Application of data-driven surrogate models for active human model response prediction and restraint system optimization Article Swipe

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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

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.

Type

article

Language

en

Landing Page

https://doi.org/10.3389/fams.2023.1156785

PDF

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

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