  Ensuring the functional correctness and safety of autonomous vehicles is a
major challenge for the automotive industry. However, exhaustive physical test
drives are not feasible, as billions of driven kilometers would be required to
obtain reliable results. Scenariobased testing is an approach to tackle this
problem and reduce necessary test drives by replacing driven kilometers with
simulations of relevant or interesting scenarios. These scenarios can be
generated or extracted from recorded data with machine learning algorithms or
created by experts. In this paper, we propose a novel graphical scenario
modeling language. The graphical framework allows experts to create new
scenarios or review ones designed by other experts or generated by machine
learning algorithms. The scenario description is modeled as a graph and based
on behavior trees. It supports different abstraction levels of scenario
description during software and test development. Additionally, the graphbased
structure provides modularity and reusable sub-scenarios, an important use case
in scenario modeling. A graphical visualization of the scenario enhances
comprehensibility for different users. The presented approach eases the
scenario creation process and increases the usage of scenarios within
development and testing processes.
