Finite element reconstruction of a vehicle-to-pedestrian impact

Traffic Inj Prev. 2020 Oct 12;21(sup1):S145-S147. doi: 10.1080/15389588.2020.1829911. Epub 2020 Nov 4.

Abstract

Objective: This study aims to reconstruct a real-world Crash Injury Research and Engineering Network vehicle-to-pedestrian impact to supplement the determination of pedestrian kinematics and injury causation.

Methods: A case involving a 46-year-old male pedestrian with a height of 163 cm and mass of 100 kg that was impacted by a 2019 Dodge Charger Pursuit police cruiser at an approximate velocity of 20.1 m/s was reconstructed. The case vehicle was represented by a rigid shell of a 2019 Dodge Charger vehicle exterior from an open-source database. The case pedestrian was represented by the Global Human Body Models Consortium (GHBMC) 50th percentile male simplified pedestrian human body model. The GHBMC model was isometrically scaled to a height of 163 cm and the external layer of flesh was morphed to a male reference geometry with the same age, height, and body mass index as the case pedestrian. The approximate location and position of the pedestrian at the time of impact was determined from case vehicle dashboard camera images and the pedestrian model was adjusted accordingly.

Results: Reconstruction kinematics aligned with proposed CIREN case kinematics. The GHBMC model predicted fractures of the left inferior ischiopubic ramus, superior pubic ramus, ilium, sacral ala, acetabulum, and right ilium.

Conclusions: Finite element reconstructions of real-world pedestrian impacts are useful for analyzing pedestrian kinematics and provide an effective tool for improving pedestrian impact injury analyses.

Keywords: CIREN; Finite element; crash; pedestrian; reconstruction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Biomechanical Phenomena
  • Finite Element Analysis*
  • Fractures, Bone / epidemiology
  • Human Body
  • Humans
  • Male
  • Middle Aged
  • Models, Biological
  • Pedestrians / statistics & numerical data*