The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign with the purpose of enabling a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

概念诱导是基于正式的逻辑推理在描述逻辑上的，已在本体工程中使用，以从基本数据（ABOX）图创建本体（Tbox）公理。在本文中，我们表明它也可以用来解释数据差异，例如在可解释的AI（XAI）的背景下，我们表明它实际上可以以对人类观察者有意义的方式进行。我们的方法利用了从Wikipedia类别层次结构策划的大型层次结构，作为背景知识。