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Distinguish method of fatigue state based on driving behavior wavelet analysis

机译:基于驾驶行为小波分析的疲劳状态判别方法

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Being a direct reflection of the drivers' states, the driving behavior research is getting widely attention recently. This paper presents a new identification method of fatigue driving state, which is obtained from driving behavior data analysis both about normal driving state and the fatigues ones. PERCLOS80 is utilized as reference to distinguish two different states. During identification process, the driving behavior data is dealt with wavelet transform. Then modulus maxima values and Lipschitz exponents which reflected smooth level of data signal are performed as index to identify driving states: normal or fatigue. Among various experimental driving behavior data, the error to driving center line is chosen as information source here, and the result shows remarkable identified effect.
机译:作为驾驶员状态的直接反映,驾驶员的驾驶行为研究近来得到了广泛的关注。本文提出了一种新的疲劳驾驶状态识别方法,该方法是通过对正常驾驶状态和疲劳驾驶状态的驾驶行为数据分析获得的。 PERCLOS80被用作区分两个不同状态的参考。在识别过程中,对驾驶行为数据进行小波变换处理。然后,将反映数据信号平滑水平的模量最大值和Lipschitz指数作为指标来识别行驶状态:正常还是疲劳。在各种实验驾驶行为数据中,此处选择到驾驶中心线的误差作为信息源,结果显示出明显的识别效果。

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