The Necessity of a Holistic Safety Evaluation Framework for AI-Based Automation Features
arXiv:2602.05157v2 Announce Type: replace Abstract: The intersection of Safety of Intended Functionality (SOTIF) and Functional Safety (FuSa) analysis of driving automation features has traditionally excluded Quality Management (QM) components (components that has no ASIL requirements allocated from vehicle-level HARA) from rigorous safety impact evaluations. While QM components are not typically classified as safety-relevant, recent developments in artificial intelligence (AI) integration reveal that such components can contribute to SOTIF-related hazardous risks. Compliance with emerging AI safety standards, such as ISO/PAS 8800, necessitates re-evaluating safety considerations for these components. This paper examines the necessity of conducting holistic safety analysis and risk assessm
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Abstract:The intersection of Safety of Intended Functionality (SOTIF) and Functional Safety (FuSa) analysis of driving automation features has traditionally excluded Quality Management (QM) components (components that has no ASIL requirements allocated from vehicle-level HARA) from rigorous safety impact evaluations. While QM components are not typically classified as safety-relevant, recent developments in artificial intelligence (AI) integration reveal that such components can contribute to SOTIF-related hazardous risks. Compliance with emerging AI safety standards, such as ISO/PAS 8800, necessitates re-evaluating safety considerations for these components. This paper examines the necessity of conducting holistic safety analysis and risk assessment on AI components, emphasizing their potential to introduce hazards with the capacity to violate risk acceptance criteria when deployed in safety-critical driving systems, particularly in perception algorithms. Using case studies, we demonstrate how deficiencies in AI-driven perception systems can emerge even in QM-classified components, leading to unintended functional behaviors with critical safety implications. By bridging theoretical analysis with practical examples, this paper argues for the adoption of comprehensive FuSa, SOTIF, and AI standards-driven methodologies to identify and mitigate risks in AI components. The findings demonstrate the importance of revising existing safety frameworks to address the evolving challenges posed by AI, ensuring comprehensive safety assurance across all component classifications spanning multiple safety standards.
Subjects:
Software Engineering (cs.SE); Systems and Control (eess.SY)
Cite as: arXiv:2602.05157 [cs.SE]
(or arXiv:2602.05157v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2602.05157
arXiv-issued DOI via DataCite
Submission history
From: Alireza Abbaspour [view email] [v1] Thu, 5 Feb 2026 00:22:24 UTC (384 KB) [v2] Mon, 30 Mar 2026 20:12:49 UTC (384 KB)
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