Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

The advancement of artificial intelligence (AI) has truly stimulated the development and
deployment of autonomous vehicles (AVs) in the transportation industry. Fueled by big data …

Suppression of radar-to-radar jammers, especially the mainbeam jammers, has been an
urgent demand in vehicular sensing systems with the expected increased number of …

Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …

This paper presents a novel integrated approach to deal with the decision making and
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …

In this paper, we study joint allocation of the spectrum, computing, and storing resources in a
multi-access edge computing (MEC)-based vehicular network. To support different vehicular …

The current cellular technology and vehicular networks cannot satisfy the mighty strides of
vehicular network demands. Resource management has become a complex and …

Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …

This study aims to model two-dimensional (lateral and longitudinal) motion of an Ego
Vehicle (EV). Intelligent Driver Model (IDM) is enhanced for this purpose. All the surrounding …

A novel framework is proposed for enhancing the driving safety and fuel economy of
autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication …