Navigating through intersections is one of the main challenging tasks for an autonomous
vehicle. However, for the majority of intersections regulated by traffic lights, the problem …

In recent years, the viability of employing multi-agent reinforcement learning technology for
adaptive traffic signal control has been extensively validated. However, owing to restricted …

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

Autonomous Intersection Management (AIM) systems present a new paradigm for conflict-
free cooperation of connected autonomous vehicles (CAVs) at road intersections, the aim of …

In this paper, we explore the challenges associated with navigating complex T-intersections
in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms …

Learning an efficient and safe driving strategy in a traffic-heavy intersection scenario and
generalizing it to different intersections remains a challenging task for autonomous driving …

Arterial streets serve as the principal undertaker for urban mobility in a typical urban road
network. In this paper, we propose a novel decentralized reinforcement learning method for …

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 …

Advanced deep reinforcement learning shows promise as an approach to addressing
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how
to navigate them safely and efficiently. This paper proposes a reinforcement learning (RL) …