Review Article: Problems and the Approaches of Machine Learning in Vehicle Ad Hoc Networks

In recent years, there has been a notable surge in research interest in vehicular ad-hoc networks (VANETs) due to advancements in wireless communication technology and the vehicle sector. Vehicles to vehicles (V2V) and vehicles to infrastructure comprise a vehicular network. The potential machine learning (ML) method can offer practical solutions for various application fields. Machine learning is a technique where a system uses data that has already been processed to learn from and improve itself automatically. Vehicular networks are a significant application domain where ML-based techniques are highly helpful in solving various issues. Vehicular nodes and infrastructure communicating wirelessly are susceptible to many kinds of assaults. Intelligent transportation systems (ITS) rely heavily on vehicle ad hoc networks (VANETs). These methods enable effective supervised and unsupervised learning of the acquired data, hence accomplishing the goal of VANETs. Because of identifying security concerns in-vehicle networks from source to destination, this evaluation attempts to apply it. We outlined the problems with traffic, safety, and communication in VANET systems, discussed whether or not they could be implemented, and investigated the potential solutions provided by machine learning techniques.