Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach

Abstract

This paper integrates Fault Tree Analysis (FTA) and Bayesian Networks (BN) to assess collision risk and establish Automotive Safety Integrity Level (ASIL) B failure rate targets for critical autonomous vehicle (AV) components. The FTA-BN integration combines the systematic decomposition of failure events provided by FTA with the probabilistic reasoning capabilities of BN, which allow for dynamic updates in failure probabilities, enhancing the adaptability of risk assessment. A fault tree is constructed based on AV subsystem architecture, with collision as the top event, and failure rates are assigned while ensuring the total remains within 100 FIT. Bayesian inference is applied to update posterior probabilities, and the results indicate that perception system failures (46.06 FIT) are the most significant contributor, particularly failures to detect existing objects (PF5) and misclassification (PF6). Mitigation strategies are proposed for sensors, perception, decision-making, and motion control to reduce the collision risk. The FTA-BN integration approach provides dynamic risk quantification, offering system designers refined failure rate targets to improve AV safety.