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.

Abstract: Recent research in non-intrusive data-driven model order reduction (MOR) enabled accurate and efficient approximation of parameterized ordinary differential equations (ODEs). However, previous studies have focused on constant parameters, whereas time-dependent parameters have been neglected. The purpose of this paper is to introduce a novel two-step MOR scheme to tackle this issue. In a first step, classic MOR approaches are applied to calculate a low-dimensional representation of high-dimensional ODE solutions, i.e. to extract the most important features of simulation data. Based on this representation, a long short-term memory (LSTM) is trained to predict the reduced dynamics iteratively in a second step. This enables the parameters to be taken into account during the respective time step. The potential of this approach is demonstrated on an occupant model within a car driving scenario. The reduced model’s response to time-varying accelerations matches the reference data with high accuracy for a limited amount of time. Furthermore, real-time capability is achieved. Accordingly, it is concluded that the presented method is well suited to approximate parameterized ODEs and can handle time-dependent parameters in contrast to common methods.