Giuseppe La Bruna
Edge-assisted Federated Learning for Autonomous Vehicle Trajectory Prediction.
Rel. Carla Fabiana Chiasserini, Claudio Ettore Casetti. Politecnico di Torino, Corso di laurea magistrale in Communications And Computer Networks Engineering (Ingegneria Telematica E Delle Comunicazioni), 2022
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Abstract
The evolution of cars has always been driven by the aim of making transport safer for passengers and pedestrians, but also the need to improve the livability of the cities themselves and therefore the driving comfort of all drivers. This has led to the availability of vehicles equipped with a wide set of sensors for the monitoring of internal and external signals. In the near future, the trend is to inevitably increase the number of data collected from the vehicle itself, necessary to provide services as useful and effective as possible. With the born of applications based on the use of neural networks, there is a huge need of a large amount of data and computational power to make their use profitable.
The most efficient way to train a neural network dedicated to a vehicular application is to use a server located at the edge of the network, because it can exchange information with vehicles through the mobile network, using low-latency communication
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