Human driven vehicles (HDVs) with selfish objectives cause low traffic efficiency in an un-
signalized intersection. On the other hand, autonomous vehicles can overcome this …

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 …

In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …

This paper proposes a cooperative strategy of connected and automated vehicles (CAVs)
longitudinal control for a mixed connected and automated traffic environment based on deep …

Taking advantage of both vehicle-to-everything (V2X) communication and automated driving
technology, connected and automated vehicles are quickly becoming one of the …

In recent years, the growing development of Connected Autonomous Vehicles (CAV),
Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent …

Autonomous Vehicles (AVs) promise to disrupt the traditional systems of transportation. An
autonomous driving environment requires an uninterrupted, continuous stream of data and …

Eco-Driving has great potential in reducing the fuel consumption of road vehicles, especially
under the connected and automated vehicles (CAVs) environment. Traditional model-based …

Road congestion induces significant costs across the world, and road network disturbances,
such as traffic accidents, can cause highly congested traffic patterns. If a planner had control …

Connected and Autonomous Vehicles (CAVs) are expected to bring major transformations to
transport efficiency and safety. Studies show a range of possible impacts, from worse …