[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121684-en":3,"doc-seo-121684-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121684,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Adaptive restraint design for a diverse population through machine learning","Using population-based simulations and machine-learning, this study develops an adaptive vehicle restraint system that accounts for occupant anthropometric variation to improve safety balance across the whole population. Two thousand MADYMO full-frontal impact crash simulations at 35 mph were conducted with validated sedan and SUV models and a parametric occupant model, varying sex, stature, and BMI. A Gaussian-process surrogate predicts injury risk with uncertainty, while an optimization framework minimizes population injury risk and reduces differences among occupant subgroups.","TYPE Original Research PUBLISHED 10 August 2023  \nDOI 10. 3389/fpubh.2023.1202970  \nOPEN ACCESS  \nEDITED BY  \nAlexander Crizzle,  \nUniversity of Saskatchewan, Canada  \nREVIEWED BY  \nCorina Klug,  \nGraz University of Technology, Austria Rodney Rudd,  \nNational Highway Tra􀀈c Safety Administration, United States  \n*CORRESPONDENCE  \nWenbo Sun  \n [sunwbgt@umich.edu](sunwbgt@umich.edu)  \nRECEIVED 09 April 2023  \nACCEPTED 27 July 2023  \nPUBLISHED 10 August 2023  \nCITATION  \nSun W, Liu J, Hu J, Jin J, Siasoco K, Zhou R and Mccoy R (2023) Adaptive restraint design for a diverse population through machine learning. Front. Public Health 11:1202970 .  \ndoi: 10.3389/fpubh.2023.1202970  \nCOPYRIGHT  \n© 2023 Sun, Liu, Hu, Jin, Siasoco, Zhou and Mccoy. 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.  \nAdaptive restraint design for a diverse population through machine learning  \nWenbo Sun1*, Jiacheng Liu2 , Jingwen Hu1 , Judy Jin2 , Kevin Siasoco3 , Rongrong Zhou3 and Robert Mccoy3  \n1 University of Michigan Transportation Research Institute (UMTRI), College of Engineering, University of Michigan, Ann Arbor, MI, United States, 2 Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States, 3 Ford Motor Company, Dearborn, MI, United States  \nObjective: 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.  \nMethods: 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-processbased 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 di􀀀erence in injury risks among di􀀀erent occupant subgroups. The e􀀀ectiveness 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.  \nResults: 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.  \nConclusions: 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.  \nKEYWORDS  \nadaptive design, machine learning, safety balance, Gaussian process, optimization  \nIntroduction  \nMotor-vehicle crashes continue to be a public health problem in the United ","cbCailtFpKuRRQAM","https://ap.wps.com/l/cbCailtFpKuRRQAM","pdf",2362479,1,10,"English","en",105,"# Objective\n# Methods\n## Simulation setup and design variables\n## Machine-learning surrogate and optimization framework\n# Results\n# Conclusions\n# Introduction\n## Public health context and safety imbalance\n## Related adaptive restraint research","[{\"question\":\"What is the objective of the adaptive restraint design study?\",\"answer\":\"To use population-based simulations and machine-learning to develop an adaptive restraint system that accounts for occupant anthropometry variation and improves safety balance across the whole population.\"},{\"question\":\"How were simulations and occupant variation handled in the methods?\",\"answer\":\"The study ran 2,000 MADYMO full-frontal impact crash simulations at 35 mph using validated sedan and SUV restraint/vehicle models and a parametric occupant model, varying sex, stature, and body mass index.\"},{\"question\":\"What machine-learning and optimization approach was used to find the adaptive policy?\",\"answer\":\"A Gaussian-process surrogate model was trained to rapidly predict injury risks and uncertainty, and an optimization framework searched for the adaptive restraint design policy that minimizes population injury risk while keeping risks consistent across occupant subgroups.\"}]","Adaptive restraint design for a diverse population through machine learning | 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is the objective of the adaptive restraint design study?","Question",{"text":75,"@type":76},"To use population-based simulations and machine-learning to develop an adaptive restraint system that accounts for occupant anthropometry variation and improves safety balance across the whole population.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were simulations and occupant variation handled in the methods?",{"text":80,"@type":76},"The study ran 2,000 MADYMO full-frontal impact crash simulations at 35 mph using validated sedan and SUV restraint/vehicle models and a parametric occupant model, varying sex, stature, and body mass index.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine-learning and optimization approach was used to find the adaptive policy?",{"text":84,"@type":76},"A Gaussian-process surrogate model was trained to rapidly predict injury risks and uncertainty, and an optimization framework searched for the adaptive restraint design policy that minimizes 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