A Scenario-Based Approach to Assess Exposure for ASIL Determination
Bibliographic record
Abstract
Exposure in ISO 26262 is defined as the state of being in an operational situation that can be hazardous if coincident with the failure mode under analysis. An operational situation is defined as a scenario that can occur during a vehicle's life with examples given such as driving, parking, or maintenance. Accurately predicting exposure is one of the more difficult tasks in the ASIL determination. ISO 26262 Part 3 attempts to provide guidance in Annex B through tables of potential operational situations and associated exposure levels. However, the contents of these tables may not allow for an accurate prediction of exposure and may lead to an exposure value that is too high or too low. In this paper, we describe a potential method for determining exposure that considers a potential mishap scenario as a composition of multiple coincident operational situations rather than considering a single operational situation as indicated in the tables in Annex B of Part 3. We purport that for a potentially hazardous situation to occur if the failure mode under analysis occurs, it typically requires multiple operational situations to be present at the same time, in addition to the failure mode. We believe that considering a mishap scenario with multiple coincident operational situations in the evaluation of exposure provides a more accurate determination of exposure.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".