Introducing Intelligence in Vehicular Ad Hoc Networks Using Machine Learning Algorithms
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Abstract
The automotive industry has gained popularity in the past decade, and it has led to tremendous advancements in intelligent vehicular networks. The increase in the number of vehicles on the roads makes it essential for vehicles to act intelligently as humans do. The concept of machine learning is that when vehicles learn and improve to operate in accordance with the previously processed data. The machine learning techniques have helped the automotive industry in developing the driverless car [1-2]. With the help of sensors and cameras, it is quite possible to use the machine learning algorithms and provide the user with the benefits associated with it. It helps to allow the vehicle to perform certain tasks which actually can replace the driver of the vehicle. The Artificial Intelligence (AI) chips integrated into the vehicles enable the vehicle to navigate roads [3-4]. This paper provides an insight into the machine learning algorithms widely used by the automotive industries, and a comparison is made between them with respect to the applications of the Vehicular Ad Hoc Network (VANET).
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Machine learning, telematics, artificial intelligence, heterogeneous networks, vehicular-ad-hoc networks.
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