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Ensuring the functional correctness and safety of autonomous vehicles is amajor challenge for the automotive industry. However, exhaustive physical testdrives are not feasible, as billions of driven kilometers would be required toobtain reliable results. Scenariobased testing is an approach to tackle thisproblem and reduce necessary test drives by replacing driven kilometers withsimulations of relevant or interesting scenarios. These scenarios can begenerated or extracted from recorded data with machine learning algorithms orcreated by experts. In this paper, we propose a novel graphical scenariomodeling language. The graphical framework allows experts to create newscenarios or review ones designed by other experts or generated by machinelearning algorithms. The scenario description is modeled as a graph and basedon behavior trees. It supports different abstraction levels of scenariodescription during software and test development. Additionally, the graphbasedstructure provides modularity and reusable sub-scenarios, an important use casein scenario modeling. A graphical visualization of the scenario enhancescomprehensibility for different users. The presented approach eases thescenario creation process and increases the usage of scenarios withindevelopment and testing processes.

SceML - A Graphical Modeling Framework for Scenario-based Testing of Autonomous Vehicles