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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.