Autonomous driving has achieved significant milestones in research and development over
the last decade. There is increasing interest in the field as the deployment of self-operating …

Applications of deep learning (DL) in autonomous vehicle (AV) projects have gained
increasing interest from both researchers and companies. This has caused a rapid …

End-to-end approaches are one of the most promising solutions for autonomous vehicles
(AVs) decision-making. However, the deployment of these technologies is usually …

Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …

Reinforcement learning (RL) shows the potential to address drawbacks of rule-based control
and model predictive control and exhibits great effectiveness in heating, ventilation and air …

A novel real-time predictive control strategy is proposed for path following (PF) and vehicle
stability of autonomous electric vehicles under extreme drive conditions. The investigated …

Autonomous driving in urban environments requires intelligent systems that are able to deal
with complex and unpredictable scenarios. Traditional modular approaches focus on …

The autonomous vehicle is widely applied in various ground operations, in which motion
planning and tracking control are becoming the key technologies to achieve autonomous …

Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. Although significant progress has been achieved, existing decision …

This paper proposes an offline sizing method and an online energy management strategy
for the electric vehicle with semi-active hybrid battery system (HBS). The semi-active HBS is …