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

摘要/Abstract

摘要：

为了对“路怒症”进行分级预警，提出了一种基于驾驶行为特征的愤怒强度判别阈值求解方法。通过在交通繁忙路段开展限时实车试验获得不同愤怒强度下的驾驶行为数据。基于方差分析选用方向盘转动角速度（SWRAR）、加速踏板踩踏速度（SGP）、横向加速度（LA）作为驾驶愤怒判别指标。运用接受者操作特征（ROC）曲线分析方法求得4种愤怒强度的最佳阈值范围分别为：正常状态0≤SWRAR<1.886 3,0≤SGP<0.397 4, 0≤LA<0.136 2；低强度愤怒1.886 3≤SWRAR<2.324 1,0.397 4≤SGP<0.505 4,0.136 2≤LA<0.155 9；中强度愤怒2.324 1≤SWRAR<3.083 5,0.505 4≤SGP<0.614 2,0.155 9≤LA<0.175 4；高强度愤怒SWRAR≥3.083 5,SGP≥0.614 2,LA≥0.175 4。验证结果表明，此驾驶行为特征阈值的准确率可达79.42%。本文研究结果可为驾驶愤怒分级预警系统的开发提供理论支撑。

关键词: 道路工程, 驾驶愤怒阈值, ROC曲线, 驾驶行为, 交通安全

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

中图分类号:

U491

万平,吴超仲,马晓凤. 基于ROC曲线和驾驶行为特征的驾驶愤怒强度判别阈值[J]. 吉林大学学报(工学版), 2020, 50(1): 121-131.

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.

图/表 17

图1

图2

图3

图4

图5

图6

表1

表2

图7

图8

图9

图10

表3

表4

表5

图11

表6

参考文献 19

1	吴超仲，雷虎 . 汽车驾驶愤怒情绪研究现状与展望[J]. 中国安全科学学报, 2010, 20(7): 3-8.
	Wu Chao-zhong , Lei Hu . Review on the study of motorists’ driving anger[J]. China Safety Science Journal, 2010, 20(7):3-8.
2	Kotinas I . Traffic safety facts: a compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system[R]. The U.S. National Highway Traffic Safety Administration, 2011.
3	雷虎 . 愤怒情绪下的汽车驾驶行为特征及其对交通安全的影响研究[D]. 武汉: 武汉理工大学智能交通系统研究中心, 2011.
	Lei Hu . The characteristics of angry drivingbehaviors and its effects on traffic safety [D]. Wuhan: Intelligent Transport Systems Research Center, Wuhan University of Technology, 2011.
4	Feng Zhong-xiang , Lei Ye-wei , Liu Hong-chao , et al . Driving anger in China: a case study on professional drivers[J]. Transportation Research Part F: Traffic Psychology and Behavior, 2016, 42: 255-266.
5	Dahlen E R , Martin R C , Ragan K , et al . Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving[J]. Accident Analysis and Prevention, 2005, 37(2): 341-348.
6	张迪, 万柏坤, 明东 . 基于生理信号的情绪识别研究进展[J]. 生物医学工程学杂志, 2015, 32(1): 229-234.
	Zhang Di , Wan Bai-kun , Ming Dong . Research progress on emotion recognition based on physiological signals[J]. Journal of Biomedical Engineering, 2015, 32(1): 229-234.
7	Paschero M , Vescovo G , Benucci L , et al . Real time classifier for emotion and stress recognition in a vehicle driver[C]∥IEEE International Symposium on Industrial Electronics,Hangzhou,China, 2012: 1690-1695.
8	Kamaruddin N , Wahab A . Driver behavior analysis through speech emotion understanding[C]∥IEEE Intelligent Vehicles Symposium University of California, San Diego, USA, 2010: 238-243.
9	Katsis C D , Goletsis Y , Rigas G , et al . A wearable system for the affective monitoring of car racing drivers during simulated conditions[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(3): 541-551.
10	Rebolledo-Mendez G , Reyes A , Paszkowicz S , et al . Developing a body sensor network to detect emotions during driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(4): 1850-1854.
11	Kessous L , Castellano G , Caridakis G . Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis[J]. Journal on Multimodal User Interfaces, 2010, 3(1): 33-48.
12	Cai H , Lin Y , Mourant R . Study on driver emotion in driver-vehicle-environment systems using multiple networked driving simulators[C]∥Driving Simulation Conference, Iowa City, Iowa, America, 2007: 1-8.
13	万平，吴超仲，林英姿，等 . 基于置信规则库的驾驶人愤怒情绪识别模型[J]. 交通运输系统工程与信息, 2015, 15(5): 96-102.
	Wan Ping , Wu Chao-zhong , Lin Ying-zi , et al . A recognition model of driving anger based on belief rule base[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 96-102.
14	刘鹏 . 融合面部表情和语音的驾驶员路怒症识别方法研究[D]. 镇江：江苏大学计算机科学与通信工程学院，2017.
	Liu Peng . Driver road rage recognition by combining facial expression and speech[D]. Zhenjiang: School of Computer Science and Telecommunication Engineering, Jiangsu University, 2017.
15	Herrero-Fernánde D . Psychometric adaptation of the driving anger expression inventory in a Spanish sample: differences by age and gender[J]. Transportation Research Part F: Psychology and Behavior, 2011, 14(4): 324-329.
16	万平，吴超仲，林英姿，等 . 基于驾驶行为多元时间序列特征的愤怒驾驶状态检测[J]. 吉林大学学报: 工学版, 2017, 47(5): 1426-1435.
	Wan Ping , Wu Chao-zhong , Lin Ying-zi , et al . Driving anger detection based on multivariate time series features of driving behaviors[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(5): 1426-1435.
17	Chen Zhi-jun , Wu Chao-zhong , Zhong Ming , et al . Identification of common features of vehicle motion under drowsy/distracted driving a case study in Wuhan China[J]. Accident Analysis and Prevention, 2015, 81: 251-259.
18	陈卫中，倪宗瓒，潘晓平，等．用ROC曲线确定最佳临界点和可疑值范围[J]. 现代预防医学, 2005, 32(7) : 729-731．
	Chen Wei-zhong , Ni Zong-zan , Pan Xiao-ping , et al . Receiver operating characteristic curves to determine the optimal operating point and doubtable value interval[J]. Modern Preventive Medicine, 2005, 32(7): 729-731.
19	Bechtel T , Capineri L , Windsor C , et al . Comparison of ROC curves for landmine detection by holographic radar with roc data from other methods[C]∥IEEE 8th International Workshop on Advanced Ground Penetrating Radar,Florence,Italy,2015:1-4.

