… In Section IV, we describe the applications of machine learning in vehicular network for …
Then, we investigate the machine learning based approaches for intelligent vehicular networking …

… Among them, machine learning (ML)-based methods are the … Several vehicular network
applications will be able to … three times higher) in a sparse vehicular network (low vehicle density)…

… in a vehicular network. 3GPP is a de-facto standard for LTE-V2V communication standard …
vehicular networks that may provide optimization of resource allocation in a vehicular network, …

… The limitations of this research are that other machine learning techniques may provide …
extracted the vehicle's movement patterns and fed them into an Artificial Neural Network (ANN) …

… in vehicular networks: blockchain and machine learning. We will … mechanisms can be
leveraged to secure vehicular networks. … machine learning and blockchain in vehicular networks …

… a shared machine learning model while protecting the individual data-sets. This article
investigates a new type of vehicular network concept, namely a Federated Vehicular Network (…

… Machine learning mechanisms are characterized by the time change and are critical … -vehicle
network scenarios. This paper aims to provide theoretical foundations for machine learning …

… integrates both federated learning and blockchain to ensure both data privacy and network
security. We present a framework to decentralize the mutual machine learning models on end…

… We generate datasets through extensive simulation based on the realistic vehicular network
environment. Moreover, we analyze generated datasets using various machine learning …

… machine learning for autonomous driving and vehicular … paper, we explain how vehicle-to-vehicle
(V2V) and vehicle-to-… learning to train ML algorithms within a vehicular network. …