5 accepted papers
This paper focuses on highly precise localization and tracking of vehicles in race circuits, where centimeter-level accuracy is required for safety and for enabling complex maneuvering. Recently, the Annealed Stein Particle Filter (ASPF) has been proposed as a promising Bayesian tracking tool for tr…
Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training. In Bayesian FL, nodes exchange information about local posterior distributions over the model parameters space.…
This paper considers the problem of cooperative localization of passive objects in a vehicular environment through the fusion of lidar point clouds collected at different moving vehicles and sent to the road infrastructure. Object localization is then used to improve the position estimate of vehicle…
Federated learning (FL) is emerging as a new paradigm for training a machine learning model in cooperative networks. The model parameters are optimized collectively by large populations of interconnected devices, acting as cooperative learners that exchange local model updates with the server, rathe…
This paper addresses the problem of multitarget tracking using a network of mobile sensors with unknown positions. In contrast to commonly-used approaches which split the sensor localization and target tracking into two different sub-problems, we propose a holistic approach for joint localization an…