Driving through complex urban environments is a challenging task for autonomous vehicles
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …

Autonomous driving has been at the forefront of public interest, and a pivotal debate to
widespread concerns is safety in the transportation system. Deep reinforcement learning …

Decision-making for automatic vehicles at unsignalized intersections with dense traffic is
one of the most challenging tasks. Due to the complex structure and frequent traffic …

Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore,
it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most …

In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …

Autonomous driving decision-making is a challenging task due to the inherent complexity
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …

Deep reinforcement learning (RL) has been widely applied to motion planning problems of
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …

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

Driving decision-making determines the safety and rationality of autonomous commercial
vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision …

Autonomous driving has gained an increased interest in both academia and industry, as
autonomous vehicles (AVs) significantly improve road safety by reducing traffic accidents …