VANETs enable information exchange among vehicles, other end devices and public networks, which plays a key role in road safety/infotainment, intelligent transportation systems, and self-driving systems. As vehicular connectivity soars, and new on-road mobile applications and technologies emerge, VANETs are generating an ever-increasing amount of data, requiring fast and reliable transmissions through VANETs. On the other hand, a variety of VANETs related data can be analyzed and utilized to improve the performance of VANETs. In this article, we first review VANETs technologies to efficiently and reliably transmit big data. Then, the methods employing big data for studying VANETs characteristics and improving VANETs performance are discussed. Furthermore, we present a case study where machine learning schemes are applied to analyze VANETs measurement data for efficiently detecting negative communication conditions.

中文翻译：

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大数据驱动的车载网络


VANET 能够实现车辆、其他终端设备和公共网络之间的信息交换，这在道路安全/信息娱乐、智能交通系统和自动驾驶系统中发挥着关键作用。随着车辆连接性的飙升以及新的道路移动应用程序和技术的出现，车载自组网正在生成越来越多的数据，需要通过车载自组网进行快速、可靠的传输。另一方面，可以分析和利用各种VANET相关数据来提高VANET的性能。在本文中，我们首先回顾了 VANET 技术，以高效可靠地传输大数据。然后，讨论了利用大数据研究VANET特性和提高VANET性能的方法。此外，我们还提出了一个案例研究，其中应用机器学习方案来分析 VANET 测量数据，以有效检测负面通信条件。