In this study, a deep reinforcement learning approach is proposed to handle tactical driving
in complex highway traffic environments for unmanned ground vehicles. Tactical driving is a …

Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …

Great progress has been made in the field of machine learning in recent years. And learning-
based methods have been widely utilized for developing highly autonomous vehicle. To this …

This work optimizes the highway decision making strategy of autonomous vehicles by using
deep reinforcement learning (DRL). First, the highway driving environment is built, wherein …

Autonomous driving has been the trend. In this paper, a Deep Reinforcement Learning
(DRL) method is exploited to model the decision making and interaction between vehicles …

Autonomous vehicles have the potential to revolutionize intelligent transportation by
improving traffic safety, increasing energy efficiency, and reducing congestion. In this study …

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

To further enhance decision making in autonomous vehicles field, grant more safety,
comfort, reduce traffic, and accidents, learning approaches were adopted, mainly …

This paper proposes a systematic driving framework where the decision making module of
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …

Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should
be able to drive to its destination as fast as possible while avoiding collision, obeying traffic …