Real-time driver distraction detection is the core to many distraction countermeasures and fundamental for constructing a driver-centered driver assistance system. While data-driven methods demonstrate promising detection performance, a particular challenge is how to reduce the considerable cost for collecting labeled data. This paper explored semi-supervised methods for driver distraction detection in real driving conditions to alleviate the cost of labeling training data. Laplacian support vector machine and semi-supervised extreme learning machine were evaluated using eye and head movements to classify two driver states: attentive and cognitively distracted. With the additional unlabeled data, the semi-supervised learning methods improved the detection performance ( $G$-mean) by 0.0245, on average, over all subjects, as compared with the traditional supervised methods. As unlabeled training data can be collected from drivers' naturalistic driving records with little extra resource, semi-supervised methods, which utilize both labeled and unlabeled data, can enhance the efficiency of model development in terms of time and cost.
Driver Distraction Detection Using Semi-Supervised Machine Learning
IEEE Transactions on Intelligent Transportation Systems ; 17 , 4 ; 1108-1120
2016-04-01
2094285 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Driver Distraction Detection Using Semi-Supervised Machine Learning
Online Contents | 2016
|Driver Distraction Detection Using Semi-Supervised Machine Learning
Online Contents | 2015
|Real-Time Detection System of Driver Distraction Using Machine Learning
Online Contents | 2013
|