics of parametric uncertainties, external disturbance,
time-varying and nonlinearities are available in the constructed model of autonomous …

For a foreseeable future, autonomous vehicles (AVs) will operate in traffic together with
human-driven vehicles. Their planning and control systems need extensive testing …

Deep reinforcement learning (DRL) has been successfully applied in mapless navigation.
An important issue in DRL is to design a reward function for evaluating actions of agents …

Sampling-based motion planning methods are widely adopted in autonomous driving.
Typically, sampling can be decoupled into two layers: a path sampling layer and a speed …

Autonomous vehicle driving systems face the challenge of providing safe, feasible and
human-like driving policy quickly and efficiently. The traditional approach usually involves a …

Intelligent motion planning is one of the core components in automated vehicles, which has
received extensive interests. Traditional motion planning methods suffer from several …

Rapid advances in every sphere of autonomous driving technology have intensified the
need to be able to benchmark and compare different approaches. While many …

In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and
representing human behavior are important. Human behavior is naturally rich and diverse …