Citation

Wang J, Zhu S, Gong Y. IEEE Trans. Intel. Transp. Syst. 2010; 11(3): 728-737.

Copyright

(Copyright © 2010, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2010.2050200

PMID

unavailable

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.