Predicting head impact conditions in vehicle-to-pedestrian impacts through computational human modeling
This study generated a virtual database of pedestrian impacts with a wide range of vehicle front-end geometries, and developed prediction models to use vehicle front-end geometry, pedestrian size, impact speed, and WAD to predict pedestrian HIT, head contact velocity, and head contact angle.
Authors
- Jingwen Hu (ORCID: https://orcid.org/0000-0001-6477-0360)
- Chin-Hsu Lin
- Yang-Shen Lin
- Kyle Boyle
- Whitney Tatem
- Peter Martin (ORCID: https://orcid.org/0000-0003-3395-8656)
- Anne Bonifas
Institutions
- General Motors (United States) (US)
- Michigan Department of Transportation (US)
- National Highway Traffic Safety Administration (US)
- University of Michigan (US)
- General Motors (India) (IN)
- General Motors (Poland) (PL)
Publication Details
- Journal
- Traffic Injury Prevention
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1080/15389588.2025.2547041
- Citations
- 1
- Primary Topic
- Automotive and Human Injury Biomechanics
- Type
- article
- Field-Weighted Citation Impact
- 1.52