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 language | English |
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Article number | 1156785 |
Journal | Frontiers in Applied Mathematics and Statistics |
Volume | 9 |
DOIs | |
Publication status | Published - 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