  Connected and Automated Vehicles (CAVs) 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.
Therefore, 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) 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 onramp scenario extraction, enabling scenario
categorization and assessment. An Hidden Markov Model (HMM) and Dynamic Time
Warping (DTW) is utilized for extraction and a decision tree with the Surrogate
Measure of Safety (SMoS) Post Enroachment Time (PET) for categorization and
assessment. The efficacy of the approach is shown with a dataset of NTD
collected on the TFNDS.

Machine Learning;Artificial Intelligence;Robotics