Towards the Certification of an Evacuation Assistance System Utilizing AI-based Approaches

Using Artificial Intelligence-based approaches in safety-critical applications requires special attention during development. For instance, as of the beginning of 2027, European Union regulations mandate certification by a notified body for AI integration in safety-critical machinery applications. Nevertheless, AI-based approaches find application across diverse domains, enhancing system performance. Evacuation Assistance Systems used for evacuating buildings during emergencies like fires or terrorist attacks are examples in this context. In recent years, there has been a surge in research and standardization attempts to provide an assurance base for utilizing AI techniques in safety-critical applications from the technical and legislative perspectives. However, the focus is often reduced to automated driving and robotics, and many questions still need to be answered.This paper presents our research on the certification of AI-based systems. We highlight our effort in determining the relevant international standards that need to be complied with. The contribution of this paper is a certification concept for AI-based systems, where performance and reliability are crucial. The unique overview of state-of-the-art and industrial standards allows a certification attempt for this type of system. It also provides a base for future work beyond the scope of automated driving and robotics, such as assistance systems and building automation.The analytical discourse presented in this contribution justifies and highlights the mapping of standards and techniques to required functionalities and architectural components of the Evacuation Assistance System, supporting the quality and performance, system acceptance, and certification for the dedicated domain and purpose.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/issrew63542.2024.00086
Primary Topic
Evacuation and Crowd Dynamics
Type
article
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article

Towards the Certification of an Evacuation Assistance System Utilizing AI-based Approaches

Arezoo Sarkheyli-Hägele, Georg Hägele, Abdelbaki Bouguerra
Evacuation and Crowd Dynamics
article

Towards the Certification of an Evacuation Assistance System Utilizing AI-based Approaches

Arezoo Sarkheyli-Hägele, Georg Hägele, Abdelbaki Bouguerra
article en

Abstract

Using Artificial Intelligence-based approaches in safety-critical applications requires special attention during development. For instance, as of the beginning of 2027, European Union regulations mandate certification by a notified body for AI integration in safety-critical machinery applications. Nevertheless, AI-based approaches find application across diverse domains, enhancing system performance. Evacuation Assistance Systems used for evacuating buildings during emergencies like fires or terrorist attacks are examples in this context. In recent years, there has been a surge in research and standardization attempts to provide an assurance base for utilizing AI techniques in safety-critical applications from the technical and legislative perspectives. However, the focus is often reduced to automated driving and robotics, and many questions still need to be answered.This paper presents our research on the certification of AI-based systems. We highlight our effort in determining the relevant international standards that need to be complied with. The contribution of this paper is a certification concept for AI-based systems, where performance and reliability are crucial. The unique overview of state-of-the-art and industrial standards allows a certification attempt for this type of system. It also provides a base for future work beyond the scope of automated driving and robotics, such as assistance systems and building automation.The analytical discourse presented in this contribution justifies and highlights the mapping of standards and techniques to required functionalities and architectural components of the Evacuation Assistance System, supporting the quality and performance, system acceptance, and certification for the dedicated domain and purpose.

University Mohamed Boudiaf of M'sila (DZ)
Climate action
Openalex Percentile: Top 71%
Evacuation and Crowd Dynamics
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