Driving Behavior Analysis Using Sequential Sparse Auto-encoder and Gaussian Mixture Model
Based on driving data,driving behavior analysis methods can extract hidden driving behavior information and enable applications such as driving style recognition.With the development of sensor technology,the scale and dimensionality of driving data required by advanced driver assistance systems are constantly increasing,which poses great challenges for driving behavior analysis.However,this also poses challenges for data analysis.Therefore,efficient and accurate driving behavior analysis methods are becoming increasingly important for advanced driver assistance systems.A driving behavior analysis method based on sequential sparse auto-encoder and Gaussian mixture model was proposed for large-scale and high-dimensional driving data sets.First,the loss function of the sparse auto-encoder was modified to effectively extract the low-dimensional representations of driving data.Then,driving behavior was visualized by projecting the extracted features into the color space using linear mapping.Finally,driving style recognition was performed by clustering the extracted features using a Gaussian mixture model.The experiments on real driving data show that the proposed method can effectively extract differentiated driving features and outperform other methods in driving style recognition according to indicators such as silhouette coefficients,achieving efficient and accurate driving behavior analysis.
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