Dataset of stochastic human body model simulations in frontal collisions

General remarks These are the simulation results supplementing the PhD thesis of Felix Ressi (DOI: 10.3217/7bge8-ffb75). A detailed description of the simulations and subsequent injury analysis can be found there. The simulations were performed using a modified version of the generic vehicle interior developed by Johan Iraeus. The original model can be found here at openvt.eu. In addition, four detailed human body models were used: THUMS v4.1 5th percentile female THUMS v4.1 50th percentile male VIVA+ 50th percentile female VIVA+ 50th percentile male The THUMS models are available free of charge from Toyota and the open source VIVA+ models are available at openvt.eu. The specific VIVA+ version used for these simulations can be found in this branch. The input for the simulations were identical for each HBM, apart from the seat position. In the conventional driving (dynamic driving task - DDT) position, each model was positioned based on a regression model. To add some scatter to the resulting seat position, it was varied between 0 mm and 25 mm behind the predicted position. For the autonomous driving (AD) position, the predicted longitudinal seat position of the VIVA+ 50th percentile male was used as a baseline for all HBMs, from which the seat was moved rearwards between 150 mm and 250 mm. Hence, in the AD simulations, all HBMs were in identical seat positions longitudinally. All input parameters for the 200 simulations, which were performed with each HBM in both seat configurations (resulting in 1600 simulations overall), are provided in the simulation_matrix.csv. Based on the value in "Seat position factor" [0, 1], the seat position for the DDT posture [0-25 mm] (relative to each HBMs driving posture) or AD posture [150-250 mm] (realtive to the DDT seat position for the VIVA+ 50M) was calculated. Criteria results DDT position - dynamic driving task (i.e. conventional driving) position AD - autonomous driving position (conventional seat back angle, but seat moved rearwards between 150 mm and 250 mm) The "results" dataframes consist of 200 rows (one for each simulation variant) and 11177 columns, with the DDT data using IDs 1-200 and the AD data IDs 201-400 (facilitating potential merging of the dataframes). The data can be read into a `pandas` dataframe by using the following line: df = pd.read_csv("results_DDT_position.csv", header=[0,1], index_col=0) This creates a MultiIndex column dataframe, which holds the data for all four HBMs used in the simulatio