Driving Fatigue Recognition Based on the Combination of Multimodal Features

Abstract

Effective identification of driving fatigue is extremely important for reducing casualties and property losses. Therefore, this paper proposes a driving fatigue recognition method based on the combination of multimodal features. Firstly, multimodal features based on photoplethysmography, eye state and vehicle motion are extracted according to the physiological and driving characteristics of driving fatigue. Secondly, C-SVM-RFE algorithm is used to optimize the features for improving the accuracy of the driving fatigue recognition. Finally, Support Vector Machine is used to establish the driving fatigue recognition model. In order to validate the driving fatigue recognition model, the photoplethysmography data, facial video data and vehicle motion data of 30 subjects in different driving states were collected, and then were processed using the above process. The results show that the model has a high accuracy in recognizing the fatigue state of the samples consisting of data from 30 subjects. It can be concluded that the method of driving fatigue recognition based on the combination of multimodal features can provide a means of driver monitoring for the traffic safety management, and reduce traffic accidents caused by driving fatigue.

Keywords: Driving Safety, Driving Fatigue Recognition, Multimodal features, Support Vector Machine

DOI: 10.54941/ahfe1005235