Analysis of head injury risk in vehicle to pedestrian collisions using a large-scale simulation dataset

Head injury is the main cause of pedestrian death in traffic accidents, and rapid prediction of injury severity can win more time for accurate treatment and vehicle decisions. This paper aims to establish an applicable large-scale numerical database for the development of head injury prediction models. A refined multi-body vehicle model was first constructed to consider the influence of vehicle front-end stiffness properties. Then a Head Weighted Injury Study (HWIS) Database containing 60,750 simulations was established by using a full factorial design of experiments with car speed, car type, collision position, collision angle, pedestrian speed, pedestrian size, and pedestrian gait as input variables, and peak linear acceleration, HIC15, peak angular velocity and acceleration as outputs. The distribution of head impact locations and injury severity in the numerical database was counted, and the effects of different factors on the risk of head injuries were further qualitatively analyzed. The results showed that the head impact locations were distributed in almost all regions of the vehicle’s front-end, and the proportion of each head injury level exceeded 10%, which demonstrated the balance and applicability in the database. In vehicle to pedestrian collisions, pedestrian size significantly influences head linear and rotational kinematic metrics. Considering the scaling, the proportion of severe head injuries in the 6YOC and AF05 was 1.37 times and 1.39 times higher than in the AM50, respectively. This study established a reliable large-scale database for the development of head injury risk prediction models. Through analyzing the effects of various factors on head injury risk indicates that in headform testing, for regions with WAD values between 1,000 and 1,500 millimeters, the performance criteria for each grid point should be reevaluated to accommodate the HIC15 tolerance limits for children and small-size pedestrians.