Scenario Extraction

Scenario extraction focuses on automatically identifying and characterizing relevant driving scenarios from large naturalistic datasets to rigorously test automated driving systems (ADS). Current research emphasizes developing robust methods for extracting these scenarios, employing techniques like large language models for textual analysis of driving data and unsupervised machine learning algorithms for clustering similar events, often incorporating data preprocessing and tagging steps to improve accuracy. This work is crucial for improving the safety and reliability of ADS by providing a more comprehensive and efficient approach to testing than traditional methods, ultimately contributing to the development of safer autonomous vehicles.