Collaborative perception is essential to address occlusion and sensor failure issues in
autonomous driving. In recent years, theoretical and experimental investigations of novel …

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 …

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Naturalistic driving action recognition (NDAR) has proven to be an effective method for
detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive …

Most of the current literature focused on centralized learning is centered around the
celebrated average-consensus paradigm and less attention is devoted to scenarios where …

This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

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) is a distributed machine learning (ML) paradigm that enables clients
to collaborate without accessing, infringing upon, or leaking original user data by sharing …