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ISO/PAS 8800. Lesson 3:Relation with ISO 26262 and SOTIF
1. Overview of the Safety Ecosystem ISO/PAS 8800, titled 'Road vehicles — Safety and artificial intelligence', was developed to address the unique safety challenges posed by Machine Learning (ML) and Artificial Intelligence (AI) in automotive applications. It does not replace the existing safety standards; rather, it acts as a specialized supplement. To understand its role, one must look at the two primary pillars of automotive safety: ISO 26262 (Functional Safety) and ISO 21448 (Safety of the Intended Functionality, or SOTIF).
2. Interaction with ISO 26262 (Functional Safety) ISO 26262 focuses on hazards caused by malfunctions in electrical and electronic (E/E) systems. These are typically divided into systematic failures (e.g., software bugs) and random hardware failures.
How ISO/PAS 8800 Fits: While ISO 26262 provides the general framework for software development (Part 6), it was not originally designed for the non-deterministic nature of AI. ISO/PAS 8800 provides specific guidance for the 'AI element' within the ISO 26262 lifecycle. It helps define how to handle systematic failures in the AI training process, model selection, and deployment that could lead to functional safety violations. ## 3. Interaction with ISO 21448 (SOTIF) SOTIF deals with hazards that occur without a system failure. Instead, these hazards arise from performance limitations or environmental triggers (e.g., a vision system failing to detect a pedestrian because of intense sun glare).
How ISO/PAS 8800 Fits: AI performance limitations are a core concern of SOTIF. ISO/PAS 8800 expands on the SOTIF concept by providing detailed methodologies for AI-specific issues like data bias, over-fitting, and robustness against adversarial attacks. It provides the technical 'how-to' for achieving the safety goals defined by the SOTIF process when AI is the underlying technology. ## 4. The Integrated Approach The relationship can be visualized as a Venn diagram where ISO/PAS 8800 sits at the intersection of AI development and automotive safety requirements.
ISO 26262: Ensures the AI hardware and integration logic don't break.
ISO 21448 (SOTIF): Ensures the AI's intended function is safe in complex environments.
ISO/PAS 8800: Provides the specific AI/ML engineering practices to satisfy both of the above. ## 5. Key Mapping Points
Data Quality: ISO/PAS 8800 provides requirements for dataset completeness and representativeness, which supports SOTIF's goal of reducing 'Unknown Unsafe' scenarios. *
Validation & Verification: It introduces AI-specific V&V methods, such as metamorphic testing, which are required to supplement the traditional testing methods found in ISO 26262.
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By Veljko Massimo Plavsic1. Overview of the Safety Ecosystem ISO/PAS 8800, titled 'Road vehicles — Safety and artificial intelligence', was developed to address the unique safety challenges posed by Machine Learning (ML) and Artificial Intelligence (AI) in automotive applications. It does not replace the existing safety standards; rather, it acts as a specialized supplement. To understand its role, one must look at the two primary pillars of automotive safety: ISO 26262 (Functional Safety) and ISO 21448 (Safety of the Intended Functionality, or SOTIF).
2. Interaction with ISO 26262 (Functional Safety) ISO 26262 focuses on hazards caused by malfunctions in electrical and electronic (E/E) systems. These are typically divided into systematic failures (e.g., software bugs) and random hardware failures.
How ISO/PAS 8800 Fits: While ISO 26262 provides the general framework for software development (Part 6), it was not originally designed for the non-deterministic nature of AI. ISO/PAS 8800 provides specific guidance for the 'AI element' within the ISO 26262 lifecycle. It helps define how to handle systematic failures in the AI training process, model selection, and deployment that could lead to functional safety violations. ## 3. Interaction with ISO 21448 (SOTIF) SOTIF deals with hazards that occur without a system failure. Instead, these hazards arise from performance limitations or environmental triggers (e.g., a vision system failing to detect a pedestrian because of intense sun glare).
How ISO/PAS 8800 Fits: AI performance limitations are a core concern of SOTIF. ISO/PAS 8800 expands on the SOTIF concept by providing detailed methodologies for AI-specific issues like data bias, over-fitting, and robustness against adversarial attacks. It provides the technical 'how-to' for achieving the safety goals defined by the SOTIF process when AI is the underlying technology. ## 4. The Integrated Approach The relationship can be visualized as a Venn diagram where ISO/PAS 8800 sits at the intersection of AI development and automotive safety requirements.
ISO 26262: Ensures the AI hardware and integration logic don't break.
ISO 21448 (SOTIF): Ensures the AI's intended function is safe in complex environments.
ISO/PAS 8800: Provides the specific AI/ML engineering practices to satisfy both of the above. ## 5. Key Mapping Points
Data Quality: ISO/PAS 8800 provides requirements for dataset completeness and representativeness, which supports SOTIF's goal of reducing 'Unknown Unsafe' scenarios. *
Validation & Verification: It introduces AI-specific V&V methods, such as metamorphic testing, which are required to supplement the traditional testing methods found in ISO 26262.