자동차안전학회지 (Journal of Auto-vehicle Safety Association)

한국자동차안전학회 (Korean Auto-vehicle Safety Association)
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정면충돌 시험결과와 딥러닝 모델을 이용한 흉부변형량의 예측

Prediction of Chest Deflection Using Frontal Impact Test Results and Deep Learning Model

  • 이권희 (동아대학교 기계공학과) ;
  • 임재문 (대덕대학교 정밀기계공학과)
  • 투고 : 2023.01.27
  • 심사 : 2023.03.23
  • 발행 : 2023.03.31
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초록

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

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과제정보

이 논문은 동아대학교 교내연구비 지원에 의하여 연구되었음.

참고문헌

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