  The advent of Large Language Models (LLM) provides new insights to validate
Automated Driving Systems (ADS). In the herein-introduced work, a novel
approach to extracting scenarios from naturalistic driving datasets is
presented. A framework called Chat2Scenario is proposed leveraging the advanced
Natural Language Processing (NLP) capabilities of LLM to understand and
identify different driving scenarios. By inputting descriptive texts of driving
conditions and specifying the criticality metric thresholds, the framework
efficiently searches for desired scenarios and converts them into ASAM
OpenSCENARIO and IPG CarMaker text files. This methodology streamlines the
scenario extraction process and enhances efficiency. Simulations are executed
to validate the efficiency of the approach. The framework is presented based on
a user-friendly web app and is accessible via the following link:
https://github.com/ftgTUGraz/Chat2Scenario.
