Research on smart connected vehicles has recently targeted the integration of vehicle-to-
everything (V2X) networks with Machine Learning (ML) tools and distributed decision …

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

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 …

Object detection with on-board sensors (eg, lidar, radar, and camera) is crucial to
autonomous driving (AD), and these sensors complement each other in modalities. While …

Deep learning is a key approach for the environment perception function of Cooperative
Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic …

Realizing human-like perception is a challenge in open driving scenarios due to corner
cases and visual occlusions. To gather knowledge of rare and occluded instances …

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 …

Intelligent transportation systems (ITSs) have been fueled by the rapid development of
communication technologies, sensor technologies, and the Internet of Things (IoT) …

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 vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-
dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to …