The predictive capacity of the MADYMO ellipsoid pedestrian model for pedestrian ground contact kinematics and injury evaluation
Citation:
Shang, S. and Masson, C. and Llari, M. and Py, M. and Ferrand, Q. and Arnoux, P.-J. and Simms, C., The predictive capacity of the MADYMO ellipsoid pedestrian model for pedestrian ground contact kinematics and injury evaluation, Accident Analysis and Prevention, 149, 105803, 2021Download Item:
Abstract:
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 aHIC (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.
Author's Homepage:
http://people.tcd.ie/csimms
Author: Simms, Ciaran
Type of material:
Journal ArticleSeries/Report no:
Accident Analysis and Prevention;149;
105803;
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Full text availableKeywords:
injury predictions, MADYMO ellipsoid pedestrian model, aHIC (approximate HIC) and BrIC score, Pedestrians, Vehicle contact, Ground contactDOI:
http://dx.doi.org/10.1016/j.aap.2020.105803Metadata
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