With the fast expanding scale of vehicular networks, vehicular edge computing (VEC) has
emerged and attracted growing attention from both industry and academia. Parked vehicles …

Vehicular edge computing (VEC) extends edge computing to vehicular networks by
exploiting computation resources of vehicles to offload tasks from other vehicles and …

The drastically increasing volume and the growing trend on the types of data have brought
in the possibility of realizing advanced applications such as enhanced driving safety, and …

Smart vehicles are expected to be equipped with high-dimensional, resource-intensive
applications, including platoon control, augmented reality supported gaming, AI-based …

Vehicular Edge Computing (VEC) has emerged as a promising paradigm to enable low-
latency Vehicle-to-Everything (V2X) services by bringing computing resources closer to …

Autonomous vehicles (AV) utilize various machine learning (ML) models for performing
tasks such as pedestrian detection, charging station prediction, routing path prediction …

The conventional architecture of vehicular ad hoc networks (VANETs) with a centralized
approach has difficulty overcoming the increasing complexity of intelligent transportation …

As a novel computing paradigm, multiaccess edge computing (MEC) migrates computing
and storage capabilities to edge nodes of the network to meet the requirements of executing …

As an effective method to increase the computing capability of vehicles, vehicle-to-vehicle
(V2V) assisted vehicular edge computing (VEC) has great potential to reduce infrastructure …

Vehicle-to-everything (V2X) communications over cellular networks have a great potential
for enabling intelligent transportation systems (ITSs), and supporting advanced services …