A Deterministic Feedback Model for Safe Driving based on Nonlinear Principal Analysis Scheme

For intelligent driver assistant service, research works have been conducted using sensors to analyze drivers' behavior and score for safe driving. There have been many studies on safe driving, but there was a limit on providing the driver with a comprehensive solution for safe driving. The studies mainly focused on a method to quantify the driving characteristics or the indicators that can be used to assess the driving risk. In order to practically utilize those indicators, it is necessary to provide the driver with the feedback on how to change which controllable factors to fix the indicators to desired values. However, there has been no study to provide feedback on interpretable factors. To improve the problem, in this paper, we propose a model that provides comprehensive feedback to the driver by offering quantitative driving improvement instructions. In the proposed model, K-means clustering is used to classify the safe driving level and the non-linear principal component analysis (NLPCA) model is trained by the classified low-risk data to analyze arbitrary driving data and provide feedback. To evaluate proposal model, we collected sensor data while driving vicinity of Daejeon Metropolitan City in Korea, and analyzed the principal component extracted using the NLPCA. We evaluated the classification accuracy of the principal components to verify the validity of the proposed model, and showed that the characteristics of safe driving can be represented through the proposal feedback model.