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Prediction of Pedestrian Head Injury from Automotive Hoods Based
on Deep Neural Networks
LI Bo1
, CHEN Zhiping1
, HUANG Zhuo1
, MENG Fanliang1
, WANG Weiwei1,2, HE Yi2
2026, 51(11):
9-15.
DOI: 10.16638/j.cnki.1671-7988.2026.011.002
In vehicle-pedestrian collisions, the impact response between the pedestrian's head and the
engine hood (hereinafter referred to "hood") is a key factor in automotive passive safety design. To
address the low computational efficiency and long iteration cycles of traditional finite element
simulation methods in new vehicle development, this paper proposes a deep learning–based rapid
prediction model for the head injury criterion (HIC). An input vector consisting of six key parameters
is constructed, including the ground clearance of the impact point on the outer panel Z1, the ground clearance of the corresponding point on the inner panel Z2, the hard-point gap Z3, the outer hood panel
thickness T1, the inner hood panel thickness T2, and impact headform category. Fully connected
layers combined with residual activation modules are used for feature encoding, and a convolutional
neural network is employed to achieve spatial aggregation of local structural features around the
impact point. In this way, an end-to-end mapping relationship from hood structural parameters to
HIC values is established, enabling rapid prediction of pedestrian head injury caused by the hood.
The results show that, within the hood test area defined by the China-new car assessment program
(C-NCAP) safety standard, the proposed prediction model achieves an average accuracy of 81.6%,
representing a 39% improvement compared with traditional support vector regression methods, thus
providing reliable technical support for rapid safety assessment during the hood development stage.
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