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ISO/PAS 8800. Lesson 5:Hazard Analysis and Risk Assessment HARA
Lesson: Hazard Analysis and Risk Assessment (HARA) in ISO/PAS 8800
1. Introduction to AI-Specific HARA Hazard Analysis and Risk Assessment (HARA) is a foundational safety activity in the automotive industry, traditionally governed by ISO 26262. However, ISO/PAS 8800 extends this process to address the unique characteristics of Artificial Intelligence (AI) and Machine Learning (ML). While traditional HARA focuses on malfunctioning behavior (e.g., a short circuit), AI HARA must also consider hazards arising from performance limitations and environmental triggers, aligning closely with ISO 21448 (SOTIF).
2. The Item Definition and ODD Analysis Before hazards can be identified, the 'Item' must be clearly defined. In the context of AI, this includes the intended functionality and a rigorous definition of the Operational Design Domain (ODD). The ODD specifies the external conditions—such as road types, weather, and lighting—under which the AI system is designed to operate safely. Any operation outside these boundaries is considered a 'limit' that the system must handle safely.
3. Hazard Identification Hazards in ISO/PAS 8800 are categorized into two primary types: 1. Malfunctioning Behavior: Failures caused by errors in the software or hardware execution. 2. Performance Limitations: Situations where the AI model performs as programmed but fails to meet safety needs (e.g., a perception system failing to detect a specific type of obstacle due to a lack of training data). The analysis explores how these behaviors lead to hazardous events in specific driving scenarios.
4. Risk Estimation: S, E, and C Once hazards are identified, the risk is estimated using three parameters: - Severity (S): The intensity of potential harm to passengers or road users (S0 to S3). - Exposure (E): The probability of the vehicle being in a scenario where the hazard could occur (E0 to E4). - Controllability (C): The ability of the driver or the system to prevent harm once the hazard has occurred (C0 to C3). These parameters are used to determine the Automotive Safety Integrity Level (ASIL), ranging from QM (Quality Management) to ASIL D. ## 5. Deriving Safety Goals The final output of the HARA is a set of Safety Goals. These are high-level safety requirements assigned to the system to mitigate the identified risks. For AI systems, safety goals often involve specific performance metrics, such as minimum detection probabilities or maximum latency requirements for safety-critical decisions.
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By Veljko Massimo PlavsicLesson: Hazard Analysis and Risk Assessment (HARA) in ISO/PAS 8800
1. Introduction to AI-Specific HARA Hazard Analysis and Risk Assessment (HARA) is a foundational safety activity in the automotive industry, traditionally governed by ISO 26262. However, ISO/PAS 8800 extends this process to address the unique characteristics of Artificial Intelligence (AI) and Machine Learning (ML). While traditional HARA focuses on malfunctioning behavior (e.g., a short circuit), AI HARA must also consider hazards arising from performance limitations and environmental triggers, aligning closely with ISO 21448 (SOTIF).
2. The Item Definition and ODD Analysis Before hazards can be identified, the 'Item' must be clearly defined. In the context of AI, this includes the intended functionality and a rigorous definition of the Operational Design Domain (ODD). The ODD specifies the external conditions—such as road types, weather, and lighting—under which the AI system is designed to operate safely. Any operation outside these boundaries is considered a 'limit' that the system must handle safely.
3. Hazard Identification Hazards in ISO/PAS 8800 are categorized into two primary types: 1. Malfunctioning Behavior: Failures caused by errors in the software or hardware execution. 2. Performance Limitations: Situations where the AI model performs as programmed but fails to meet safety needs (e.g., a perception system failing to detect a specific type of obstacle due to a lack of training data). The analysis explores how these behaviors lead to hazardous events in specific driving scenarios.
4. Risk Estimation: S, E, and C Once hazards are identified, the risk is estimated using three parameters: - Severity (S): The intensity of potential harm to passengers or road users (S0 to S3). - Exposure (E): The probability of the vehicle being in a scenario where the hazard could occur (E0 to E4). - Controllability (C): The ability of the driver or the system to prevent harm once the hazard has occurred (C0 to C3). These parameters are used to determine the Automotive Safety Integrity Level (ASIL), ranging from QM (Quality Management) to ASIL D. ## 5. Deriving Safety Goals The final output of the HARA is a set of Safety Goals. These are high-level safety requirements assigned to the system to mitigate the identified risks. For AI systems, safety goals often involve specific performance metrics, such as minimum detection probabilities or maximum latency requirements for safety-critical decisions.