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.

翻译：大语言模型（LLM）的出现为验证自动驾驶系统（ADS）提供了新的思路。本文介绍了一种从自然驾驶数据集中提取场景的新方法。提出了名为Chat2Scenario的框架，该框架利用LLM的先进自然语言处理（NLP）能力来理解和识别不同的驾驶场景。通过输入驾驶条件的描述性文本并指定关键性度量阈值，该框架能够高效搜索所需场景，并将其转换为ASAM OpenSCENARIO和IPG CarMaker文本文件。此方法简化了场景提取流程并提高了效率。通过仿真实验验证了该方法的有效性。该框架基于用户友好的Web应用程序呈现，可通过以下链接访问：https://github.com/ftgTUGraz/Chat2Scenario。