Toolbox for Test Planning and Test Realization of Scenario-Based Field Tests for Automated and Connected Driving

Current test and validation procedures for automated and connected driving functions use field tests on public roads primarily to identify unknown critical scenarios. These scenarios are then validated or varied within simulations or on closed proving grounds. In order to validate complex urban driving scenarios, this approach may need to be supplemented by the method of scenario-based field tests in real traffic environments. Scenario-based tests constitutes the methodical basis for an efficient and purposive test of automated and connected driving functions in public testbeds. This paper focuses on concepts for the design, planning and implementation of scenario-based tests of automated and connected driving functions under real traffic conditions. The developed toolbox will be explained using the example of the “Digital Testbed Dresden / Saxony" as part of the initiative “Synchrone Mobilität 2023”

Finding scenarios that matter: How standardized metadata for scenario-based testing will be key for collecting required data for your simulation environment

Selecting test scenarios which fit the most to required test cases can be challenging, especially if scenario resources originate from external sources. A well-defined, queryable metadata definition about the content of scenarios is essential for a purposive scenario search. Nevertheless, a common and harmonized semantic interpretation of the metadata content is equally important. Otherwise, a comparison between the search results over multiple data sources will be ambiguous and may lead to undesirable scenarios. The usage of standards for metadata descriptions helps in this regard, but it will not solve all issues. The syntax of the metadata is clearly defined in the standards, but the semantics often leave room for interpretation, challenging the search for scenarios. This presentation elaborates the usage of standards like ASAM OpenLABEL and SRMD for content-wise metadata, demonstrates the remaining challenges and approaches, and derive key implications for the further development of data ecosystems for simulative testing of AD-function