Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …

To fulfill high-level automation, an automated vehicle needs to learn to make decisions and
control its movement under complex scenarios. Due to the uncertainty and complexity of the …

Decision-making is still a significant challenge to realize fully autonomous driving. Using
deep reinforcement learning (DRL) to solve autonomous driving decision-making problems …

This study presents a three‐lane highway overtaking strategy for an automated vehicle,
which is based on a heuristic planning reinforcement learning algorithm. The proposed …

In this study, a deep reinforcement learning approach is proposed to handle tactical driving
in complex highway traffic environments for unmanned ground vehicles. Tactical driving is a …

In order to solve the manual labelling, long-tail effect and driving conservatism of the existing
decision-making algorithm. This paper proposed an integrated decision-making framework …

Making proper decisions at intersections that are one of the most dangerous and
sophisticated driving scenarios is full of challenges, especially for autonomous vehicles …

Numerous accidents and fatalities occur every year across the world as a result of the
reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to …

Road intersection is one of the most complex and accident-prone traffic scenarios, so it's
challenging for autonomous vehicles (AVs) to make safe and efficient decisions at the …

As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a
prominent role in the decision-making process of autonomous driving (AD), which enables …