Toward Safety-Critical Artificial Intelligence (AI)-Based Embedded Automotive Systems

The rise of AI models across diverse domains includes promising advancements, but also poses critical challenges. In particular, establishing trust in AI-based systems for mission-critical applications is challenging for most domains. For the automotive domain, embedded systems are operating in real-time and undertaking mission-critical tasks. Ensuring dependability attributes, especially safety, of these systems remains a predominant challenge. This article focuses on the application of AI-based systems in safety-critical contexts within automotive domains. Drawing from current standardization methodologies and established patterns for safe application, this work offers a reflective analysis, emphasizing overlaps and potential avenues to put AI-based systems into practice within the automotive landscape. The core focus lies in incorporating pattern concepts, fostering the safe integration of AI in automotive systems, with requirements described in standardization and topics discussed by AI working groups. This article aims to provide a concept on leveraging AI-based systems while addressing safety concerns within the automotive sector and current versions of related standards. The proposed approach explores synergies and highlights pathways for the utilization of AI-based systems within safety-critical automotive applications.