Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …

In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …

In the context of Automated Vehicles, the Automated Lane Change system, is fundamentally
based upon the separate constructs of Perception, Decision making, Trajectory Planning …

The three main modules of autonomous vehicles, ie, sensing, decision making, and motion
controlling, have been studied separately in most existing works on autonomous driving …

Autonomous vehicles promise to improve traffic safety while, at the same time, increase fuel
efficiency and reduce congestion. They represent the main trend in future intelligent …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

We present a control approach for autonomous vehicles based on deep reinforcement
learning. A neural network agent is trained to map its estimated state to acceleration and …

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) …

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years,
with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed …