@article{ref1,
title="Driving Safety Monitoring Using Semisupervised Learning on Time Series Data",
journal="IEEE transactions on intelligent transportation systems",
year="2010",
author="Wang, Jinjun and Zhu, Shenghuo and Gong, Yihong",
volume="11",
number="3",
pages="728-737",
abstract="This paper introduces a dangerous-driving warning system that uses statistical modeling to predict driving risks. The major challenge of the research is how to discover the safe/dangerous driving patterns from a sparsely labeled training data set. This paper proposes a semisupervised learning method to utilize both the labeled and the unlabeled data, as well as their interdependence to build a proper danger-level function. In addition, the learned function adopts a continuous parametric form, which is more suitable in modeling the continuous safe/dangerous-driving state transitions in a practical dangerous-driving warning system. Our comprehensive experimental evaluations reveal that, in comparison with driving danger-level estimation using classification-based methods, such as the hidden Markov model (HMM) or the conditional random field algorithm, the proposed method requires less training time and achieved higher prediction accuracy.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2010.2050200",
url="http://dx.doi.org/10.1109/TITS.2010.2050200"
}