Machine learning for next‐generation intelligent transportation systems: A survey

• Published in: Transactions on emerging telecommunications technologies Vol. 33; no. 4
• Main Authors: Yuan, Tingting, Rocha Neto, Wilson, Rothenberg, Christian Esteve, Obraczka, Katia, Barakat, Chadi, Turletti, Thierry
• Format: Journal Article
• Language: English
• Published: Wiley-Blackwell 01.04.2022
• Subjects: Artificial Intelligence; Computer Science; Networking and Internet Architecture; Machine Learning (ML); Autonomous Vehicles; Cooperative Driving; Intelligent Transportation System (ITS)
• ISSN: 2161-3915; 2161-3915
• DOI: 10.1002/ett.4427

Intelligent transportation systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management, and autonomous vehicles, to name a few. ITS are expected to be an integral part of urban planning and future smart cities, contributing to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS pose a variety of challenges due to its scalability and diverse quality‐of‐service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of machine learning (ML), which has recently gained significant traction, to enable ITS. We provide a thorough survey of the current state‐of‐the‐art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can further use and benefit from ML technology. We investigate embedding ML into ITS by categorizing the ML exploitation under perception, prediction, and management tasks of ITS applications. Furthermore, we outline trends of the future of ITS. We expect this survey to provide basic knowledge for beginners and to encourage insights into the future ITS.