Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once
collected and processed through Machine Learning algorithms, enable AI-based services at …

As vehicle driving evolves from human-controlled to autonomous, human–machine
interaction ensures intuitive usage as well as the feedback from vehicle occupants to the …

When performing learning tasks in pervasive environments, the main challenge arises from
the need of combining federated and continual settings. The former comes from the massive …

This position paper introduces Urgent Edge Computing (UEC) as a paradigm shift
addressing the evolving demands of time-sensitive applications in distributed edge …

Unmanned aerial vehicles (UAVs) have witnessed a dramatic increase in use over the last
20 years, gradually becoming popular tools for a variety of applications, while they …

In the current era of the Internet of Vehicles (IoV), vehicle to vehicle data sharing can provide
customized applications for Connected and Autonomous Vehicles (CAVs). The …

Trust management has become a fundamental requirement for Social Internet of Things
(SIoT) to enable a trustworthy social network of smart objects necessary for enhancing the …

Current automotive safety standards are cautious when it comes to utilizing deep neural
networks in safety-critical scenarios due to concerns regarding robustness to noise, domain …

A distributed decision support system has been developed to assist seafarers during several
navigation tasks, for instance, in avoiding a collision with a detected obstacle in the sea and …

A critical aspect in Federated Learning is the aggregation strategy for the combination of
multiple models, trained on the edge, into a single model that incorporates all the knowledge …