| Vehicle–pedestrian collisions pose a significant risk to Vulnerable Road Users (VRUs), with head injuries being among the most severe outcomes. Accurate prediction of the pedestrian head impact location and collision time is essential for the effective deployment of safety mechanisms such as pedestrian airbags and active hood systems. Traditional crash analysis relies on high-fidelity Finite Element Analysis (FEA) simulations, which are computationally expensive and time-consuming. In this work, we investigate deep learning models as surrogate predictors for key collision parameters. Using a dataset generated from virtual crash simulations covering multiple vehicle profiles, impact velocities, and pedestrian configurations, we train models to predict the three-dimensional head impact coordinates and the collision time. We compare the performance of a Multilayer Perceptron (MLP) and a Kolmogorov–Arnold Network (KAN). Experimental results show that KAN significantly outperforms MLP, achieving lower test losses for both collision time (21.0 vs.\ 106.7) and head coordinate prediction (1149.9 vs.\ 6982.2). These results demonstrate the potential of KAN-based surrogate models to accelerate crash analysis and support the development of improved pedestrian safety systems. |
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