Please use this identifier to cite or link to this item:
Authors: Hay, Julian
Schories, Lars
Bayerschen, Eric
Wimmer, Peter
Zehbe, Oliver
Kirschbichler, Stefan
Fehr, Jörg
Title: Application of data-driven surrogate models for active human model response prediction and restraint system optimization
Issue Date: 2023 Zeitschriftenartikel 16 Frontiers in applied mathematics and statistics 9 (2023), No.1156785
ISSN: 2297-4687
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.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Files in This Item:
File Description SizeFormat 
fams-09-1156785.pdf3,21 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons