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Ensuring Safety of Machine Learning Components Using Operational Design Domain

Torens, Christoph and Jünger, Franz and Schirmer, Sebastian and Schopferer, Simon and Zhukov, Dmytro and Dauer, Johann C. (2023) Ensuring Safety of Machine Learning Components Using Operational Design Domain. In: AIAA SciTech 2023 Forum, pp. 1-14. AIAA SciTech 2023 Forum, 2023-01-23 - 2023-01-27, National Harbor, MD & Online. doi: 10.2514/6.2023-1124.

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Official URL: https://arc.aiaa.org/doi/abs/10.2514/6.2023-1124


The introduction of machine learning in the aviation domain is an ongoing process. This is also true for safety-critical domains, especially for the area of Urban Air Mobility. A significant growth in number of air taxis and an increasing level of autonomy is to be expected allowing for operating a large number of air taxis in complex urban environments. Due to the complexity of the tasks and the environment, key autonomy functions will be realized using machine learning, for example the camera-based detection of objects. However, the safety assurance for avionics systems using machine learning components is challenging. This work investigates safety and verification aspects of machine learning components. A camera-based detection of humans on the ground, e.g. to assess a potential landing area, serves as an example for an machine learning-based autonomy functio and was integrated into an Unmanned Aircraft. In the context of this exemplary machine learning component, the concept of Operational Design Domain as recently adapted European Aviation Safety Agency in the context of machine learning assurance is described along with other key concepts of machine learning assurance. Furthermore, runtime assurance is used to monitor conformance to the Operational Design Domain during flight. The presented flight test results indicate that monitoring the Operational Design Domain can support performance as well as the safety of the operation.

Item URL in elib:https://elib.dlr.de/196759/
Document Type:Conference or Workshop Item (Speech)
Title:Ensuring Safety of Machine Learning Components Using Operational Design Domain
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Torens, ChristophUNSPECIFIEDhttps://orcid.org/0000-0002-0651-4390145585669
Schirmer, SebastianUNSPECIFIEDhttps://orcid.org/0000-0002-4596-2479UNSPECIFIED
Schopferer, SimonUNSPECIFIEDhttps://orcid.org/0000-0001-5254-3961UNSPECIFIED
Dauer, Johann C.UNSPECIFIEDhttps://orcid.org/0000-0002-8287-2376UNSPECIFIED
Date:23 January 2023
Journal or Publication Title:AIAA SciTech 2023 Forum
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-14
Keywords:urban air mobility, machine learning, operational design domain, safety, safe autonomy
Event Title:AIAA SciTech 2023 Forum
Event Location:National Harbor, MD & Online
Event Type:international Conference
Event Start Date:23 January 2023
Event End Date:27 January 2023
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 - Unmanned Aerial Systems
Location: Braunschweig
Institutes and Institutions:Institute of Flight Systems > Unmanned Aircraft
Institute of Flight Systems
Deposited By: Torens, Christoph
Deposited On:30 Oct 2023 16:21
Last Modified:24 Apr 2024 20:57

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