  Currently, most existing approaches for the design of Automated Driving
System (ADS) scenarios focus on the description at one particular abstraction
level typically the most detailed one. This practice often removes information
at higher levels, such that this data has to be re-synthesized if needed. As
the abstraction granularity should be adapted to the task at hand, however,
engineers currently have the choice between re-calculating the needed data or
operating on the wrong level of abstraction. For instance, the search in a
scenario database for a driving scenario with a map of a given road-shape
should abstract over the lane markings, adjacent vegetation, or weather
situation. Often though, the general road shape has to be synthesized (e.g.
interpolated) from the precise GPS information of road boundaries. This paper
outlines our vision for multi-level ADS scenario models that facilitate
scenario search, generation, and design. Our concept is based on the common
modelling philosophy to interact with scenarios at the most appropriate
abstraction level. We identify different abstraction levels of ADS scenarios
and suggest a template abstraction hierarchy. Our vision enables seamless
traversal to such a most suitable granularity level for any given scenario,
search and modelling task. We envision that this approach to ADS scenario
modelling will have a lasting impact on the way we store, search, design, and
generate ADS scenarios, allowing for a more strategic verification of
autonomous vehicles in the long run.
