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.

翻译：DevOps在许多行业，包括自动驾驶汽车的开发中，都是必不可少的。在此类场景中，存在一些重复性活动，它们会降低安全运营（SafetyOps）周期的速度。其中一项活动是“危险分析与风险评估”（HARA），这是启动安全需求规范的关键步骤。作为提高SafetyOps中这一步骤速度的潜在方法，我们深入探索了大型语言模型（LLMs）的能力。我们的目标是系统性地评估它们在安全工程领域应用的潜力。为此，我们提出了一个框架，支持通过LLMs实现HARA更高程度的自动化。尽管我们致力于尽可能自动化整个过程，但专家评审仍然至关重要，以确保分析结果的有效性和正确性，并据此进行必要的修改。