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 …

The development of Intelligent Transportation System (ITS) has brought about
comprehensive urban traffic information that not only provides convenience to urban …

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

Bird's eye view (BEV) perception is becoming increasingly important in the field of
autonomous driving. It uses multi-view camera data to learn a transformer model that directly …

Modern cars are equipped with autonomous systems to assist the driver and improve driving
experience. Driving assist system (DAS) is one of the most significant components of a self …

Abstract In Internet of Things (IoT) applications, federated learning is commonly used for
distributedly training models in a privacy-preserving manner. Recently, federated learning is …

Coordination among vehicular actors becomes increasingly important at the dawn of
autonomous driving. With communication serving as the basis for this process, latency …

The explosive growth of the interconnected vehicle network creates vast amounts of data
within individual vehicles, offering exciting opportunities to develop advanced applications …

Most of the machine learning and artificial intelligence applications are data driven. When it
comes to sensitive data, maintaining the data privacy principles is a big challenge. Building …

The traditional machine learning algorithms focus on centralised data repository where the
aggregate data used for training is stored in a common location and processed. This …