From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment
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Abstract (arXiv)
Scenario-based development and test processes are a promising approach for verifying and validating automated driving functions. For this purpose, scenarios have to be generated during the development process in a traceable manner. In early development stages, the operating scenarios of the item to be developed are usually described in an abstract, linguistic way.Within the scope of a simulation-assisted test process, these linguistically described scenarios have to be transformed into a state space representation and converted into data formats which can be used with the respective simulation environment. Currently, this step of detailing scenarios takes a considerable manual effort. Furthermore, a standardized interpretation of the linguistically described scenarios and a consistent transformation into the data formats are not guaranteed due to multiple authors as well as many constraints between the scenario parameters. In this paper, the authors present an approach to automatically detail a keyword-based scenario description for execution in a simulation environment and provide a basis for test case generation. As a first step, the keyword-based description is transformed into a parameter space representation. At the same time, constraints regarding the selection and combination of parameter values are documented for the following process steps (e. g. evolutionary or stochastic test methods). As a second step, the parameter space representation is converted into data formats required by the simulation environment. As an example, the authors use scenarios on German freeways and convert them into the data formats OpenDRIVE (description of the road) and OpenSCENARIO (description of traffic participants and environmental conditions) for execution in the simulation environment Virtual Test Drive.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 May 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 53
- 2022Using Ontologies for the Formalization and Recognition of Criticality for Automated Drivingcited by 12
- 2021A Review of Testing Object-Based Environment Perception for Safe Automated Drivingcited by 9
- 2025Multi-Agent Scenario Generation in Roundabouts with a Transformer-enhanced Conditional Variational Autoencodercited by 0
- 2026From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Drivingcited by 0
- 2026From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Driving9 Aug 2026
- 2025Multi-Agent Scenario Generation in Roundabouts with a Transformer-enhanced Conditional Variational Autoencoder28 Oct 2025
- 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review8 Feb 2024
- 2022Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving3 May 2022
- 2021A Review of Testing Object-Based Environment Perception for Safe Automated Driving16 Feb 2021