Essential feature extraction of driving behavior using a deep learning method

Abstract

Driving behavior can be represented by many different types of measured sensor information obtained through a control area network. We assume that the measured sensor information is generated from several hidden time-series data through multiple nonlinear transformations. These hidden time-series data are statistically independent of each other and capture essential driving behavior. Driving behavior information is usually generated by multiple nonlinear transformations that fuse essential features, e.g., "Yaw rate" is generated by fusing the velocity of the vehicle and the change of driving direction. However, driving behavior data is often redundant because such data includes multivariate information and involves duplicated essential features. In this paper, we propose a feature extraction method to extract essential features from redundant driving behavior data using a deep sparse autoencoder (DSAE), which is a deep learning method. Two-dimensional features are extracted from seven-dimensional artificial data using a DSAE and are determined experimentally to be highly correlated with the prepared essential features. DSAEs are also used to extract features from an actual driving behavior data set. To verify a DSAE's ability to extract essential driving behavior features and filter out redundant information, we prepare twelve data sets that include some or all of the driving behavior information. Twelve DSAEs are used to independently extract features from the twelve prepared data sets, and canonical correlation analysis is used to analyze the canonical correlation coefficients between extracted features. Furthermore, we verify DSAEs' ability to extract essential driving behavior features from the redundant driving behavior data sets.