驾驶行为多重分形特征在驾驶疲劳检测中的应用

doi:10.13229/j.cnki.jdxbgxb20200061

Abstract

Abstract:

Driver fatigue caused by long-term driving is usually accompanied by a decline in driving performance. Therefore， driver fatigue threatens the safety of the drivers and passengers seriously. The development of driver fatigue detection technology can help remind the drivers and take relevant measures against driver fatigue， thereby improving traffic safety to a certain degree. The purpose of this paper is to improve the classification accuracy of the driver fatigue detection model. To achieve this goal， this paper introduces multi-fractal features of the driver behavior data. Six male subjects participated in the driving simulation experiment and UC-win/Road driving simulation software was used to collect driver behavior data such as driving speed， acceleration， steering wheel angle and steering wheel angular velocity. Indicators of the mean， the standard deviation and several different multi-fractal features of each kind of data were calculated. The changes of the drivers' subjective fatigue were also measured with a time interval of 600 s. And the relationship between the indicators and driver fatigue were measured. Additionally， the accuracies of driver fatigue detection models based on support vector machine （SVM） considering different features and different time window were compared. The research shows： The correlation between the singularity strength of acceleration （A₀） and driver fatigue is significant； Compared with the correlation between other indicators and driver fatigue， that between A₀ and driver fatigue is less affected by the time window width； The singularity strength of acceleration can help improve the accuracy of SVM； Compared with the time window of 15 s， a wider time window of 30 s can make the improvement more obvious. Therefore， it has certain application value in driver fatigue detection technology.

Key words: road and railway engineering, driver fatigue, traffic safety, multi-fractal features

CLC Number:

U492.8

Shu-wei ZHANG,Zhong-yin GUO,Zhen YANG,Ben-min LIU. Application of multi⁃fractal features of driving performance in driver fatigue detection[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 557-564.

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驾驶员编号	性别	年龄/岁	驾龄/年
A	男	28	4
B	男	30	7
C	男	33	7
D	男	46	14
E	男	49	21
F	男	55	30

指标	描述	15 s		30 s
指标	描述	均值	标准差	均值	标准差
SM	行驶速度均值/(m·s^-1)	27.769	12.400	27.764	12.441
AM	加速度均值/(m²·s^-1)	0.132	0.213	0.133	0.199
STM	方向盘转角均值	0.011	0.021	0.011	0.021
SVM	方向盘角速度均值	0.000	0.001	0.000	0.001
SSD	行驶速度标准差/(m·s^-1)	0.850	0.719	1.685	1.430
ASD	加速度标准差/(m²·s^-1)	0.079	0.092	0.112	0.095
STSD	方向盘转角标准差	0.002	0.005	0.003	0.005
SVSD	方向盘角速标准差	0.007	0.019	0.009	0.018

时间窗宽度/s	指标	Spearman相关系数
15	SM	0.417**
	AM	0.321**
	STM	0.005
	SVM	-0.093
	SSD	0.398*
	ASD	0.371*
	STSD	0.302*
	SVSD	0.523**
30	SM	0.306
	AM	0.317
	STM	0.019
	SVM	-0.107
	SSD	0.322
	ASD	0.329
	STSD	0.179
	SVSD	0.342

趋势函数	速度		加速度
趋势函数	拟合公式	R²	拟合公式	R²
DFA1	y=0.984x-5.127	0.954	y=0.800x-3.850	0.933
DFA2	y=0.880x-4.944	0.968	y=0.705x-3.687	0.939
DFA3	y=0.767x-4.739	0.984	y=0.585x-3.483	0.950
DFA4	y=0.664x-4.546	0.996	y=0.484x-3.311	0.956
DFA5	y=0.582x-4.390	0.997	y=0.410x-3.183	0.959
DFA6	y=0.532x-4.289	0.993	y=0.359x-3.094	0.959

指标描述	15 s		30 s
指标描述	均值	标准差	均值	标准差
速度序列的奇异强度S₀	1.953	0.182	1.938	0.202
速度序列的广义Hurst指数S₁	0.554	0.079	0.543	0.072
速度序列的标度指数S₂	0.108	0.159	0.086	0.145
加速度序列的奇异强度A₀	1.501	0.393	1.467	0.407
加速度序列的广义Hurst指数A₁	0.395	0.080	0.371	0.071
加速度序列的标度指数A₂	-0.210	0.160	-0.258	0.140

Application of multi⁃fractal features of driving performance in driver fatigue detection

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