Machine Learning based Meta-Analysis of IIHS Moderate Overlap Frontal Crash Test for Rear Seat Occupant Safety Performance

A machine learning (ML)-based meta-analysis was conducted to evaluate rear seat occupant safety performance in the Insurance Institute for Highway Safety (IIHS) Moderate Overlap Frontal (MOF) 2.0 crash test. ML models were trained on historical IIHS crash test data to predict rear passenger injury metrics using vehicle architecture, restraint system characteristics, crash pulse parameters, and vehicle kinematics as input features. The models demonstrated high predictive accuracy and were subsequently used in a Sobol sensitivity analysis to identify critical design parameters influencing injury outcomes. The analysis revealed that rear passenger injury metrics were most sensitive to restraint system parameters. Specifically, crash pulse magnitude was the dominant factor for head injury metrics, pretensioner activation time for neck tension force, and lap belt force for the Neck Injury Criterion (Nij). For chest-related metrics—sternum deflection, dynamic belt position, and maximum belt position—the initial belt position emerged as the most influential factor. This study demonstrates the potential of ML models to uncover dominant injury mechanisms and critical design parameters without explicitly encoding biomechanical knowledge. The findings also offer actionable insights to guide future vehicle safety design improvements for rear seat occupants.