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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.