Predictive Analysis of Alertness Related Features for Driver Drowsiness Detection

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

Drowsiness during driving is a major cause of accidents of drivers which has socio-economic and psychological impact on the affected person. In Intelligent Transportation Systems (ITS), the detection of the drowsy and alert state of the driver is an interesting research problem. This paper proposed a novel method to detect the drowsy state of the driver based on three parameters, namely physiological, environmental and vehicular. The undertaken model proposes a simplistic approach and achieves comparable results to the state of the art with an ROC score of 81.28 and also elaborates on the specificity and sensitivity metrics.

Keywords

Multimodal Drowsiness Feature selection Machine learning SVM LDA XGBoost 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Cluster Innovation CentreUniversity of DelhiDelhiIndia

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