Ensuring the safety of autonomous vehicles requires addressing not only hardware and software failures, but also functional insufficiencies arising from limitations in perception, decision-making, and sensor performance. While traditional Fault Tree Analysis (FTA) provides a systematic framework for identifying failure pathways, it does not account for the dynamic dependencies and uncertainties inherent in real-world driving scenarios. To overcome these limitations, this paper proposes an integrated methodology that combines FTA with BN from the perspective of the Safety of the Intended Functionality (SOTIF). The proposed framework enables both qualitative and quantitative safety analysis, capturing logical structural as well as probabilistic dependencies among triggering conditions, functional insufficiencies, and failure modes. A case study on object detection failure in autonomous vehicles is conducted to demonstrate the applicability of the methodology. The results indicate that environmental factors such as weather and occlusion are major contributors to object detection failures, which contributes to 45.76% and 58.72%, respectively. Additionally, posterior probability analysis highlights the influence of specific variables and provides actionable insights for prioritizing mitigation strategies. This study contributes a novel framework that enhances safety assessment capabilities in SOTIF contexts and provides system designers with practical guidance to improve the reliability and robustness of autonomous vehicle systems.
Dai et al. (Fri,) studied this question.
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