Pedestrian Collision Safety Performance Prediction Method Based on Deep Learning Models

This study presents an interpretable surrogate framework for predicting pedestrian-leg injury severity that integrates high-fidelity finite-element (FE) simulations with a TabNet-based deep-learning model. We generated a parametric dataset of 3000 impact scenarios—covering ten vehicle types and various legform impactors—using automated FE runs configured via Latin hypercube sampling. After preprocessing and one-hot encoding of categorical features, we trained TabNet alongside Support-Vector Regression, Random Forest, and Decision-Tree ensembles. All models underwent hyperparameter tuning via Optuna’s Bayesian optimization coupled with repeated four-fold cross-validation (20 trials per model). TabNet achieved the best balance of explanatory power and predictive accuracy, with an average R2 = 0.94 ± 0.01 and RMSE = 0.14 ± 0.02. On an independent test set, 85%, 88%, and 90% of predictions for tibial acceleration, knee-flexion angle, and shear displacement, respectively, fell within ±20% of true peaks. SHAP-based analyses confirm that collision-point location and bumper geometry dominate injury outcomes. These results demonstrate TabNet’s capacity to deliver rapid, robust, and explainable injury predictions, offering actionable design insights for vehicle front-end optimization and regulatory assessment in early development stages.