Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve
traffic safety. With the development of communication and automation technologies, regular …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

Deep learning has been widely used in intelligent vehicle driving perception systems, such
as 3D object detection. One promising technique is Cooperative Perception, which …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

This paper studies the distributed dynamic event-triggered model predictive control (MPC) of
vehicle platoon systems subject to denial-of-service (DoS) attacks and external …

With features of collaborative interaction and autonomous decision-making, connected and
autonomous vehicles (CAVs) offer a viable solution for a sustainable and efficient future of …

In the initial period of automobile development, the vast majority of researchers would never
have guessed network communications would be applied to vehicles one day. After the …

Automated driving is a promising tool for reducing traffic accidents. While some companies
claim that many cutting-edge automated driving functions have been developed, how to …

The emerging connected automated vehicle (CAV) technique brings a new opportunity for
the development of cooperative collision avoidance systems. This paper proposes a …