欢迎你的新AI队友：通过约束大型语言模型进行安全分析
Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models
AI总结：
针对自动驾驶安全工程中HARA流程效率低的问题，提出利用大型语言模型自动化该流程的框架，并强调专家审查仍不可替代。
摘要：
DevOps在许多行业中都是必需的，包括自动驾驶汽车的开发。在这些环境中，存在一些迭代活动会降低SafetyOps周期的速度。其中一项活动是“危害分析与风险评估”（HARA），这是启动安全需求规范的关键步骤。作为提高SafetyOps中此步骤速度的一种潜在方法，我们深入研究了大型语言模型（LLMs）的能力。我们的目标是系统评估其在安全工程领域应用的潜力。为此，我们提出了一个框架，以支持使用LLMs实现更高程度的HARA自动化。尽管我们努力尽可能自动化流程，但专家审查对于确保分析结果的有效性和正确性仍然至关重要，并需据此进行必要的修改。
英文摘要：
DevOps is a necessity in many industries, including the development of Autonomous Vehicles. In those settings, there are iterative activities that reduce the speed of SafetyOps cycles. One of these activities is "Hazard Analysis & Risk Assessment" (HARA), which is an essential step to start the safety requirements specification. As a potential approach to increase the speed of this step in SafetyOps, we have delved into the capabilities of Large Language Models (LLMs). Our objective is to systematically assess their potential for application in the field of safety engineering. To that end, we propose a framework to support a higher degree of automation of HARA with LLMs. Despite our endeavors to automate as much of the process as possible, expert review remains crucial to ensure the validity and correctness of the analysis results, with necessary modifications made accordingly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
