The missing link: Developing a safety case for perception components in\n automated driving
Bibliographic record
Abstract
Safety assurance is a central concern for the development and societal\nacceptance of automated driving (AD) systems. Perception is a key aspect of AD\nthat relies heavily on Machine Learning (ML). Despite the known challenges with\nthe safety assurance of ML-based components, proposals have recently emerged\nfor unit-level safety cases addressing these components. Unfortunately, AD\nsafety cases express safety requirements at the system level and these efforts\nare missing the critical linking argument needed to integrate safety\nrequirements at the system level with component performance requirements at the\nunit level. In this paper, we propose the Integration Safety Case for\nPerception (ISCaP), a generic template for such a linking safety argument\nspecifically tailored for perception components. The template takes a deductive\nand formal approach to define strong traceability between levels. We\ndemonstrate the applicability of ISCaP with a detailed case study and discuss\nits use as a tool to support incremental development of perception components.\n
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".