@inproceedings{4f49deb3589d4071aa302274a5601283,
title = "Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model",
abstract = "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 1 and IPG CarMaker text files 2. 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.",
keywords = "Automated Driving Systems, Large Language Model, Scenario Extraction, Virtual Testing",
author = "Yongqi Zhao and Wenbo Xiao and Tomislav Mihalj and Jia Hu and Arno Eichberger",
year = "2024",
month = jul,
day = "15",
doi = "10.1109/IV55156.2024.10588843",
language = "English",
series = "IEEE Intelligent Vehicles Symposium, Proceedings",
publisher = "IEEE Xplore",
pages = "559--566",
booktitle = "35th IEEE Intelligent Vehicles Symposium, IV 2024",
}