Effect of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents using deep learning
Effect of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents using deep learning
复制标题
利用深度学习研究行人体质差异对车行事故头部伤害预测的影响
DOI：
10.1080/15389588.2021.1981886
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发表时间：
2021
影响因子：
2
通讯作者：
Takayama Shinichi
中科院分区：
文献类型：
--
作者：
Kunitomi Shouhei;Takayama Shinichi
本研究的目的是通过深度learning.MethodsA系列的参数研究进行了使用家用汽车有限元模型和MADYMO行人模型（AM 50，AF 05，6 YO）的行人体质差异对头部损伤预测的影响。汽车模型是通过11次碰撞试验开发和调整的。行人模型的初始步态是从志愿者实验中获得的，以再现420个碰撞前的反应。此外，通过将行人模型（3种类型）、行人方向（各2种）、碰撞位置（各3种）和汽车速度（6个级别）与碰撞前参数相结合，共进行了45，360次汽车与行人碰撞模拟。在模拟之后，通过用15 ms（HIC）的头部损伤标准标记行人碰撞图像并基于模型类型将图像划分为训练数据和测试数据来创建图像数据集。接下来，使用训练数据集进行深度学习以获得训练模型。最后，行人体质差异对头部伤害预测的影响进行了调查的基础上，每个训练模型的测试data.ResultsThe结果表明，头部的影响区域和图像中的行人信息量的不同取决于行人模型的准确性。在深度学习的头部损伤预测中，AF 05的预测准确率最高（93.25%），其次是AM 50（90.61%）和6 YO（88.29%）。这些使用深度学习的结果表明，行人体质差异对头部伤害预测准确率的影响为2.32-4.96 points.ConclusionsBased on the prediction results of the trained models that learned the relationships between the pedestrian collision images and HIC from simulations，我们证明了深度学习方法在成人男性、体型较小的女性和儿童头部伤害预测中的理想性能。此外，我们的研究结果证实了行人体质差异对伤害预测准确性的影响。
ObjectiveThe aim of this study is to identify the effects of pedestrian physique differences on head injury prediction in car-to-pedestrian accidents via deep learning.MethodsA series of parametric studies was carried out using a family car finite element model and MADYMO pedestrian models (AM50, AF05, 6YO). The car model was developed and tuned by 11 impact tests. The initial gaits for the pedestrian models were obtained from volunteer experiments to reproduce 420 pre-crash reactions. Furthermore, by factoring the pedestrian models (3 types), pedestrian directions (2 each), impact positions (3 each), and car velocities (6 levels) with the pre-crash parameters, a total of 45,360 car-to-pedestrian impact simulations were performed. After the simulations, image datasets were created by labeling the pedestrian collision images with head injury criteria of 15 ms (HIC) and dividing the images into training and test data based on model type. Next, deep learning was conducted using the training dataset to obtain trained models. Finally, the effects of pedestrian physique differences on head injury predictions were investigated based on the accuracy of each trained model for test data.ResultsThe results indicate that the head impact area and the amount of pedestrian information in the image differ depending on the pedestrian models. In head injury prediction with deep learning, AF05 showed the highest prediction accuracy (93.25%), followed by AM50 (90.61%) and 6YO (88.29%). These results using deep learning show that pedestrian physique differences affect the head injury prediction accuracies by 2.32–4.96 points.ConclusionsBased on the prediction results of the trained models that learned the relationships between the pedestrian collision images and HIC from simulations, we demonstrated the desirable performance of deep learning methods in head injury prediction for adult men, women with small physique, and children. Furthermore, our results confirmed the effect of pedestrian physique differences on the injury prediction accuracy.