In recent years, multi-agent reinforcement learning techniques have been widely used and
evolved in the field of artificial intelligence. However, traditional reinforcement learning …

Vehicle Group Intelligence (VGI) represents a significant research domain that contributes to
the advancement of intelligent transportation systems. This study employs bibliometric …

User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …

In a mixed traffic environment, the connected vehicle platoon cannot communicate and
collaborate with the surrounding vehicles. In this case, there is a high risk of collision in large …

Anticipating the forthcoming integration of shared autonomous vehicles (SAVs) into urban
networks, the imperative of devising an efficient real-time scheduling and routing strategy for …

Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and
human-driven vehicles (HVs) at a signalized intersection has become a concern of …

Intersections are essential road infrastructures for traffic in modern metropolises. However,
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …

Cooperation at unsignalized intersections in mixed traffic environments, where Connected
and Autonomous Vehicles (CAVs) and Manually Driving Vehicles (MVs) coexist, holds …

A critical challenge that future autonomous driving systems face is improving the ability to
cope with complex real-world interaction scenarios such as uncontrolled intersections. In the …

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority
control method based on deep reinforcement learning was constructed in the bus network …