Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems

Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-base...

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Autores principales:	Andreas Rausch, Azarmidokht Motamedi Sedeh, Meng Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	safety engineering autonomous system perception artificial intelligence autoencoder novelty detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/99dbbcd60bfe4bf99211cf25abf9360e
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id	oai:doaj.org-article:99dbbcd60bfe4bf99211cf25abf9360e
record_format	dspace
spelling	oai:doaj.org-article:99dbbcd60bfe4bf99211cf25abf9360e2021-11-11T14:59:46ZAutoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems10.3390/app112198812076-3417https://doaj.org/article/99dbbcd60bfe4bf99211cf25abf9360e2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9881https://doaj.org/toc/2076-3417Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection, that is, identifying data that differ in some respect from the data used for training, becomes a safety measure for system development and operation. In this study, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from the literature by minimizing false negatives.Andreas RauschAzarmidokht Motamedi SedehMeng ZhangMDPI AGarticlesafety engineeringautonomous systemperceptionartificial intelligenceautoencodernovelty detectionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9881, p 9881 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	safety engineering autonomous system perception artificial intelligence autoencoder novelty detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	safety engineering autonomous system perception artificial intelligence autoencoder novelty detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Andreas Rausch Azarmidokht Motamedi Sedeh Meng Zhang Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
description	Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection, that is, identifying data that differ in some respect from the data used for training, becomes a safety measure for system development and operation. In this study, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from the literature by minimizing false negatives.
format	article
author	Andreas Rausch Azarmidokht Motamedi Sedeh Meng Zhang
author_facet	Andreas Rausch Azarmidokht Motamedi Sedeh Meng Zhang
author_sort	Andreas Rausch
title	Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
title_short	Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
title_full	Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
title_fullStr	Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
title_full_unstemmed	Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems
title_sort	autoencoder-based semantic novelty detection: towards dependable ai-based systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/99dbbcd60bfe4bf99211cf25abf9360e
work_keys_str_mv	AT andreasrausch autoencoderbasedsemanticnoveltydetectiontowardsdependableaibasedsystems AT azarmidokhtmotamedisedeh autoencoderbasedsemanticnoveltydetectiontowardsdependableaibasedsystems AT mengzhang autoencoderbasedsemanticnoveltydetectiontowardsdependableaibasedsystems
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Autoencoder-Based Semantic Novelty Detection: Towards Dependable AI-Based Systems

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