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 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: this https URL.
Abstract (translated)
大语言模型的出现为验证自动驾驶系统（ADS）提供了新的见解。在本文中，我们提出了一种从自然驾驶数据集中提取场景的新方法。一个名为Chat2Scenario的框架利用了LLM的先进自然语言处理（NLP）功能来理解和识别不同的驾驶场景。通过输入驾驶条件的描述文本并指定关键度指标阈值，该框架有效地搜索所需的场景，并将它们转换为ASAM OpenSCENARIO和IPG CarMaker文本文件。这种方法简化了场景提取过程，提高了效率。通过模拟验证了该方法的有效性。该框架基于易用的网页应用程序，可以通过以下链接访问：this <https://url>。
URL
https://arxiv.org/abs/2404.16147
