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Human Occupant Motion in Pre-Crash Scenario

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Software, (2022)Related to: Jonas Kneifl, Julian Hay, and Jörg Fehr (2021). Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme. Preprint. arXiv: 2110.13583 math.DS.
DOI: 10.18419/darus-2471

Zusammenfassung

This dataset provides simulation results from a high-fidelity human body model in a pre-crash scenario as well as a surrogate model to approximate those simulation results.The high-fidelity results contain the motion of all points describing the occupant at certain points of time for several scenarios. In each scenario, the accelerations of the vehicle center of gravity act on the model. The translational accelerations in x- and y-direction as well as the angular acceleration around the z-axis are considered.In addition to the occupants motion, the corresponding time vector and used vehicle acceleration are also part of the dataset.The surrogate model was created by combining dimensionality reduction using proper orthogonal decomposition along with a long short-term memory network for regression. A standalone script to evaluate the model is provided as well.

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