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

Autonomous vehicles have gained extensive attention in recent years, both in academia and
industry. For these self-driving vehicles, decision-making in urban environments poses …

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

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 …

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 …

Decision‐making is an important component of autonomous driving perception, decision‐
making, planning, and control pipeline, which undertakes the task of how the ego vehicle …

Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the
notion of lanes, and consider the whole lateral space within the road boundaries. This …

Autonomous driving has brought about a growing interest in enhancing traffic efficiency and
ensuring road safety. One of the fundamental functions of autonomous driving technology is …

Autonomous driving holds significant promise for substantially reducing road fatalities.
Unlike traditional machine learning methods that have conventionally been applied to …

In complex urban traffic scenarios, autonomous vehicles face significant challenges in
adapting to diverse and dynamic traffic conditions. Reward-based reinforcement learning …