To operate intelligent vehicular applications such as automated driving, mechanisms including machine learning (ML), artificial intelligence (AI), and others are used to abstract knowledge from information. Knowledge is defined as a state of understanding obtained through experience and analysis of collected information, and it is promising for vehicular applications. However, to achieve its full potential, it requires a unified framework that is cooperatively created and shared. This article investigates the meaning and scope of knowledge as applied to vehicular networks and defines a structure for vehicular knowledge description, storage, and sharing. Through the example of passenger-comfort-based automated driving, we expose the potential benefits of such knowledge structuring for network load and delay.

中文翻译：

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车辆知识网络的定义和框架：以知识为中心的网络的应用

为了操作诸如自动驾驶之类的智能车辆应用，包括机器学习（ML），人工智能（AI）等机制可用于从信息中提取知识。知识被定义为通过经验和对收集到的信息进行分析而获得的理解状态，它对车辆应用很有前途。但是，为了发挥其全部潜力，它需要协作创建和共享的统一框架。本文研究了应用于车辆网络的知识的含义和范围，并定义了用于车辆知识描述，存储和共享的结构。通过基于乘客舒适度的自动驾驶示例，我们揭示了这种知识结构对于网络负载和延迟的潜在好处。