基于ROC曲线和驾驶行为特征的驾驶愤怒强度判别阈值

doi:10.13229/j.cnki.jdxbgxb20180918

Abstract

Abstract:

In order to make effective graded warning for road rage, an optimal threshold determination method for discriminating driving anger intensity based on driving behavior features is proposed in this study. Different driving behavior data were acquired in driving states with different anger intensity by conducting timed experiments in busy traffic sections. The steering wheel rotation angel rate (SWRAR), speed of gas pedaling (SGP), and lateral acceleration (LA) were selected as three effective driving behavior features for discriminating different anger intensity by analysis of variance. A method of receiver operating characteristic (ROC) curve analysis was introduced to determine the optimal discrimination thresholds of SWRAR, SGP and LA. For normal state the ranges of the thresholds are 0≤SWRAR<1.8863, 0≤SGP<0.397 4, 0≤LA<0.136 2; for low anger state the ranges are 1.886 3≤SWRAR<2.324 1, 0.397 4≤SGP<0.505 4, 0.136 2≤LA<0.155 9; for moderate anger state the ranges are 2.324 1≤SWRAR<3.083 5, 0.505 4≤SGP<0.614 2, 0.155 9≤LA<0.175 4; and for high anger state the ranges are SWRAR $≥$ 3.083 5, SGP $≥$ 0.614 2, LA $≥$ 0.175 4. The verification performances show that the accuracy of the optimal thresholds for discriminating different anger intensity can reach 79.42%. The results can provide theoretical foundation for developing multilevel warning systems for driving anger based on the optimal discrimination thresholds.

Key words: road engineering, driving anger discrimination, receiver operating characteristic (ROC)curve, driving behavior, traffic safety

CLC Number:

U491

Ping WAN,Chao-zhong WU,Xiao-feng MA. Discriminating threshold of driving anger intensity based on driving behavior features by ROC curve analysis[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 121-131.

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驾驶行为指标	驾驶愤怒强度
	无	低	中	高
SGP/(m?s^-1)	0.289(0.124)	0.458(0.171)	0.579(0.211)	0.656(0.247)
SBP/(m?s^-1)	0.218(0.062)	0.224(0.066)	0.228(0.068)	0.233(0.071)
SWRAR/[(°)?s^-1]	1.316(0.142)	2.083(0.167)	2.658(0.191)	3.404(0.232)
FA/(m?s^-2)	0.469(0.121)	0.611(0.169)	0.736(0.197)	0.863(0.221)
LA/(m?s^-2)	0.127(0.036)	0.148(0.042)	0.164(0.051)	0.188(0.057)

驾驶行为指标		平方和	df	均方	F	显著性
SGP	组间	32.624 31	3	10.874 77	372.158 39	0.034
	组内	51.720 83	1770	0.029 22
	总数	84.345 14	1773
SBP	组间	0.046 29	3	0.015 43	3.624 64	0.126
	组内	7.534 47	1770	0.004 26
	总数	7.580 76	1773
SWRAR	组间	865.531 25	3	288.510 42	10042.519 16	0.028
	组内	50.850 13	1770	0.028 73
	总数	916.381 38	1773
FA	组间	32.047 02	3	10.682 34	404.607 89	0.083
	组内	46.731 02	1770	0.026 40
	总数	78.778 04	1773
LA	组间	0.707 95	3	0.235 98	126.299 67	0.042
	组内	3.307 14	1770	0.001 87
	总数	4.015 09	1773

愤怒等级	判别指标	AUC	Std.Error	Asymptotic Sig.	95%置信区间
愤怒等级	判别指标	AUC	Std.Error	Asymptotic Sig.	上限	下限
	SWRAR	0.821 9	0.031 4	0.034	0.769 7	0.842 8
等级1	SGP	0.835 4	0.035 1	0.029	0.752 4	0.853 1
	LA	0.792 5	0.028 7	0.042	0.704 8	0.821 6
	SWRAR	0.831 4	0.039 2	0.039	0.794 3	0.862 5
等级3	SGP	0.852 7	0.042 6	0.023	0.816 7	0.881 3
	LA	0.822 7	0.032 4	0.023	0.790 4	0.853 9
	SWRAR	0.853 6	0.041 2	0.037	0.810 3	0.884 9
等级5	SGP	0.873 4	0.052 8	0.018	0.821 8	0.904 6
	LA	0.824 5	0.037 3	0.046	0.785 4	0.862 7

愤怒等级	判别指标	TPR	FPR	最佳判别阈值
等级1	SWRAR	0.8276	0.2142	1.8863
	SGP	0.8043	0.2015	0.3974
	LA	0.7625	0.1438	0.1362
等级3	SWRAR	0.7489	0.1632	2.3241
	SGP	0.8673	0.2147	0.5054
	LA	0.8137	0.1847	0.1559
等级5	SWRAR	0.7827	0.1524	3.0835
	SGP	0.8344	0.1781	0.6142
	LA	0.7538	0.1395	0.1754

驾驶行为指标	正常	低强度愤怒	中强度愤怒	高强度愤怒
驾驶行为指标	anger level<1	1≤anger level<3	3≤anger level<5	anger level≥5
SWRAR	[0,1.886 3)	[1.886 3,2.324 1)	[2.324 1,3.083 5)	[3.083 5,+∞)
SGP	[0,0.397 4)	[0.397 4,0.505 4)	[0.505 4,0.614 2)	[0.614 2,+∞)
LA	[0,0.136 2)	[0.136 2,0.155 9)	[0.155 9,0.175 4)	[0.175 4,+∞)

Discriminating threshold of driving anger intensity based on driving behavior features by ROC curve analysis

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