As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

中文翻译：

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面向 6G 的未来智能安全车载网络：机器学习方法

作为一个强大的工具，车辆网络已经建立起来，以连接世界各地的人类通信和交通未来许多年。然而，随着车辆的快速增长，车载网络变得异构、动态、大规模，难以满足下一代超低时延、高可靠、高安全、海量连接等严格要求。 (6G) 网络。最近，机器学习 (ML) 已成为一种强大的人工智能 (AI) 技术，可以使车辆和无线通信变得高效且适应性强。自然，将ML应用于车载通信和网络成为一个热门话题，并在学术界和工业界得到广泛研究，为未来6G车载网络的智能化铺平了道路。在本文中，