Ensuring Trustworthiness of AI-Enhanced Embedded Systems
A road-vehicle standard-based unified AI safety lifecycle and blueprint for integrating both robustness and resilience into AI systems deployed in safety-critical domains. The post Ensuring Trustworthiness of AI-Enhanced Embedded Systems appeared first on Semiconductor Engineering .
A road-vehicle standard-based unified AI safety lifecycle and blueprint for integrating both robustness and resilience into AI systems deployed in safety-critical domains.
Artificial Intelligence (AI) is unlocking new capabilities in safety-critical systems, from enhanced motor control to autonomous driving. However, integrating AI safely remains a significant challenge due to its data-driven nature and operation in open and real-world variability. While established standards such as ISO 26262 and ISO 21448 provide a foundation for functional safety and intended functionality, they do not fully address AI-specific properties like robustness, resilience, and transparency. Emerging standards such as ISO/PAS 8800 begin to close this gap by introducing AI-specific safety lifecycles and properties.
This white paper presents a unified safety lifecycle that integrates these standards. Using AI-Based Motor Control Unit (AI-MC) as an illustrative use case, it shows how robustness and resilience can be systematically realized through concrete development-time measures and operational-time measures (e.g., Out-of-Distribution). Together, these enable safe and reliable deployment of AI-based systems on real-time high integrity platforms. The recommendations presented aim to guide practitioners in systematically integrating AI into safety-critical systems without compromising safety, availability, or trustworthiness.
Read more here.
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