In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …

Connected and automated vehicles (CAVs) require multiple tasks in their seamless
maneuverings. Some essential tasks that require simultaneous management and actions …

The application of multisource information fusion in real-world scenarios is an emerging
practice because it effectively uses consistent and complementary data to optimize decision …

Multi-source information fusion has attracted considerable attention in the few past years
and plays a great role in real applications. However, the uncertainty or conflict will make the …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

In this study, a Human-Lead-Platoon CACC ((HLP-CACC) controller is proposed for
connected and automated vehicles to “include” human drivers in platooning process. The …

Purpose-This study aims to propose an enhanced eco-driving strategy based on
reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the …

In high-speed railways, the pantograph-catenary system (PCS) is a critical subsystem of the
train power supply system. In particular, when the double-PCS (DPCS) is in operation, the …

This paper presents an adaptive leading cruise control strategy for the automated vehicle
(AV) and first considers its impact on the following human-driven vehicle (HDV) with diverse …

Most of the current variable speed limit (VSL) strategies are designed to alleviate congestion
in relatively short freeway segments with a single bottleneck. However, in reality …