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 …

Smart cities demand resources for rich immersive sensing, ubiquitous communications,
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …

The virtual world created by the development of the Internet, computers, artificial intelligence
(AI), and hardware technologies have brought various degrees of digital transformation to …

Federated learning (FL), as a distributed machine learning technology, allows large-scale
nodes to utilize local datasets for model training and sharing without revealing privacy …

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

Accurate perception is essential for advancing autonomous driving and addressing safety
challenges in modern transportation systems. Despite significant advancements in computer …

The internet of things (IoT) represents a disruptive concept that has been changing society in
several ways. There have been several successful applications of IoT in the industry. For …

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent
Transportation Systems, notably in urban environments. An ITS leverages advancements in …

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

Federated learning (FL), as a safe distributed training mode, provides strong support for the
edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and …