Machine Learning Technologies in Internet of Vehicles

Abstract

Recently, there was much interest in Technology which has emerged greatly to the development of smart cars. Internet of Vehicle (IoV) enables vehicles to communicate with public networks and interact with surrounding environment. It also enables vehicles to exchange information in addition to collect information about other vehicles and roads. However, actual applications of smart IoV systems face many challenges. These challenges are related to different problematic issues like big data connection with IoV, cloud network, data processing, and efficient communication between a large amount of different vehicles types, in addition to optimum decision data processing on or off board. Intelligence of the huge amount of data that can be processed to reduce road congestion and improve traffic management, as well as ensuring road safety is an important issue in future IoV trends.Artificial Intelligence (AI) technology with Machine Learning (ML) mechanisms offers smart solutions that can improve IoV network efficiency. For example, decision for data processing at various layers i.e. on-board units (OBUs), Fog level or cloud level are one of the problems which need ML algorithms. Other critical issues that can be resolved by ML mechanisms are time, energy, rapid topology of IoV, optimization quality of experience (QoE) and channel modeling. These issues need to be optimized. This chapter provides theoretical fundamentals for ML models, algorithms in IoV applications and future directions.