Unsupervised Lane-Change Identification for On-Ramp Merge Analysis in Naturalistic Driving Data

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.