Towards Dependable Integration Concepts for AI-Based Systems

Macher, Georg; Blazevic, Romana; Veledar, Omar; Brenner, Eugen

doi:10.1007/978-3-031-40953-0_10

Cited by 2 publications

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Blazevic,

Veledar,

Stolz

et al. 2024

SAE Int. J. CAV

View full text Add to dashboard Cite

<div>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.</div> <div>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.</div> <div>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.</div>

show abstract

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

Blazevic,

Veledar,

Stolz

et al. 2024

SAE Int. J. CAV

View full text Add to dashboard Cite

show abstract

Insides to Trustworthy AI-Based Embedded Systems

Blazevic,

Veledar,

Macher

2024

SAE Technical Paper Series

View full text Add to dashboard Cite

<div class="section abstract"><div class="htmlview paragraph">In an era characterized by the rapid proliferation and advancement of AI-based technologies across various domains, the spotlight is placed on the integration of these technologies into trustworthy autonomous systems. The integration into embedded systems necessitates a heightened focus on dependability. This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering. We establish the body of knowledge and key findings deliberated upon during an expert workshop held at an international conference focused on computer safety, reliability and security. The dialogue makes it evident that despite advancements, the assurance of dependability in AI-driven systems remains an unresolved challenge, lacking a one-size-fits-all solution. On the other hand, the positive outcome of this dialogue about the dependability of AI in embedded systems is that experts foster a shared understanding across diverse domains of expertise. We enhance the outcomes by considering the entirety of the PESTEL analysis framework encompassing political, environmental, social, technological, economic and legal dimensions. Therefore, this work synthesizes insights aiming to provide a comprehensive view informed by a multitude of perspectives and factors.</div></div>

show abstract

Towards Dependable Integration Concepts for AI-Based Systems

Cited by 2 publications

References 18 publications

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

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

Insides to Trustworthy AI-Based Embedded Systems

Contact Info

Product

Resources

About