关键词: 微型面包车；正面碰撞；BP 神经网络；MWOA；损伤预测

Abstract: In order to quickly predict the risk of head injury of occupants in road traffic accidents and solve the relationship between occupant injury and influencing factors, construct a driver-side injury prediction model based on modified whale optimization algorithm-back propagation (MWOA-BP) neural network. Select the minivan as the research model, the study condition is in frontal collision, and construct the injury prediction model which selects the initial velocity of the vehicle collision, seat belt use, and airbag deployment as inputs, and the driver's head abbreviated injury scale (AIS) as the prediction target. The MWOA-BP prediction model is trained, and the damage prediction effect is compared with the traditional back propagation (BP) neural network model. The results show that the MWOA-BP prediction model has good test effect, and the test accuracy is up to 90%. Combined with real accidents, the damage prediction model is applied to prove that the damage prediction model can be applied to actual accidents.

Key words: Minivan; Frontal collision; BP neural network; MWOA; Injury prediction