Extraction and Analysis of Highway On-Ramp Merging Scenarios from Naturalistic Trajectory Data
This work addresses the need for scenario databases to verify safety in automated vehicles, but it is incremental as it applies existing techniques to a specific domain.
The paper tackles the problem of identifying and extracting highway on-ramp merging scenarios from naturalistic trajectory data to support safety validation for connected and automated vehicles, proposing a method using Hidden Markov Models and Dynamic Time Warping that is demonstrated on data from the Test Bed Lower Saxony.
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.