Reliability-based driver drowsiness detection using Dempster-Shafer theory

In this paper, driving drowsiness detection based on visual features offers a noninvasive solution to detect the driver´s state. Fusion with lane and driver features is addressed in order to complement each other once any visual signs failed. Given uncertainty exists greatly, Dempster-Shafer theory is used to improve the accuracy of detection while reliability is given to present the data´s robustness. Experimental results demonstrate that the performance of driving drowsiness vigilance is enhanced in the proposed framework and efficiently tolerates the failure of feature collection.