FTA generation using GenAI with an Autonomy sensor Usecase
This work addresses the need for efficient FTA generation in the automotive industry, specifically for autonomous driving use cases, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of generating Fault Tree Analysis (FTA) for autonomous driving systems by exploring the use of Generative AI (GenAI) with a focus on Lidar sensor malfunctions, and it successfully demonstrated the possibility of training existing Large Language Models through prompt engineering for this purpose.
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.