Abstract
Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and low-latency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driver-less vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed.
Highlights
Machine learning (ML) is an artificial intelligence (AI) technique used to teach a system about the unknown and make efficient and effective decisions
Vehicular ad hoc network (VANET) has emerged as a solution to ameliorating road safety and traffic congestion, supporting infotainment, and improving the quality of experience (QoE) of users
ML has become an integral part of Cognitive radio (CR)-VANETs to ease complexities and enhance network performance
Summary
Machine learning (ML) is an artificial intelligence (AI) technique used to teach a system about the unknown and make efficient and effective decisions. A smart and efficient transportation system can provide smooth traffic flow, reduced road accidents, and a green environment, which in turn improves economic competitiveness. The two main factors that cause spectrum scarcity are as follows: (a) frequency bands are allocated to licensed users based on the traditional fixed spectrum assignment policy and (b) a huge volume of real-time data is generated and transferred over a wireless medium in a dynamic environment. CR-based VANET or CR-VANET is a promising technology to tackle road safety, congestion, and infotainment issues, and it serves as a basic building block for next-generation transportation systems, especially autonomous-driving vehicles. This study focuses on the applications of ML in CR-VANETs to ensure that decision making is fast, highly reliable, secure, and energy-efficient. This study reviews the recent advancements and future directions of ML used in CR-VANETs
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