This letter presents an approach for implementing game-theoretic decision-making in
combination with deep reinforcement learning to allow vehicles to make decisions at an …

Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …

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

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 …

Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics 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 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 …

Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …

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 …

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 …