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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.01530 |
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| _version_ | 1866917975551901696 |
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| author | Baek, Changmin Cho, Junik Lee, Dongjin |
| author_facet | Baek, Changmin Cho, Junik Lee, Dongjin |
| contents | This work presents a Gaussian Process (GP) modeling method to predict statistical characteristics of injury kinematics responses using Human Body Models (HBM) more accurately and efficiently. We validate the GHBMC model against a 50\%tile male Post-Mortem Human Surrogate (PMHS) test. Using this validated model, we create various postured models and generate injury prediction data across different postures and personalized D-ring heights through parametric crash simulations. We then train the GP using this simulation data, implementing a novel adaptive sampling approach to improve accuracy. The trained GP model demonstrates robustness by achieving target prediction accuracy at points with high uncertainty. The proposed method performs continuous injury prediction for various crash scenarios using just 27 computationally expensive simulation runs. This method can be effectively applied to designing highly reliable occupant restraint systems across diverse crash conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting passenger injury distributions under uncertainty variables using Gaussian process modeling with GHBMC Baek, Changmin Cho, Junik Lee, Dongjin Applications This work presents a Gaussian Process (GP) modeling method to predict statistical characteristics of injury kinematics responses using Human Body Models (HBM) more accurately and efficiently. We validate the GHBMC model against a 50\%tile male Post-Mortem Human Surrogate (PMHS) test. Using this validated model, we create various postured models and generate injury prediction data across different postures and personalized D-ring heights through parametric crash simulations. We then train the GP using this simulation data, implementing a novel adaptive sampling approach to improve accuracy. The trained GP model demonstrates robustness by achieving target prediction accuracy at points with high uncertainty. The proposed method performs continuous injury prediction for various crash scenarios using just 27 computationally expensive simulation runs. This method can be effectively applied to designing highly reliable occupant restraint systems across diverse crash conditions. |
| title | Predicting passenger injury distributions under uncertainty variables using Gaussian process modeling with GHBMC |
| topic | Applications |
| url | https://arxiv.org/abs/2504.01530 |