Driver Distraction Detection Using Semi-Supervised Machine Learning

作者：Liu, Tianchi^*; Yang, Yan^*; Huang, Guang-Bin^*; Yeo, Yong Kiang^*; Lin, Zhiping^*

来源：IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4): 1108-1120.

DOI：10.1109/TITS.2015.2496157

• Advanced driver assistance system
• driver distraction
• eye movement
• machine learning
• semi-supervised learning

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.

• 出版日期2016-4
• 单位东南大学