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Challenges in using neural networks in safety-critical applications
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Gripen C/D Saab Aeronautics.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Avionics Systems Saab.
Show others and affiliations
2020 (English)In: AIAA/IEEE Digital Avionics Systems Conference - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we discuss challenges when using neural networks (NNs) in safety-critical applications. We address the challenges one by one, with aviation safety in mind. We then introduce a possible implementation to overcome the challenges. Only a small portion of the solution has been implemented physically and much work is considered as future work. Our current understanding is that a real implementation in a safety-critical system would be extremely difficult. Firstly, to design the intended function of the NN, and secondly, designing monitors needed to achieve a deterministic and fail-safe behavior of the system. We conclude that only the most valuable implementations of NNs should be considered as meaningful to implement in safety-critical systems.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2020.
Keywords [en]
Avionics, Deep neural networks, Machine learning, Safety-critical, Digital avionics, Safety engineering, Security systems, Aviation safety, Fail safes, Neural networks (NNS), Safety critical applications, Safety critical systems, Neural networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-52970DOI: 10.1109/DASC50938.2020.9256519ISI: 000646035600048Scopus ID: 2-s2.0-85097976487ISBN: 9781728198255 (print)OAI: oai:DiVA.org:mdh-52970DiVA, id: diva2:1514839
Conference
39th AIAA/IEEE Digital Avionics Systems Conference, DASC 2020, 11 October 2020 through 16 October 2020
Available from: 2021-01-07 Created: 2021-01-07 Last updated: 2024-11-18Bibliographically approved
In thesis
1. Synthetic Data in Data-driven Systems
Open this publication in new window or tab >>Synthetic Data in Data-driven Systems
2025 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Dataset generation is cumbersome yet of great importance for successful training of machine learning models. Collecting real-world data is expensive and sometimes prohibited, considering e.g. safety aspects or legal restrictions. By generating the bulk of training data by synthetic means it is possible to impose arbitrary and extensive scene randomization for increased data diversity.

Methods to quantify similarity between datasets on a statistical level are important tools to detect Out-of-Distribution (OOD) data and domain alignment. We have studied how such methods can be used to correlate model prediction accuracy drop when exposed to OOD-data.

Domain adaptation can be applied as an additional step to synthetic data, to decrease the gap to real world datasets, however it can introduce inadvertent label-flipping, a sort of semantic inconsistency between synthetic source and domain adapted output. Therefore, we pursuit another way of reducing the domain gap, by generating high-fidelity digital representations of real-world scenes and objects. We do this through the use of Neural Radience Fields and Gaussian Splats. These methods allow us to render objects of interest for a detection problem, with the perfect annotation of synthetically produced data, and a high degree of realism which we show improves detection accuracy compared to traditionally generated visual content.

Abstract [sv]

Generering av data för AI-modeller är besvärligt men av stor betydelse för väl-fungerande träning av maskininlärningsmodeller. Att samla in riktig sensordata är dyrt och ibland inte möjligt, med hänsyn till exempelvis säkerhetsaspekter eller juridiska begränsningar. Genom att generera huvuddelen av träningsdata på syntetisk väg är det möjligt att införa omfattande scenrandomisering vilket leder till ökad datadiversifiering. Metoder för att kvantifiera likheter mellan datamängder på statistisk nivå är viktiga verktyg för att identifiera när data ligger utanför den tänkta distributionen. Vi har studerat hur sådana metoder kan användas för att korrelera hur en modellsprecision sjunker när den exponeras för osedd data. Domänanpassning kan tillämpas som ett ytterligare steg till syntetisk data, för att minska gapet till riktig sensordata, men detta kan innebära att man introducerar oavsiktliga annoteringsfel, en sorts semantisk inkonsistens mellan syntetisk källdata och domänanpassad utdata. Därför går vi en annan väg för att minska domängapet genom att generera digitala representationer med hög kvalitet av verkliga scener och föremål. Vi gör detta genom att använda Neural Radience Fields (NeRF) och Gaussiska Splats. Dessa metoder gör det möjligt för oss att skapa objekt av intresse för ett detektionsproblem, med automatisk annotering baserad på syntetiskt framställda data, och en hög grad av realism som vi visar förbättrar detektionsnoggrannheten jämfört med traditionellt genererat visuellt innehåll.

Place, publisher, year, edition, pages
Västerås: Mälardalens Universitet, 2025. p. 186
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 370
Keywords
datasets, neural networks, synthetic data generation, automatic annotation, dataset generation
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-69154 (URN)978-91-7485-689-7 (ISBN)
Presentation
2025-01-30, Delta, Mälardalens universitet, Västerås, 13:00 (English)
Opponent
Supervisors
Available from: 2024-11-18 Created: 2024-11-18 Last updated: 2024-11-18Bibliographically approved

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Forsberg, HåkanDaneshtalab, Masoud

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