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Automation in transport has already gained momentum across the world and there are plethora of pilot schemes for managing urban traffic, mixed with semi and fully automated vehicles. We expect that modern software solutions for managing situations in modern traffic would react to existing computational algorithms for traffic decision making, and tune them according to the semantic of situations we encounter or contextualize in traffic. A combination of software artifacts/computations, data repositories, automated and manual vehicles, human intervention, cognition and communication links enable an ad-hoc existence of various instances of Internet-of-Everything (IoE), in which we have to make constant decisions in order to participate in or manage urban traffic. In this paper we propose a framework for creating an instance of IoE, using semantics stored in such an environment and reasoning upon the best possible instance of IoE suitable for vehicle(s). The framework uses reasoning upon SWRL enabled OWL ontologies and learning technologies in order to a) understand and manipulate the semantics of the IoE and b) assist in driving decisions for any type of vehicle, which happens to be within that IoE instance. We illustrate the framework by creating a software architectural proposal from it and explain mechanisms, which would create a software application for managing modern traffic. Instances of IoE would follow and accommodate various changes we experience in traffic today.
Juric et al. (Tue,) studied this question.