Driver Drowsiness Detection Based on Multisource Information

Driver drowsiness is one of the major causes of on-road accidents. Abnormal eye behavior, steering wheel activity, and vehicle trajectory during different drowsiness stages were studied in detail to overcome the limitations of single-sensor approaches. Some measures, such as percentage of eyelid closure, maximum close duration, and percentage of nonsteering were analyzed using analysis of variance (ANOVA) methods. Moreover, a two-stage data fusion framework was developed for the modeling combination of information from different sources. Fisher's linear discriminant was implied as the feature-level fusion method, and Dempster-Shafer evidence theory was introduced in the decision-level fusion process. The results suggest that the recognition system proposed here provided 90.7% accuracy. The reliability and accuracy of the fusion method were significantly higher than those of single sensors. (C) 2012 Wiley Periodicals, Inc.