用生成式AI自动生成自动驾驶传感器故障树，提升安全分析效率
FTA generation using GenAI with an Autonomy sensor Usecase
- 通过提示工程调用开源大模型生成故障树
- 成功实现基于PlantUML的故障树自动化构建
- 适用于自动驾驶系统各类传感器故障分析
功能安全是系统设计中的关键环节，尤其在汽车领域已持续演进多年。目前已有多种方法用于生成不同场景下自动驾驶相关的故障树分析（FTA）。本文探索生成式人工智能（GenAI）在故障树分析中的应用，聚焦于激光雷达（Lidar）传感器失效这一具体用例。研究对比了多个开源大语言模型（LLM），并深入分析其中一种模型的输出表现。结果表明，通过提示工程可有效训练现有大模型，完成针对任意自主系统用例的故障树分析，并借助PlantUML工具实现可视化生成，验证了该方法的可行性与实用性。
原文摘要 · Abstract (English)
Functional safety forms an important aspect in the design of systems. Its emphasis on the automotive industry has evolved significantly over the years. Till date many methods have been developed to get appropriate FTA(Fault Tree analysis) for various scenarios and features pertaining to Autonomous Driving. This paper is an attempt to explore the scope of using Generative Artificial Intelligence(GenAI) in order to develop Fault Tree Analysis(FTA) with the use case of malfunction for the Lidar sensor in mind. We explore various available open source Large Language Models(LLM) models and then dive deep into one of them to study its responses and provide our analysis. This paper successfully shows the possibility to train existing Large Language models through Prompt Engineering for fault tree analysis for any Autonomy usecase aided with PlantUML tool.
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