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Research on Occupant Injury Prediction Method in Vehicle Collision Based on Deep Learning
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Zehui Huang1, 2, Hongbin Tang1, 2, Xuesong Wang1, 2, Duo Han1, 2, Shibin Wang1, 2, Baichen Liu1, 2
Automotive Engineer | 2024, (4) : 8 - 11
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Automotive Engineer | 2024, (4): 8-11
• Special Topic on Passive Safety Technology •
Research on Occupant Injury Prediction Method in Vehicle Collision Based on Deep Learning
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Zehui Huang1, 2, Hongbin Tang1, 2, Xuesong Wang1, 2, Duo Han1, 2, Shibin Wang1, 2, Baichen Liu1, 2
Affiliations
  • 1 Global R&D Center, China FAW Corporation Limited, Changchun 130013
  • 2 National Key Laboratory of Advanced Vehicle Integration and Control, Changchun 130013
Published: 2024-04-15 doi: 10.20104/j.cnki.1674-6546.20230496
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To predict injury of the occupant in vehicle collision more rapidly and accurately, a training database for deep learning models was established based on frontal 100% overlap rigid barrier real-world collision data, and data preprocessing and features extraction were conducted. Deep learning models were constructed separately based on Long Short-Term Memory (LSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) neural network, and Temporal Convolutional Networks (TCN) for injury prediction training. The validation results show that the model prediction accuracy reaches 0.8579, 0.8209 and 0.9674, respectively, demonstrating feasibility of the proposed method.

Deep learning  /  Occupant injury prediction  /  Long Short-Term Memory (LSTM)  /  Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) neural network  /  Temporal Convolutional Network (TCN)
Zehui Huang, Hongbin Tang, Xuesong Wang, Duo Han, Shibin Wang, Baichen Liu. Research on Occupant Injury Prediction Method in Vehicle Collision Based on Deep Learning[J]. Automotive Engineer, 2024 , (4) : 8 -11 . DOI: 10.20104/j.cnki.1674-6546.20230496
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Year 2024 volume Issue 4
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doi: 10.20104/j.cnki.1674-6546.20230496
  • Online Date：2025-11-25
  • Published：2024-04-15
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  • Revised：2023-11-15
Affiliations
    1 Global R&D Center, China FAW Corporation Limited, Changchun 130013
    2 National Key Laboratory of Advanced Vehicle Integration and Control, Changchun 130013
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