An approach about dangerous driving behavior recognition was proposed to solve the problem of uncertainty and complex in driver's behavior identification. This approach was based on a combination of Dempster-Shafer (D-S) evidence theory with entropy weight grey incidence considering both the comprehension of different indexes and the uncertainty of different goals. The weights of indexes were calculated by entropy theory to determine uncertainty reliabilities with grey relation analysis and to construct a Mass Functions for different goals. Dangerous driver status was identified based on Dempster synthesis rule with D-S theory evidence that integrated Mass functions. An expert evaluation method based on facial video was used to judge driving behaviors. The experimental results show that the method provides the recognition accuracy of 91.25% under high-speed condition. Therefore, the reliability and accuracy of the identification method are significantly higher than those of single sensor are.%为解决驾驶员疲劳驾驶险态辨识的复杂不确定性问题,提出了一种疲劳驾驶行为状态辨识方法.该方法基于熵权灰色关联和Dempster-Shafer (D-S)证据理论,兼顾处理了不同指标的综合性与标识目标的不确定性.利用熵权理论计算指标权重,运用灰色关联分析法,确定各指标的不确定信度,构建不同目标的Mass函数;基于D-S证据理论的Dempster证据合成法则,融合Mass函数,实现驾驶员疲劳驾驶行为险态辨识;运用面部视频的专家评价判断方法检验辨识方法.试验结果表明:该方法在高速工况下识别精度达91.25%.因而,与基于单传感器的检测方法相比,有效提高了驾驶行为辨识的准确性、可靠性.
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