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The predictive capacity of the MADYMO ellipsoid pedestrian model for pedestrian ground contact kinematics and injury evaluation.

Authors :
Shang, Shi
Masson, Catherine
Llari, Maxime
Py, Max
Ferrand, Quentin
Arnoux, Pierre-Jean
Simms, Ciaran
Source :
Accident Analysis & Prevention. Jan2021, Vol. 149, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

• This paper presents the first detailed assessment of the capacity of the MADYMO pedestrian model to replicate ground contact. • Sensitivity studies showed substantial influences of the vehicle-pedestrian contact characteristic on the ground contact kinematics. • The predictive capacity of the MADYMO pedestrian model for ground contact injury predictions needs to be improved. Pedestrian injuries occur in both the primary vehicle contact and the subsequent ground contact. Currently, no ground contact countermeasures have been implemented and no pedestrian model has been validated for ground contact, though this is needed for developing future ground contact injury countermeasures. In this paper, we assess the predictive capacity of the MADYMO ellipsoid pedestrian model in reconstructing six recent pedestrian cadaver ground contact experiments. Whole-body kinematics as well as vehicle and ground contact related a HIC (approximate HIC) and BrIC scores were evaluated. Reasonable results were generally achieved for the timings of the principal collision events, and for the overall ground contact mechanisms. However, the resulting head injury predictions based on the ground contact HIC and BrIC scores showed limited capacity of the model to replicate individual experiments. Sensitivity studies showed substantial influences of the vehicle-pedestrian contact characteristic and certain initial pedestrian joint angles on the subsequent ground contact kinematics and injury predictions. Further work is needed to improve the predictive capacity of the MADYMO pedestrian model for ground contact injury predictions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00014575
Volume :
149
Database :
Academic Search Index
Journal :
Accident Analysis & Prevention
Publication Type :
Academic Journal
Accession number :
147405634
Full Text :
https://doi.org/10.1016/j.aap.2020.105803