Safety Analysis of the Local Interconnect Network Protocol in the Context of ISO26262
Bibliographic record
Abstract
In this work, we analyze the use of the Local Interconnect Network (LIN) bus (and some of its potential variants) as Safety Element out of Context (SEooC) from an ISO-26262 perspective and provide the reader with an analysis methodology to compare between a range of different LIN protocol configurations and benchmark them against Automotive Safety Integrity Level (ASIL) targets as defined in ISO-26262. A methodology for a quantitative residual failure probability analysis is shown before applying it to the standard LIN protocol. The residual failure rate in time (RF) of LIN (compliant with ISO26262) has been investigated with a range of reasonable application assumptions. This paper shows that a high bit error probability assumption of 3e-5 yields an RF of 3e-4/h which is too high to satisfy the assumed ASIL-B target (1e-7/h) or higher functional safety requirements in noisy application. However, an additional safety mechanism such as the ISO-26262 proposed CRC-8 with 0×97 polynomial is studied and is found to yield a reduced RF of ∼3e-19/h. As well, change in the assumptions such as a lower bit error probability of 1e-6 is found to yield a reduced RF of 4e-10/h. These results indicate that, based on the assumptions, the standard LIN may not meet the ASIL-B RF targets. Refining the assumptions and conducting a system level analysis is definitely required as future work to establish a more accurate residual fault assessment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".