Design and Application of Deep Learning-Based Crash Damage Prediction Model for Self-Driving Cars

Abstract

The collision damage of automated cars has grown in importance as self-driving car technology has advanced to the pilot operation stage. The study builds a collision damage prediction model for automated driving cars, optimized deep convolutional neural networks using the self-attention mechanism, and designs a degree convolutional neural network algorithm incorporating the attention mechanism in order to avoid the dangers that will be encountered on the way to automated driving in advance. The findings demonstrated that the four index values of the modified algorithm in the calculation of the index were, respectively, 94.0%, 94.8%, 93.6%, and 0.88, with higher overall performance. The prediction model's accuracy during training on the training data set and validation data set was 100% and 98%, respectively, demonstrating its efficacy. The prediction model's prediction accuracy in calculating the degree of auto collision damage for 10 working conditions in the validation dataset is 83.3%, and the prediction results are essentially consistent with the trend of the actual collision damage degree curve, demonstrating both the viability and high prediction accuracy of the prediction model. The aforementioned findings demonstrated the model's strong performance and great application value in the field of self-driving car collision avoidance and warning.