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：摘要：大型语言模型的出现为验证自动驾驶系统提供了新思路。本文提出了一种从自然驾驶数据集中提取场景的新方法，构建了基于LLM高级自然语言处理能力的Chat2Scenario框架，用于理解与识别不同驾驶场景。该框架通过输入驾驶条件描述文本并定义临界性指标阈值，高效搜索目标场景并将其转换为ASAM OpenSCENARIO和IPG CarMaker文本文件。该方法简化了场景提取流程，显著提升了效率。通过仿真实验验证了方法的有效性。该框架以用户友好的网络应用程序形式呈现，可通过以下链接访问：https://github.com/ftgTUGraz/Chat2Scenario。