Image Recognition and Decision Algorithm for Highway Foggy Driving Safety Assistance System

In this article, an innovative image recognition and decision-making algorithm is proposed to meet the severe challenge of highway traffic safety in foggy days. By deeply analyzing the characteristics of foggy images, an image recognition model combining convolutional neural network (CNN) and support vector machine (SVM) is designed to realize accurate recognition of vehicles, obstacles and traffic signs. On this basis, an intelligent decision algorithm is developed, which can make driving decisions quickly according to the recognition results. In order to verify the effectiveness of the proposed method, experiments are carried out and compared with the traditional CNN model. The results show that the CNN-SVM model proposed in this article significantly improves the recognition accuracy, the mean absolute error (MAE) decreases by 32.07%, and the recognition accuracy increases by 27.49%. The decision-making algorithm performs well in the simulated driving scene and can make decisions quickly and accurately. With the help of innovative image recognition and decision-making algorithm, the proposed system effectively improves the driving safety in foggy conditions and provides a new vision for the development of intelligent transportation system.