Head injury risk prediction for vulnerable road users based on Chinese adult male head data - Ying Lu; Jun Bai; Yufa Liu; Yu Shu (Scientific Reports)
Abstract Prediction of injuries to vulnerable road users (VRUs) during the head-ground collision phase has been a long-standing challenges in accident modeling. This study aims to reveal the severity of head injury in vehicle-VRU collision (VVC) accidents and quantify the relationship between the head-ground collision (HGC) velocity and the injury levels of brain tissue with local human attributes. First, a finite element head model with Chinese human attributes was constructed and verified. The simulation model of the HGC was subsequently established and verified by comparison with the Nahum Experiment, Yoganandan experiment, and head-fall-to-ground (HFOG) experiments. Finally, regression models for the relationships between the HGC velocity and injury parameters of the brain tissue were constructed, and the optimal cutoff value of the HGC velocity was determined. Based on the results of the VVC accident reconstruction and case studies, these regression models and the cutoff value of the HGC velocity can accurately determine the severity of head injuries in pedestrians.