湖南大学学报（自然科学版）2025，Vol.52Issue(10)：68-74,7.DOI:10.16339/j.cnki.hdxbzkb.2025207
深度学习算法在改款车型假人伤害评估中的应用研究
Application Study of Deep Learning Algorithm in Dummy Injury Assessment in Facelift Vehicle Development
摘要
Abstract
The convolutional neural network and long short-term memory(CNN-LSTM)model were utilized to carry out the application study of the dummy injury prediction with the curve variable input.To construct the training and testing dataset,the correlated 1D simplified car model and restraint system CAE model were introduced herein to collect the input and output responses,separately.Besides,Pytorch was used to construct both the dummy head acceleration and chest deflection CNN-LSTM prediction models,and the effect of sample number on the precision of the training model was further studied.The results indicate that the increase of training sample number could not further enhance the model performance once the training datasets reach a certain size.However,the R value of the predicted results could be over 0.85 with the training sample number coming to 50,which can satisfy the predictive accuracy requirements of engineering development.关键词
约束系统/深度学习/假人伤害评估/卷积神经网络-长短时记忆Key words
restraint system/deep learning/dummy injury assessment/convolutional neural network and long short-term memory分类
信息技术与安全科学引用本文复制引用
何恩泽,高伟钊,符志,曾祥杰,潘锋..深度学习算法在改款车型假人伤害评估中的应用研究[J].湖南大学学报（自然科学版）,2025,52(10):68-74,7.基金项目
重庆市科学技术局博士"直通车"科研项目(CSTB2023NSCQ-BSX0011),Chongqing Science and Technology Bureau Doctoral"Express"Program(CSTB2023NSCO-BSX0011) （CSTB2023NSCQ-BSX0011）
