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 …

Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the
automotive domain sector, offering promising solutions to address challenges such as traffic …

Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine
learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML …

Federated Learning (FL) is a promising technique to enhance the safety and efficiency of
intelligent transportation systems. While FL has been extensively studied, the …

Recent studies have demonstrated the potentials of federated learning (FL) in achieving
cooperative and privacy-preserving data analytics. It would also be promising if FL can be …

The rapid growth of the Internet of Vehicles (IoV) paradigm sparks the generation of large
volumes of distributed data at vehicles, which can be harnessed to build models for …

Supported by some of the major revolutionary technologies, such as Internet of Vehicles
(IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks …

Advancements in Vehicular ad-hoc Network (VANET) technology have led to a growing
network of interconnected devices, including edge devices, resulting in substantial data …

Machine learning (ML) has recently been adopted in vehicular networks for applications
such as autonomous driving, road safety prediction and vehicular object detection, due to its …

In the current era of the Internet of Vehicles (IoV), vehicle to vehicle data sharing can provide
customized applications for Connected and Autonomous Vehicles (CAVs). The …