Artificial Intelligence and Machine Learning as key enablers for V2X communications: A comprehensive survey

The automotive industry is undergoing a profound digital transformation to create autonomous vehicles. Vehicle-to-Everything (V2X) communications enable the provisioning of transportation use cases for road traffic and safety management. At the same time, during the past decade, Artificial Intelligence (AI) and Machine Learning (ML) have been in the spotlight because of their outstanding performance in various domains, including natural language processing, and computer vision. Considering also current standardization efforts, towards incorporating AI and ML as integral sub-systems of beyond 5G and 6G networks, these technologies are considered very promising to optimize user, control, and management network functions, but also to support road safety and even entertainment applications. This survey systematically reviews existing research at the intersection of AI/ML and V2X communications, focusing on handover management, proactive caching, physical and computation resources allocation, beam selection optimization, packet routing, and QoS prediction in vehicular environments. The authors extract the underlying AI/ML techniques, the training features, their architecture and discuss several aspects regarding the intricacies of vehicular environments and ML. These aspects include time complexity of the algorithms, quality of real-world vehicle traces, suitability of AI/ML techniques in relevance to the designated network operation and the underlying automotive use case, as well as velocity and positioning accuracy requirements towards the creation of more realistic and representative synthetic data.

• Record URL:
  https://doi.org/10.1016/j.vehcom.2022.100569
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2214209622001164
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22142096
• Supplemental Notes:
  • © 2022 Elsevier Inc. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Christopoulou, Maria
  • 0000-0001-7062-6146
  • Barmpounakis, Sokratis
  • 0000-0002-5326-2237
  • Koumaras, Harilaos
  • 0000-0003-0486-2700
  • Kaloxylos, Alexandros
  • 0000-0003-2182-5290
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 100569
• Serial:
  • Vehicular Communications
  • Volume: 39
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2214-2096
  • Serial URL: http://www.sciencedirect.com/science/journal/22142096/1/2

Subject/Index Terms

• TRT Terms: Artificial intelligence; Computer network protocols; Vehicle to infrastructure communications; Vehicle to vehicle communications; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01870796
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 24 2023 9:29AM