相关文章 15

[1]	王芳,李晓光,郭慧,胡佳. 基于驾驶员视觉兴趣区的沙漠草原公路曲线间直线段线形指标优化[J]. 吉林大学学报(工学版), 2020, 50(1): 114-120.
[2]	狄胜同,贾超,乔卫国,李康,童凯. 橡胶集料混凝土细观损伤特性的加载速率效应[J]. 吉林大学学报(工学版), 2019, 49(6): 1900-1910.
[3]	张云龙,周刘光,王静,吴春利,吕翔. 冻融对粉砂土力学特性及路堤边坡稳定性的影响[J]. 吉林大学学报(工学版), 2019, 49(5): 1531-1538.
[4]	彭勇,高华,万蕾,刘贵应. 沥青混合料劈裂强度影响因素数值模拟[J]. 吉林大学学报(工学版), 2019, 49(5): 1521-1530.
[5]	江亮,贺宜. 电动两轮车风险驾驶行为及事故影响因素分析[J]. 吉林大学学报(工学版), 2019, 49(4): 1107-1113.
[6]	李晓珍,柳俊哲,戴燕华,贺智敏,巴明芳,李玉顺. 碳化作用下水泥浆内亚硝酸根离子的含量分布[J]. 吉林大学学报(工学版), 2019, 49(4): 1162-1168.
[7]	于天来,李海生,黄巍,王思佳. 预应力钢丝绳加固钢筋混凝土梁桥抗剪性能[J]. 吉林大学学报(工学版), 2019, 49(4): 1134-1143.
[8]	黄晓明,曹青青,刘修宇,陈嘉颖,周兴林. 基于路表分形摩擦理论的整车雨天制动性能模拟[J]. 吉林大学学报(工学版), 2019, 49(3): 757-765.
[9]	王静,吕翔,曲肖龙,钟春玲,张云龙. 路基土抗剪强度与化学及矿物成分的关系[J]. 吉林大学学报(工学版), 2019, 49(3): 766-772.
[10]	李伊,刘黎萍,孙立军. 沥青面层不同深度车辙等效温度预估模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1703-1711.
[11]	念腾飞, 李萍, 林梅. 冻融循环下沥青特征官能团含量与流变参数灰熵分析及微观形貌[J]. 吉林大学学报(工学版), 2018, 48(4): 1045-1054.
[12]	臧国帅, 孙立军. 基于惰性弯沉点的刚性下卧层深度设置方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1037-1044.
[13]	宫亚峰, 申杨凡, 谭国金, 韩春鹏, 何钰龙. 不同孔隙率下纤维土无侧限抗压强度[J]. 吉林大学学报(工学版), 2018, 48(3): 712-719.
[14]	程永春, 毕海鹏, 马桂荣, 宫亚峰, 田振宏, 吕泽华, 徐志枢. 纳米TiO₂/CaCO₃-玄武岩纤维复合改性沥青的路用性能[J]. 吉林大学学报(工学版), 2018, 48(2): 460-465.
[15]	马晔, 尼颖升, 徐栋, 刁波. 基于空间网格模型分析的体外预应力加固[J]. 吉林大学学报(工学版), 2018, 48(1): 137-147.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

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,+∞)