Intelligent Damage Prediction During Vehicle Collisions Based on Simulation Datasets

Accurate prediction of vehicle damage in collision scenarios is crucial for enhancing road safety. However, traditional collision simulation methods are computationally intensive and time consuming. In this study, we proposed an intelligent damage prediction model that significantly reduces the computational time required for collision simulations by leveraging collision simulation datasets in conjunction with the random forest (RF) algorithm. A finite element model for vehicle collision simulation was first established. Subsequently, a dataset comprising 160 collision scenarios was generated by systematically varying the collision object, angle, offset, and speed, ensuring comprehensive coverage of vehicle damage data. The dataset was employed to construct an RF-based prediction model to estimate vehicle collision damage. Validation trials demonstrated that the proposed model achieved a mean absolute percentage error of 20.09% compared with 33.18% of a support vector machine regression (SVMR) model. The root-mean-square error of the proposed model was 33.94, whereas that of the SVMR model was 68.16. Compared with the SVMR model, the proposed RF model exhibited superior fitting performance, with reduced dispersion between the predicted and actual values. This enhanced model offers rapid damage prediction for trajectory planning systems and adaptive restraint systems in autonomous vehicles, ultimately contributing to enhanced road safety.