Two-Stage Probabilistic Modeling to Study the Influence of Uncertainties in Occupant Seating Position and Seatbelt Routing on Occupant Loads

To assess crashworthiness of vehicles, it is important to consider the inherent variability of physical crash tests in the virtual design, i.e., before the first physical tests, through robustness evaluations. For this, forward uncertainty propagation methods have to be established based on computational models of the vehicle for relevant load cases. In the context of occupant safety, the seating position of an anthropometric test device (ATD) and the seatbelt routing in the initial state are influential uncertain parameters for injury criteria. For this purpose, we propose a two-stage probabilistic modeling approach to incorporate these aleatoric uncertainties, based on test data, into the respective computational models for stochastic crash simulations. The ATD seating position is modeled using linear Gaussian Bayesian networks, and the seatbelt routing is represented by multitask Gaussian process models. This method enables the generation of physically consistent geometric variations that are automatically transferred into adapted finite-element models. The approach is applied in a generic frontal crash scenario with the THOR-50M ATD. The results highlight that variations in seatbelt routing affect chest compressions, while uncertainties in ATD seating position significantly influence lower body loading. The diagonal belt path is identified as the dominant factor for chest injury criteria, whereas the horizontal hip point location and correlated leg posture govern pelvis and femur loads. The findings underline the importance of probabilistic modeling geometric uncertainties in virtual robustness assessments of occupant protection systems.