As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning (ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dy-namic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover man-agement and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.

Kang Tan;Duncan Bremner;Julien Le Kernec;Lei Zhang;Muhammad Imran

James Watt School of Engineering University of Glasgow,Glasgow,G128QQ,UK

数字通信与网络（英文）

[1]Kang Tan;Duncan Bremner;Julien Le Kernec;Lei Zhang;Muhammad Imran-.Machine learning in vehicular networking:An overview)[J].数字通信与网络（英文）,2022(01):18-24

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