Connected and autonomous vehicles offer the possibility to carry out control strategies, thus
having great potential to improve traffic efficiency and road safety. The efficient passage of …

The development of autonomous vehicles has shown great potential to enhance the
efficiency and safety of transportation systems. However, the decision-making issue in …

Multi-agent reinforcement learning (MARL) methods have emerged as a promising solution
for multi-agent collaborative driving in the intersection and roundabout scenarios. However …

Achieving safe lane changing is a crucial function of autonomous vehicles, with the
complexity and uncertainty of interaction involved. Learning-based approaches and vehicle …

The recent proliferation of the research on multi-agent deep reinforcement learning (MDRL)
offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) …

With the advent of machine learning, several autonomous driving tasks have become easier
to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic …

Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …

Using wireless communication and sensor detection technologies, the cooperative vehicle
infrastructure system (CVIS) can acquire a wealth of vehicle and road information to provide …

Abstract The application of Deep Reinforcement Learning (DRL) has significantly impacted
the development of autonomous driving technology in the field of intelligent transportation …

Connected and automated vehicles (CAVs) have emerged as a potential solution to the
future challenges of developing safe, efficient, and eco-friendly transportation systems …