We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
翻译:我们正处在一个新时代的末端,即连接的自治车辆拥有前所未有的用户经验,道路安全和空气质量得到极大改善,交通环境和使用案例多种多样,以及大量先进的应用。实现这一宏伟的愿景需要大大加强车辆到一切(V2X)的通信网络,这种网络应当非常智能,能够同时支持超快、超可靠和低相对的大规模信息交流。预计第六代(6G)通信系统将满足下一代V2X的这些要求。在本篇文章中,我们从一系列领域,例如新材料、算法和系统结构,概述一系列关键的赋能技术。我们的目标是建立真正智能的运输系统,我们设想机器学习将为先进的车辆通信和网络发挥推动作用。为此,我们概述了6G电视网络中机器学习的最新进展。为了刺激今后在这一领域的研究,我们讨论了这些技术的实力、公开挑战、成熟程度和加强领域。