Abstract Intelligent Connected Vehicle (ICV), as the product of the deep integration of
automobile, electronics, information, and artificial intelligence in the era of 5G advanced and …

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 …

This paper delves into the transformative role of machine learning (ML) techniques in
revolutionizing the security of electric and flying vehicles (EnFVs). By exploring key domains …

The gradual transition from a traditional transportation system to an intelligent transportation
system (ITS) has paved the way to preserve green environments in metro cities. Moreover …

In vehicular ad-hoc networks (VANET), federated learning enables vehicles to
collaboratively train a global model for intelligent transportation without sharing their local …

Electric vehicles are growing in popularity as a form of transportation, but are still underused
for several reasons, such as their relatively low range and the high costs associated with …

Location privacy is critical in vehicular networks, where drivers' trajectories and personal
information can be exposed, allowing adversaries to launch data and physical attacks that …

Deep learning (DL) has been shown to be very effective for many application domains of
machine learning (ML), including image classification, voice recognition, natural language …

This paper introduces a Machine learning intrusion detection system (IDS) to detect DoS
attacks and FUZZY attacks on CAN bus in smart vehicles and classify messages to Normal …

Location privacy is of utmost importance in vehicular networks, where drivers' trajectories
and personal information can be exposed, posing threats to drivers' safety and personal …