Automated Decomposition and Allocation of Automotive Safety Integrity Levels Using Exact Solvers
Bibliographic record
Abstract
The number of software-intensive and complex electronic automotive systems is continuously increasing. Many of these systems are safety-critical and pose growing safety-related concerns. ISO 26262 is the automotive functional safety standard developed for the passenger car industry. It provides guidelines to reduce and control the risk associated with safety-critical systems that include electric and (programmable) electronic parts. The standard uses the concept of Automotive Safety Integrity Levels (ASILs) to decompose and allocate safety requirements of different stringencies to the elements of a system architecture in a top-down manner: ASILs are assigned to system-level hazards, and then they are iteratively decomposed and allocated to relevant subsystems and components. ASIL decomposition rules may give rise to multiple alternative allocations, leading to an optimization problem of finding the cost-optimal allocations. Recognizing the difficulties of the problem, researchers have proposed dedicated tools using heuristics, such as Tabu search and genetic algorithms. However, these algorithms may find near-optimal solutions, potentially missing the optimal solutions desired by stakeholders. In this paper, we aim at finding all optimal ASIL allocations using off-the-shelf solvers. We implement our approach using three major classes of state-of-the-art solvers: CSP (Constraint Satisfaction Problem), SMT (Satisfiability Modulo Theories), and ILP (Integer Linear Programming). We evaluate the feasibility and performance of our approach on three variants of a real-world Hybrid Braking System for electrical vehicle integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".