Real-world pedestrian crash reconstructions: Vehicle model validation and biomechanical injury analysis.

Abstract

Objectives: The overarching objective of this study was to reconstruct five real-world pedestrian crashes using data from the Vulnerable Road User In-Depth Crash Investigation Study (VICIS) database, the Global Human Body Models Consortium (GHBMC) simplified pedestrian models, and morphed generic vehicle (GV) models reflecting U.S. vehicle front-end geometry to investigate pedestrian injury risks, compare simulated injury outcomes and contact kinematics with real-world observations, and evaluate the suitability of these simplified models for crash reconstruction.

Methods: Five real-world pedestrian crashes from VICIS were reconstructed based on injury distribution and test data availability. Cases included four males (ages 14, 48, 56, and 64) and one female (age 57). Vehicles included three sport utility vehicles (SUVs) and two sedans, impacting at an average speed of 47 kph (range: 16-65 kph). Sedan and SUV GVs were morphed using computer-aided design (CAD) models to match front-end geometry. The windshield was modeled as a three-layer structure with fracture-enabled outer glass layers. Morphed models were validated against Euro New Car Assessment Program (NCAP) headform, upper legform, and lower legform tests using correlation and analysis (CORA) ratings. The models were used to reconstruct crashes by applying initial velocity and scaling GHBMC pedestrian models to match the case pedestrian height and weight. The contact points from simulations were compared with real-world crash evidence. AIS2+ injuries from the cases were compared to reconstructed results using injury metrics and risk functions.

Results: The average ± SD CORA score for all pedestrian NCAP validation tests was 0.72 ± 0.1, indicating a good rating. Contact points from reconstructions closely matched real-world crashes. Brain injury criterion (BrIC) and cumulative strain damage measure (CSDM) injury risks (>90%) predicted cerebral injuries, while the Head Injury Criterion (HIC) injury risks remained low in two cases (<5%), underpredicting skull fractures. Chest deflection predicted thorax injury (injury risk >73%), whereas thoracic trauma index (TTI) risks were low (<50%). Tibia fractures from the cases were confirmed by injury risk estimations (>90%) using the revised tibia index (RTI).

Conclusions: The GV-based pedestrian crash reconstruction framework demonstrated strong potential for real-world crash studies. CAD-based morphing enabled close matching of case vehicle front geometry, and material/structural tuning enhanced model responses aligned with physical vehicle data. The results of the reconstruction matched well with the actual crash data.