Leveraging machine learning for predicting human body model response in restraint design simulations

Abstract

The objective of this study was to leverage and compare multiple machine learning techniques for predicting the human body model response in restraint design simulations. Parametric simulations with 16 independent variables were performed. Ordinary least-squares (OLS), least absolute shrinkage and selection operator (LASSO), neural network (NN), support vector regression (SVR), regression forest (RF), and an ensemble method were used to develop response surface models of the simulations. The hyperparameters of the machine learning techniques were optimized through grid search and cross-validation to avoid under-fitting and over-fitting. The ensemble method outperformed other techniques, followed by LASSO, SVR, NN, RF, and OLS. Findings indicated that optimizing the metamodel hyper-parameters are essential to predict the optimum set of restraint design parameters.

Keywords:

• Metamodel
• machine learning
• restraint system optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the National Highway Traffic Safety Administration (NHTSA) under grant DTNH2215D00022-0004. The driver air bag, knee air bag, and seat belt models used in the front-seat simulations were provided by Joyson Safety Systems (JSS). Views or opinions expressed or implied are those of the authors and are not necessarily representative of the views or opinions of NHTSA or JSS.