Abstract: 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.

Key words: hood; deep learning; pedestrian protection; head injury prediction

摘要： 在车辆与行人碰撞事故中，行人头部与汽车发动机盖（简称“发盖”）的冲击响应是汽 车被动安全设计的关键考量。针对传统有限元仿真方法在新型车开发周期中存在的计算效率 低、迭代周期长等问题，文章提出一种基于深度学习的头部损伤准则（HIC）快速预测模型。 通过构建由六个关键参数组成的输入向量，包括外板离地高度 Z1、内板离地高度 Z2、硬点间 隙 Z3、发盖外板厚度 T1、发盖内板厚度 T2 以及碰撞头型类别；采用全连接层与残差激活模块 进行特征编码，结合卷积神经网络实现碰撞点局部结构特征的空间聚合，建立从发盖结构参 数到 HIC 值的端到端映射关系，实现汽车发盖对行人头部伤害的快速预测。结果表明，本预 测模型在中国新车评价规程（C-NCAP）安全标准规定的发盖区域内达到了 81.6%的平均精度， 较传统支持向量回归方法提升了 39%，为发盖开发阶段的快速安全评估提供了可靠的技术支 持。

关键词: 发动机盖；深度学习；行人保护；头部伤害预测