RdbS
The relationship between real-world testing and simulation-based evaluation is becoming increasingly important in assessing vehicle functions, as many testing methodologies aim to shift more tests into simulation environments.
Real-Data-Based Scenario Generation (RdbS)
The real-data-based scenario generation toolchain (RdbS) creates logical scenarios with associated parameter distributions in the OpenSCENARIO® format, using real trajectory data. These scenarios offer a valuable extension to TSC.
By using RdbS, users can generate realistic traffic scenarios, enabling more accurate simulations in virtual environments. This improves the validation of automated driving functions in simulations before they are tested on the road. The toolchain is being used in direct collaborative industry work with OEMs and Tier 1 suppliers for data generation.
Read More
Schicktanz, Clemens and Gimm, Kay (2025) Detection and Analysis of Critical Interactions in Illegal U-Turns at an Urban Signalized Intersection. Data Science for Transportation. Springer Nature. doi: 10.1007/s42421-025-00117-5. ISSN 2948-1368.
Schicktanz, Clemens and Gimm, Kay (2024) Detection and analysis of corner case scenarios at a signalized urban intersection. Accident Analysis and Prevention. Elsevier. doi: 10.1016/j.aap.2024.107838. ISSN 0001-4575.
Schicktanz, Clemens and Klitzke, Lars and Gimm, Kay and Knake-Langhorst, Sascha and Rizzo, Giancarlo and Mosebach, Henning Hajo and Liesner, Karsten (2025) DLR Urban Traffic dataset (DLR-UT) (Version 1.2.0).