To operate intelligent vehicular applications such as automated driving, machine learning, artificial intelligence and other mechanisms are used to abstract from information what is commonly referred to as knowledge. Defined as a state of understanding obtained through experience and analysis of collected information, knowledge is promising for vehicular applications. However, it lacks a unified framework to be cooperatively created and shared to achieve its full potential. This paper investigates on the meaning and scope of knowledge applied to vehicular networks, and suggests a structure for vehicular knowledge description, storage and sharing. Through the example of passenger comfort-based rerouting, it exposes the potential benefits for network load and delay of such knowledge structuring.
@article{arxiv.2005.14505,
title = {A Definition and Framework for Vehicular Knowledge Networking},
author = {Duncan Deveaux and Takamasa Higuchi and Seyhan Uçar and Jérôme Härri and Onur Altintas},
journal= {arXiv preprint arXiv:2005.14505},
year = {2020}
}