Recent advancements in vehicle-to-everything (V2X) communication have notably improved
existing transport systems by enabling increased connectivity and driving autonomy levels …

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with …

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …

Vehicle localization is essential for intelligent and autonomous vehicles. To improve the
accuracy of vehicle stand-alone localization in highly dynamic driving conditions during …

Next-generation autonomous and networked industrial systems (ie, robots, vehicles, drones)
have driven advances in ultra-reliable low-laten-cy communications (URLLC) and …

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

The widespread adoption of the fifth generation (5G) of cellular networks has brought new
opportunities for localization-based services. High-precision positioning use cases and …

This research examines the feasibility of using synchronization signals broadcasted by
currently deployed fifth generation (5G) cellular networks to determine the position of a static …

The evolution of Advanced Driver Assistance Systems (ADAS) towards the ultimate goal of
autonomous driving relies on a conspicuous number of sensors, to perform a wide range of …

This paper considers the problem of cooperative lidar sensing in vehicular networks. We
focus on the task of associating the vehicle-generated measurements by lidars to enable a …