… 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 …

… of a new sensing technique in Vehicle-to-Vehicle networks (V2V) called: Automotive Doppler
… vehicle dynamics as vehicles maneuver relative to each other. When machine learning is …

… concept, we modeled our proposed vehicular network using a network simulation tool, namely
… productive simulation of vehicular-based networks and many other network protocols. We …

… realistic vehicular network simulations using the network simulator 3 by obtaining vehicular
… collected data sets for training the machine learning models using the simulated environment …

… As machine learning is gaining increased attention also in … applications of machine learning
for RA in vehicular networks. … vehicular networks lead by network slicing, 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…

… 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. …

… in vehicular networks and propose using machine learning … vehicular networks. Furthermore,
to describe the feasibility of … of machine learning for SAGIN-assisted vehicular networks. …

… methods enabled by machine learning, in particular deep learning. Methods that combine
the theoretical models derived from domain knowledge and the data-driven capabilities of …

… Employing machine learning into 6G vehicular networks to support … RL with Deep Learning
(DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief …