Mining driving safety pattern using semi-supervised learning on time series data

This paper introduces a driving danger-level warning system that uses statistical modeling to predict driving risks. The major challenge of the research is how to model the safe/dangerous driving patterns from a sparsely labeled training data set. This paper utilizes both the labeled and the unlabeled data as well as their interdependency 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 practical dangerous driving warning system. Our comprehensive experimental evaluations reveal that, in comparison with sequential classification based methods, the proposed method requires less training time and achieved higher prediction accuracy.