SceML - A Graphical Modeling Framework for Scenario-based Testing of Autonomous Vehicles
This work addresses the challenge of ensuring functional correctness and safety for autonomous vehicles by providing a more efficient way to create and manage test scenarios, primarily benefiting automotive engineers and testers.
The paper proposes SceML, a graphical modeling framework for scenario-based testing of autonomous vehicles. It allows experts to create and review scenarios, reducing the need for extensive physical test drives by replacing them with simulations.
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.