Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
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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.