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A framework for rapid on-board deterministic estimation of occupant injury risk in motor vehicle crashes with quantitative uncertainty evaluation

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Abstract

Accurate on-board occupant injury risk prediction of motor vehicle crashes (MVCs) can decrease fatality rates by providing critical information timely and improving injury severity triage rates. The present implemented prediction algorithms in vehicle safety systems are probabilistic and rely on multi-variate logistic regression of real-world vehicle collision databases. As a result, they do not utilize important vehicle and occupant features and tend to overgeneralize the solution space. This study presents a framework to address these problems with deterministic and computationally efficient lumped parameter model simulations driven by a database of vehicle crash tests. A 648-case mixed database was generated with finite element and multi-body models and validated under the principal directions of impact with experimental results for a single vehicle body type. Using the finite element database, we developed lumped parameter models for four principal modes of impact (i.e., frontal, rear, near side and far side) with parameters identified via genetic algorithm optimization. To obtain confidence bounds for the injury risk prediction, the parameter uncertainty and model adequacy were evaluated with arbitrary and bootstrapped polynomial chaos expansion. The developed algorithm was able to achieve over triage rates of 17.1% ± 8.5%, whilst keeping the under triage rates below 8% on a finite element-multi body model database of a single vehicle body type. This study demonstrated the feasibility and importance of using low-complexity deterministic models with uncertainty quantification in enhanced occupant injury risk prediction. Further research is required to evaluate the effectiveness of this framework under a wide range of vehicle types. With the flexibility of parameter adjustment and high computational efficiency, the present framework is generic in nature so as to maximize future applicability in improved on-board triage decision making in active safety systems.

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Authors and Affiliations

  1. State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China

    Iason Bance, SaiChao Yang, Qing Zhou, ShengBo Li & BingBing Nie

Authors
  1. Iason Bance
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  2. SaiChao Yang
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  3. Qing Zhou
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  4. ShengBo Li
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  5. BingBing Nie
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Corresponding author

Correspondence to BingBing Nie.

Additional information

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 51705276 and 51675295), Tsinghua University Initiative Scientific Research Program (Grant No. 2019Z08QCX13), and the National Key R&D Program of China (Grant Nos. 2017YFE0118400 and 2018YFE0192900).

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The supporting information is available online at tech.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Bance, I., Yang, S., Zhou, Q. et al. A framework for rapid on-board deterministic estimation of occupant injury risk in motor vehicle crashes with quantitative uncertainty evaluation. Sci. China Technol. Sci. 64, 521–534 (2021). https://doi.org/10.1007/s11431-019-1565-9

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  • DOI: https://doi.org/10.1007/s11431-019-1565-9

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