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Development of a model for the prediction of occupant loads in vehicle crashes: introduction of the Real Occupant Load Criterion for Prediction (ROLC\(_p\))

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Abstract

The objective in the development of passive vehicle safety systems is to protect the occupants in case of an accident. The severity of injuries experienced by the occupants are, among other factors, evaluated based on sensor signals from instrumented dummies in crash tests. Dummy signals, the so-called occupant loads, highly depend on the properties of vehicle structure and restraint systems. These properties need to be defined in very early stages of the development process. To support the engineers in their decision process, different metrics are used to evaluate the vehicle deceleration, the so-called crash pulse. These metrics do not consider the influences of vehicle-specific restraint system properties and can therefore only be used for pulse characterization. They are not suitable to make statements about the expected occupant loads in a crash test. For an efficient design of the passive safety systems, it is important to gain insights on the interaction between vehicle structure and restraint system properties in early stages of the development process. To predict occupant loads based on information, which is available in these early phases, a new method, the Real Occupant Load Criterion for Prediction (ROLC\(_p\)), is presented. By considering the vehicle pulse and specific restraint system properties in its calculation, the ROLC\(_p\) shows good correlation with the dummy’s maximum chest acceleration. As the ROLC\(_p\) can be used in early design phases, it represents a useful tool to improve the current vehicle safety development process.

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Acknowledgements

We gratefully thank Mr. Werner Langner for his useful advice and many fruitful discussions during this research. We would also like to thank all the students involved in this project for their motivation and dedication, especially Mr. Alvaro Otero and Mr. Steffen Beigang.

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Authors and Affiliations

  1. Dr. Ing. h.c. F. Porsche AG, Stuttgart, Germany

    Maximilian Rabus & Simon Alexander Maurer

  2. IDIADA Fahrzeugtechnik GmbH, Ingolstadt, Germany

    Mohamed Karim Belaid

  3. Fraunhofer Ernst-Mach-Institute for High-Speed Dynamics, Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany

    Stefan Hiermaier

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  1. Maximilian Rabus
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Correspondence to Maximilian Rabus.

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Annex 1: Input features for the prediction pipeline

Annex 1: Input features for the prediction pipeline

Table 3 provides an overview of all input features describing the vehicle, test setup and restraint system configuration used in the final prediction pipeline (after data preprocessing and feature selection) for the parameters \(x_{1p}\) and \(x_{2p}\).

Table 3 Input features for the prediction pipeline of \(x_{1p}\) and \(x_{2p}\)
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Rabus, M., Belaid, M.K., Maurer, S.A. et al. Development of a model for the prediction of occupant loads in vehicle crashes: introduction of the Real Occupant Load Criterion for Prediction (ROLC\(_p\)). Automot. Engine Technol. 7, 229–244 (2022). https://doi.org/10.1007/s41104-022-00111-x

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