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Adaptive restraint design for a diverse population through machine learning
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 simulati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448517/ https://www.ncbi.nlm.nih.gov/pubmed/37637800 http://dx.doi.org/10.3389/fpubh.2023.1202970 |
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author | Sun, Wenbo Liu, Jiacheng Hu, Jingwen Jin, Judy Siasoco, Kevin Zhou, Rongrong Mccoy, Robert |
author_facet | Sun, Wenbo Liu, Jiacheng Hu, Jingwen Jin, Judy Siasoco, Kevin Zhou, Rongrong Mccoy, Robert |
author_sort | Sun, Wenbo |
collection | PubMed |
description | 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 and the associated uncertainties. An optimization framework was formulated to seek the optimal adaptive restraint design policy that minimizes the population injury risk across a wide range of occupant sizes and shapes while maintaining a low difference in injury risks among different occupant subgroups. The effectiveness of the proposed method was tested by comparing the population-wise injury risks under the adaptive design policy and the traditional state-of-the-art design. RESULTS: Compared to the traditional state-of-the-art design for midsize males, the optimal design policy shows the potential to further reduce the joint injury risk (combining head, chest, and lower extremity injury risks) among the whole population in the sedan and SUV models. Specifically, the two subgroups of vulnerable occupants including tall obese males and short obese females had higher reductions in injury risks. CONCLUSIONS: This study lays out a method to adaptively adjust vehicle restraint systems to improve safety balance. This is the first study where population-based crash simulations and machine-learning methods are used to optimize adaptive restraint designs for a diverse population. Nevertheless, this study shows the high injury risks associated with obese and female occupants, which can be mitigated via restraint adaptability. |
format | Online Article Text |
id | pubmed-10448517 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104485172023-08-25 Adaptive restraint design for a diverse population through machine learning Sun, Wenbo Liu, Jiacheng Hu, Jingwen Jin, Judy Siasoco, Kevin Zhou, Rongrong Mccoy, Robert Front Public Health Public Health 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 and the associated uncertainties. An optimization framework was formulated to seek the optimal adaptive restraint design policy that minimizes the population injury risk across a wide range of occupant sizes and shapes while maintaining a low difference in injury risks among different occupant subgroups. The effectiveness of the proposed method was tested by comparing the population-wise injury risks under the adaptive design policy and the traditional state-of-the-art design. RESULTS: Compared to the traditional state-of-the-art design for midsize males, the optimal design policy shows the potential to further reduce the joint injury risk (combining head, chest, and lower extremity injury risks) among the whole population in the sedan and SUV models. Specifically, the two subgroups of vulnerable occupants including tall obese males and short obese females had higher reductions in injury risks. CONCLUSIONS: This study lays out a method to adaptively adjust vehicle restraint systems to improve safety balance. This is the first study where population-based crash simulations and machine-learning methods are used to optimize adaptive restraint designs for a diverse population. Nevertheless, this study shows the high injury risks associated with obese and female occupants, which can be mitigated via restraint adaptability. Frontiers Media S.A. 2023-08-10 /pmc/articles/PMC10448517/ /pubmed/37637800 http://dx.doi.org/10.3389/fpubh.2023.1202970 Text en Copyright © 2023 Sun, Liu, Hu, Jin, Siasoco, Zhou and Mccoy. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Sun, Wenbo Liu, Jiacheng Hu, Jingwen Jin, Judy Siasoco, Kevin Zhou, Rongrong Mccoy, Robert Adaptive restraint design for a diverse population through machine learning |
title | Adaptive restraint design for a diverse population through machine learning |
title_full | Adaptive restraint design for a diverse population through machine learning |
title_fullStr | Adaptive restraint design for a diverse population through machine learning |
title_full_unstemmed | Adaptive restraint design for a diverse population through machine learning |
title_short | Adaptive restraint design for a diverse population through machine learning |
title_sort | adaptive restraint design for a diverse population through machine learning |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448517/ https://www.ncbi.nlm.nih.gov/pubmed/37637800 http://dx.doi.org/10.3389/fpubh.2023.1202970 |
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