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2025 (English)In: Traffic Injury Prevention, ISSN 1538-9588, E-ISSN 1538-957XArticle in journal (Refereed) Epub ahead of print
Abstract [en]
To combat the global fatality rates among vulnerable road users (VRUs), prioritizing research on head injury mechanisms and human tolerance levels in vehicle-to-VRU traffic collisions is imperative. A foundational step for VRU injury prevention is often to create virtual reconstructions of real-world collisions. Thus, efficient and trustworthy reconstruction tools are needed to make use of recent advances in accident data collection routines and Finite Element (FE) human body modeling. In this study, a comprehensive and streamlined reconstruction methodology, starting from a video-recorded accident, has been developed. The workflow, that includes state-of-the-art tools for personalization of human body models (HBMs) and vehicles, was evaluated and demonstrated through 20 real-world VRU collision cases. The FE models successfully replicated the vehicle damage that was observed in on-scene photographs of the post-impact vehicle, as well as impact kinematics captured in dash cam or surveillance recordings. The findings highlight how video evidence can considerably narrow down the number of plausible impact scenarios and raise the credibility of virtual reconstructions of real-world VRU collision events. More importantly, this study demonstrates how, with an efficient and systematic methodology, FE might be feasible also for large-scale VRU accident datasets.
Place, publisher, year, edition, pages
Informa UK Limited, 2025
National Category
Applied Mechanics
Research subject
Technology and Health; Applied and Computational Mathematics, Numerical Analysis
Identifiers
urn:nbn:se:kth:diva-359625 (URN)10.1080/15389588.2024.2449257 (DOI)001411806100001 ()2-s2.0-85216745142 (Scopus ID)
Funder
Vinnova, 2019-03386Swedish Research Council, 2020-04724Swedish Research Council, 2020-04496
2025-02-062025-02-062025-04-25Bibliographically approved