Fast calculating surrogate models for leg and head impact in vehicle-pedestrian collision simulations.

Objective: In previous research, a tool chain to simulate vehicle-pedestrian accidents from ordinary driving state to in-crash has been developed. This tool chain allows for injury criteria-based, vehicle-specific (geometry, stiffness, active safety systems, etc.) assessments. Due to the complex nature of the included finite element analysis (FEA) models, calculation times are very high. This is a major drawback for using FEA models in large-scale effectiveness assessment studies. Therefore, fast calculating surrogate models to approximate the relevant injury criteria as a function of pedestrian vehicle collision constellations have to be developed.

Method: The development of surrogate models for head and leg injury criteria to overcome the problem of long calculation times while preserving high detail level of results for effectiveness analysis is shown in this article. These surrogate models are then used in the tool chain as time-efficient replacements for the FEA model to approximate the injury criteria values. The method consists of the following steps: Selection of suitable training data sets out of a large number of given collision constellations, detailed FEA calculations with the training data sets as input, and training of the surrogate models with the FEA model's input and output values.

Results: A separate surrogate model was created for each injury criterion, consisting of a response surface that maps the input parameters (i.e., leg impactor position and velocity) to the output value. In addition, a performance test comparing surrogate model predictions of additional collision constellations to the results of respective FEA calculations was carried out. The developed method allows for prediction of injury criteria based on impact constellation for a given vehicle. Because the surrogate models are specific to a certain vehicle, training has to be redone for a new vehicle. Still, there is a large benefit regarding calculation time when doing large-scale studies.

Conclusion: The method can be used in prospective effectiveness assessment studies of new vehicle safety features and takes into account specific local features of a vehicle (geometry, stiffness, etc.) as well as external parameters (location and velocity of pedestrian impact). Furthermore, it can be easily extended to other injury criteria or accident scenarios; for example, cyclist accidents.