Open Access

Carsten Koch, Lars Klitzke, Kay Gimm, Frank Köster

• Automated Vehicles are envisioned to transform the future industrial and private transportation sectors. However, due to the system's enormous complexity, functional verification and validation of safety aspects are essential before the technology merges into the public domain. In recent years, a scenario-driven approach has gained acceptance, emphasizing the requirement of a solid data basis of scenarios. The large-scale research facility Test Bed Lower Saxony (TFNDS) of the German Aerospace Center (DLR) enables the provision of ample information for a database of scenarios on highways. For that purpose, however, the scenarios of interest must be identified and extracted from the collected Naturalistic Trajectory Data (NTD). This work addresses this problem and proposes a methodology for on-ramp scenario extraction, enabling scenario categorization and assessment. A Hidden Markov Model and Dynamic Time Warping is utilized for extraction and a decision tree with the Surrogate Measure of Safety Post Encroachment Time for categorization and assessment. The efficacy of the approach is shown with a dataset of NTD collected on the TFNDS.