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Model-Based Systems Engineering for AI-Based Systems

Sprockhoff, Jasper and Lukic, Bojan and Janson, Vincent Josef and Gupta, Siddhartha and Ahlbrecht, Alexander and Durak, Umut and Krüger, Thomas (2023) Model-Based Systems Engineering for AI-Based Systems. AIAA SciTech 2023 Forum, 2023-01-23 - 2023-01-27, National Harbor, MD, USA. doi: 10.2514/6.2023-2587.

Full text not available from this repository.

Official URL: https://arc.aiaa.org/doi/abs/10.2514/6.2023-2587


In recent years, there has been significant progress in Artificial Intelligence (AI), leading to an increasing interest for integration of AI-based functions into newly developed systems. AI promises several benefits, amongst others, beyond the state-of-the-art functions and performance. However, the use of AI-techniques also introduces new challenges regarding safety and security of systems and their certification. These challenges mostly originate from the "black box nature" of complex AI algorithms. To tackle the challenges, safety of the AI-based systems has to be addressed throughout the entire development and life cycle of the system. The adaption of existing methods to the development of AI-based systems is necessary. An established method for the development of complex systems is Model-Based Systems Engineering (MBSE), which offers several advantages for the systems engineering process. In this paper three application examples of how MBSE can support the engineering process of AI-based systems are presented using an application use case: An AI-based threat localization system. First, a systematic development framework is used to design and model the AI-based system. Second, it is demonstrated how safety analysis can be integrated into a model of the system to identify potentially hazardous scenarios, which could arise, for example, due to erroneous predictions by an AI. For the analysis, an approach called Model-Based STPA is utilized which is based on the System-Theoretic Process Analysis. Third, it is demonstrated how MBSE can help in performing scenario-based safety assessment. From the operational domain model, executable configurations are generated to run scenario-based test cases.

Item URL in elib:https://elib.dlr.de/194442/
Document Type:Conference or Workshop Item (Speech)
Title:Model-Based Systems Engineering for AI-Based Systems
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sprockhoff, JasperUNSPECIFIEDhttps://orcid.org/0009-0005-5725-0726UNSPECIFIED
Date:19 January 2023
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Model-Based Systems Engineering, Artificial Intelligence, Machine Learning, Safety Analysis, Scenario-Based Testing
Event Title:AIAA SciTech 2023 Forum
Event Location:National Harbor, MD, USA
Event Type:international Conference
Event Start Date:23 January 2023
Event End Date:27 January 2023
Organizer:American Institute of Aeronautics and Astronautics, Inc.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Aircraft Systems
Location: Braunschweig
Institutes and Institutions:Institute of Flight Systems > Safety Critical Systems&Systems Engineering
Institute for AI Safety and Security
Deposited By: Sprockhoff, Jasper
Deposited On:27 Mar 2023 09:28
Last Modified:24 Apr 2024 20:55

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