SOTIF-Analysis Using Fuzzy Cause Trees

This paper introduces the concept of Fuzzy Cause Trees (FCT) as a novel approach to Safety of the Intended Functionality (SOTIF) analysis in the automotive industry. Traditional methods like fault tree analysis and Bayesian Networks face limitations when dealing with the complex, interrelated conditions that affect system performance, particularly in perception systems. FCT leverages fuzzy logic to more accurately model and analyze the causal relationships between triggering conditions and system insufficiencies under uncertainty. This method not only offers a quantitative understanding of cause-effect chains but also identifies critical paths for intervention, guiding engineers in mitigating significant risks and enhancing the overall SOTIF of automotive systems. The paper presents the theoretical framework of FCT, its advantages over existing methods, and a practical application through a running example, highlighting its potential to advance SOTIF analyses in the automotive industry. We present several quantitative analysis techniques to derive insights from the model, which can be used to reveal a path towards the mitigation of functional insufficiencies.