The roundabout is a typical changeable, interactive scenario in which automated vehicles
should make adaptive and safe decisions. In this article, an optimization embedded …

Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic
transportation environments. In this paper, a reinforcement learning approach with value …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation
system in the future. Many studies have been made to improve AVs' ability of environment …

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 …

Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications such …

Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

The essential of developing an advanced driving assistance system is to learn human-like
decisions to enhance driving safety. When controlling a vehicle, joining roundabouts …

The development of intelligent driving technologies is expected to have the potential in
energy economics. Some reported studies mainly focused on the economical driving …