  Connected and Automated Vehicles (CAVs) are envisioned to transform the
future industrial and private transportation sectors. Due to the complexity of
the systems, 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 for CAVs emphasizing the
requirement of a solid data basis of scenarios. The large-scale research
facility Test Bed Lower Saxony (TFNDS) enables the provision of substantial
information for a database of scenarios on motorways. For that purpose,
however, the scenarios of interest must be identified and categorized in the
collected trajectory data. This work addresses this problem and proposes a
framework for on-ramp scenario identification that also enables for scenario
categorization and assessment. The efficacy of the framework is shown with a
dataset collected on the TFNDS.
