Adaptive restraint design for a diverse population through machine learning

ABSTRACT:

Objective

Using population-based simulations and machine-learning algorithms to develop an adaptive restraint system that accounts for occupant anthropometry variations to further enhance safety balance throughout the whole population.

Methods

Two thousand MADYMO full frontal impact crash simulations at 35 mph using two validated vehicle/restraint models representing a sedan and an SUV along with a parametric occupant model were conducted based on the maximal projection design of experiments, which considers varying occupant covariates (sex, stature, and body mass index) and vehicle restraint design variables (three for airbag, three for safety belt, and one for knee bolster). A Gaussian-process-based surrogate model was trained to rapidly predict occupant injury risks an

SUBMITTER: Sun W 

PROVIDER: S-EPMC10448517 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature