Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it