The development of assessment methods for the performance of Automated Vehicles (AVs) is essential to enable and speed up the deployment of automated driving technologies, due to the complex operational domain of AVs. As traditional methods for assessing vehicles are not applicable for AVs, other approaches have been proposed. Among these, real-world scenario-based assessment is widely supported by many players in the automotive field. In this approach, test cases are derived from real-world scenarios that are obtained from driving data. To minimize any ambiguity regarding these test cases and scenarios, a clear definition of the notion of scenario is required. In this paper, we propose a more concrete definition of scenario, compared to what is known to the authors from the literature. This is achieved by proposing an ontology in which the quantitative building blocks of a scenario are defined. An example illustrates that the presented ontology is applicable for scenario-based assessment of AVs.

An initial description ought to be as compact as possible, and its elements should correspond as closely as possible to meaningful objects or events in the signal-forming process. Frequently, local extrema in the signal and its derivatives-- and intervals bounded by extrema--are particularly appropriate descriptive primitives: although local and closely tied to the signal data, these events often have direct semantic interpretations, e.g. as edges in images. A description that characterizes a signal by its extrema and those of its first few derivatives is a qualitative description of exactly the kind we were taught to use in elementary calculus to "sketch" a function. A great deal of effort has been expended to obtain this kind of primitive qualitative description (for overviews of this literature, see [1,2,3].) and the problem has proved extremely difficult. The problem of scale has emerged consistently as a fundamental source of difficulty, because the events we perceive and find meaningful vary enormously in size and extent. The problem is not so much to eliminate fine-scale noise, as to separate events at different scales arising from distinct physical processes.[4]