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

Julian Hay*, Lars Schories, Eric Bayerschen, Peter Wimmer, Oliver Zehbe, Stefan Karl Kirschbichler, Jörg Fehr

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Article number1156785
JournalFrontiers in Applied Mathematics and Statistics
Volume9
DOIs
Publication statusPublished - 27 Apr 2023

Keywords

  • active human model
  • prediction of injury values
  • restraint system optimization
  • simulation framework
  • surrogate modeling

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability

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