Automatic coding machine-based driving behavior heterogeneity feature identification method

The invention discloses an automatic coding machine-based driving behavior heterogeneity feature identification method, and relates to the field of intelligent driving analysis. The automatic coding machine-based driving behavior heterogeneity feature identification method considers capturing potential information of GPS and derivative data by building an automatic coding machine model in deep learning and performing behavior mode study and judgment through large-scale unsupervised clustering to realize the effect of performing modeling on driving behaviors by utilizing massive GPS data. For the ubiquitous problem of difficult multisource heterogeneous driving behavior data collection and fusion, a deep learning network can be built in a reliable, efficient and expandable mode; the methodhas the characteristics of high error tolerance, low cost, flexibility and the like; and traffic safety management and vehicle insurance industry, especially intelligent vehicle driving behavior analysis and quick test system