Prediction of the classification of the pedestrian landing mechanism in pedestrian-vehicle collisions

Abstract

To predict the classification of the pedestrian landing mechanism in pedestrian-vehicle collisions, 1303 reconstructed real pedestrian-vehicle collision cases were selected, and relevant data from before, during, and after the collisions were extracted. A total of 1303 sets of data with eight parameters were obtained via significance analysis, correlation analysis, and collinearity analysis. Then, the Backpropagation Neural Network (BPNN), Genetic Algorithm (GA) optimized BPNN (GA-BPNN), Principal Component Analysis (PCA) optimized BPNN (PCA-BPNN), Principal Component Analysis (PCA) and Genetic Algorithm (GA) optimized BPNN (PCA-GA-BPNN) were used to construct prediction models for the classification of the pedestrian landing mechanism, and the prediction effects were evaluated. The PCA-GA-BPNN model was found to be the optimal model; the prediction accuracies of the pre-collision, in-collision, and post-collision models were 72.4%, 96.4%, and 96.8%, respectively. Further analysis revealed that the optimal model could also accurately predict the classification of the pedestrian landing mechanism in six cadaver experiments. Additionally, the ratio of the pedestrian height to the vehicle hood height ( R P-V) was found to have an impact on the prediction effect of the model. Thus, an improved model considering R P-V was proposed, and was found to significantly improve the prediction accuracy of pedestrian forward-throwing mechanism. The research results provide new ideas for ground-related injury prediction, and also provide support for pedestrian protection in intelligent vehicles.