Path selection method of intelligent vehicle based on fuzzy big data game

In view of the traditional intelligent vehicle routing method, the problems of inaccurate selection, long time and low efficiency have always existed. We proposed a path selection method for intelligent vehicle based on fuzzy big data game. Through analysis of the modelling principle of intelligent transportation vehicle routing and the relevant principle of the least squares algorithm, we calculated the function of risk factors in the path selection of intelligent transportation vehicles and established the conditional constraint model for vehicle routing. By using the depth neural network method, the path congestion state was identified, and the intelligent vehicle routing database was established. The simulation results show that the extraction time and accuracy of the method are better than those of the traditional path selection method in the improved method.