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A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0003-2357-3795
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0002-3910-0418
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.ORCID iD: 0000-0001-8522-4705
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2024 (English)In: Accident Analysis and Prevention, ISSN 0001-4575, E-ISSN 1879-2057, Vol. 200, article id 107555Article in journal (Refereed) Published
Abstract [en]

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84–165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

Place, publisher, year, edition, pages
Elsevier BV , 2024. Vol. 200, article id 107555
Keywords [en]
Car-to-pedestrian collision, Case-specific buck, Finite element simulations, Head injury, Impact bio-mechanics
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:kth:diva-344929DOI: 10.1016/j.aap.2024.107555Scopus ID: 2-s2.0-85188682260OAI: oai:DiVA.org:kth-344929DiVA, id: diva2:1848555
Note

QC 20240404

Available from: 2024-04-03 Created: 2024-04-03 Last updated: 2024-04-04Bibliographically approved

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Lindgren, NataliaZhou, ZhouLi, XiaogaiKleiven, Svein

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Huang, QiLindgren, NataliaZhou, ZhouLi, XiaogaiKleiven, Svein
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