The DOI matches its Crossref record.
Concept Enforcement and Modularization as Methods for the ISO 26262 Safety Argumentation of Neural Networks
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Evidence-based safety arguments: framework for safety case development method based on goal structuring notation (GSN).
“Evidence-based safety arguments: framework for safety case development method based on goal structuring notation (GSN) [70][71][72][73].”
From A requirements model for AI algorithms in functional safety-critical systems with an explainable self-enforcing network from a developer perspective (2024) · cites this paper as [72]
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Klüver, C., Greisbach, A., Kindermann, M., & Püttmann, B. (2024) · Security and Safety · doi:10.1051/sands/2024020 · Cited by 8
Metadata
- Authors
- Schwalbe, G., & Schels, M.
- Affiliations
- Continental (Germany) (DE)
- ORCID
- Schwalbe, Gesina
- Publisher
- Universitatsbibliothek Bamberg
- Published
- 2020
- OpenAlex
- W3001927475
- Cited by
- 14
- References
- 0
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2022: 4 · 2021: 2 · 2020: 1
- Type
- other
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-sa
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Physical Sciences › Computer Science › Hardware and Architecture › Real-Time Systems Scheduling
- Physical Sciences › Computer Science › Software › Software Reliability and Analysis Research
- Keywords
- ISO 26262, safety argumentation, artificial neural networks, modularization, vehicle safety, functional safety, safety-critical systems
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