The intersection of Safety of Intended Functionality (SOTIF) and Functional Safety (FuSa) analysis of driving automation features has traditionally excluded Quality Management (QM) components 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.

翻译：驾驶自动化功能预期功能安全与功能安全分析的交叉领域，传统上一直将质量管理组件排除在严格的安全影响评估之外。虽然质量管理组件通常不被归类为安全相关组件，但人工智能集成的最新进展表明，此类组件可能引发与预期功能安全相关的危险风险。遵循新兴的人工智能安全标准，如ISO/PAS 8800，必须重新评估这些组件的安全考量。本文探讨了对人工智能组件进行整体安全分析与风险评估的必要性，强调其在部署于安全关键驾驶系统时，特别是在感知算法中，可能引发违反风险接受标准的潜在危害。通过案例研究，我们展示了即使在归类为质量管理的组件中，人工智能驱动的感知系统缺陷如何显现，从而导致具有关键安全影响的非预期功能行为。通过理论分析与实际案例的结合，本文主张采用融合功能安全、预期功能安全及人工智能标准的综合性方法论，以识别和缓解人工智能组件中的风险。研究结果表明，必须修订现有安全框架以应对人工智能带来的新挑战，确保跨越多重安全标准的所有组件分类都能获得全面的安全保障。