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Vision-based estimation of driver drowsiness with ORD model using evidence theory

Driver drowsiness influences critically the driving safety and the lack of discerning the drowsy level precisely causes failure to take measures to prevent the accidents. In this paper, a novel intelligent surveillance system is proposed to estimate driver drowsiness based on the Observer Rating of Drowsiness (ORD) model integrated into evidence theory via fusion of lane and eye features. ORD is a subjective assessment of drowsiness that is reflected in people's physical appearance, behaviors and mannerisms. Its drowsiness model in five levels, which acts as the framework in evidence theory, is used to describe the driver's state. Based on expert knowledge and data statistics, various visual eye features are studied to enhance the robustness of this system. The system is validated in real world scenarios, and experiment results demonstrate that it is promising to improve the robustness and temporal response of driver surveillance in real-time.