2021/04/12 by Lars Klitzke, Kay Gimm, Klitzke, Lars +5 · 1 citation
Computer Science · Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #I.2.8 #I.5.4 #I.6.5 #Machine Learning (cs.LG) #Robotics (cs.RO) #Transportation Planning and Optimization #Vehicle emissions and performance #cs.AI #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.2104.05661
7 pages
openalex publication_date 2021/04/12 · arxiv created 2022/03/03 · arxiv updated 2022/03/04 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.