Bayesian Fault Injection Safety Testing for Highly Automated Vehicles With Uncertainty
Bibliographic record
Abstract
Highly Automated Vehicles (HAVs) are exposed to numerous unexpected faults that threaten the functionality of the Autonomous Driving System (ADS) in HAVs, and even minor faults can lead to serious consequences such as collisions. Accordingly, fault tolerance of HAVs should be thoroughly evaluated before large-scale deployment. Fault Injection (FI) testing is commonly used for the verification and validation of HAVs. However, due to the time cost of FI simulation, it is impossible to simulate all combinations of initial conditions and the high-dimensional fault space. Meanwhile, the inherent uncertainty in complex ADS of the HAV under test cause uncertain testing results, which leads to unreliable results in one-time simulation. To address these problems, an accelerated FI method considering uncertainty within ADS based on Dynamic Bayesian Network (DBN) is proposed. DBN is applied to serve as a surrogate for HAVs and to learn the causal relationship between factors in the complex system of HAVs. Rolling Forecast and Monte Carlo sampling are combined to predict the collision probability after FI. Taking open-source Autonomous Driving System Baidu Apollo as the System Under Test, the experimental results demonstrate that the DBN-based FI method performs well both in efficiency and accuracy across various scenarios. DBN-based FI is 447 times faster than simulation and can achieve 88.9% precision. Furthermore, the collision probability and the variable distribution calculated by uncertain prediction are close to those obtained by simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".