Collaborative perception in autonomous driving: Methods, datasets, and challenges
Collaborative perception is essential to address occlusion and sensor failure issues in
autonomous driving. In recent years, theoretical and experimental investigations of novel …
autonomous driving. In recent years, theoretical and experimental investigations of novel …
Federated learning for connected and automated vehicles: A survey of existing approaches and challenges
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 …
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
A systematic survey of control techniques and applications in connected and automated vehicles
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 …
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …
Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
Towards knowledge-driven autonomous driving
This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …
investigation highlights the limitations of current autonomous driving systems, in particular …
Peer-to-peer federated continual learning for naturalistic driving action recognition
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 …
detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive …
Federated learning in intelligent transportation systems: Recent applications and open problems
Intelligent transportation systems (ITSs) have been fueled by the rapid development of
communication technologies, sensor technologies, and the Internet of Things (IoT) …
communication technologies, sensor technologies, and the Internet of Things (IoT) …
On the convergence of decentralized federated learning under imperfect information sharing
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 …
celebrated average-consensus paradigm and less attention is devoted to scenarios where …
An improved big data analytics architecture using federated learning for IoT-enabled urban intelligent transportation systems
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 …
Transportation Systems, notably in urban environments. An ITS leverages advancements in …
Fedmfs: Federated multimodal fusion learning with selective modality communication
Multimodal federated learning (FL) aims to enrich model training in FL settings where
devices are collecting measurements across multiple modalities (eg, sensors measuring …
devices are collecting measurements across multiple modalities (eg, sensors measuring …