Application of data-driven surrogate models for active human model response prediction and restraint system optimization Article Swipe
Julian Hay
,
Lars Schories
,
Eric Bayerschen
,
Peter Wimmer
,
Oliver Zehbe
,
Stefan Kirschbichler
,
Jörg Fehr
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fams.2023.1156785
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fams.2023.1156785
Surrogate models are a must-have in a scenario-based safety simulation framework to design optimally integrated safety systems for new mobility solutions. The objective of this study is the development of surrogate models for active human model responses under consideration of multiple sampling strategies. A Gaussian process regression is chosen for predicting injury values based on the collision scenario, the occupant's seating position after a pre-crash movement and selected restraint system parameters. The trained models are validated and assessed for each sampling method and the best-performing surrogate model is selected for restraint system parameter optimization.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fams.2023.1156785
- https://www.frontiersin.org/articles/10.3389/fams.2023.1156785/pdf?isPublishedV2=False
- OA Status
- gold
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367322960
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367322960Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fams.2023.1156785Digital Object Identifier
- Title
-
Application of data-driven surrogate models for active human model response prediction and restraint system optimizationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-27Full publication date if available
- Authors
-
Julian Hay, Lars Schories, Eric Bayerschen, Peter Wimmer, Oliver Zehbe, Stefan Kirschbichler, Jörg FehrList of authors in order
- Landing page
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https://doi.org/10.3389/fams.2023.1156785Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fams.2023.1156785/pdf?isPublishedV2=FalseDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fams.2023.1156785/pdf?isPublishedV2=FalseDirect OA link when available
- Concepts
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Surrogate model, Kriging, Gaussian process, Computer science, Sampling (signal processing), Regression analysis, Process (computing), Machine learning, Artificial intelligence, Gaussian, Operating system, Computer vision, Physics, Quantum mechanics, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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