Driver state recognition with physiological signals: Based on deep feature fusion and feature selection techniques

Abstract

Driver state constitutes a significant factor affecting traffic safety, and accurate detection of driver state can significantly enhance driving safety. Therefore, the objective of this investigation is to develop a high accuracy driver state recognition model driven by physiological data. Firstly, expert knowledge and deep learning algorithms were used to extract the shallow and deep features of the driver's electroencephalogram (EEG) and electrodermal activity (EDA) data, respectively; secondly, the principal component analysis (PCA) technique was employed to reduce dimensionality of deep features, enabling fusion with the shallow features; thirdly, an improved recursive feature elimination (RFE) algorithm based on Shapley additive explanation (SHAP) value was proposed for the issue of redundant features in the fused features, and a hybrid feature selection algorithm was devised in conjunction with the chi-square test; finally, the driver state was recognized based on machine learning algorithms. The findings demonstrate superior performance of the driver state recognition model built on the fused features compared to models constructed solely with shallow or deep features. Moreover, the application of the hybrid feature selection algorithm further improved the performance of the driver state recognition model, resulting in a satisfactory accuracy (94.88 %) and F1 score (94.86 %). This study highlights the efficacy of fusing deep features and conducting feature selection in enhancing the accuracy of the driver state recognition model.