AUTHOR=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

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=9

YEAR=2023

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2023.1156785

DOI=10.3389/fams.2023.1156785

ISSN=2297-4687

ABSTRACT=<p>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.</p>