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.