一种混行环境下驾驶人认知分心识别方法

doi:10.13229/j.cnki.jdxbgxb20210215

Abstract

Abstract:

To reduce traffic accidents in an environment where intelligent connected vehicle and non-connected vehicle are mixed， a cognitive distraction recognition model based on bi-directional long short-term memory（Bi-LSTM） with attention mechanism at unsignalized intersections was proposed in a mixed traffic environment. The driving simulator data of 60 drivers in the mixed traffic environment was collected and support vector machine recursive feature elimination algorithm（SVM-RFE） was adopted to extract the optimal feature subset as the input of the model. The results show that the recognition accuracy of the model is as high as 96.58% and the F₁-scores is 96.24%. Compared with SVM and decision tree distraction recognition models， this model has the best performance in terms of accuracy， recall， the F₁-scores and the ROC curve. The model can be applied to the autonomous driving distraction alarm assistance system， which is of great significance to improving road safety.

Key words: distracted driving, mixed traffic environment, Bi-LSTM model, recursive feature elimination algorithm

CLC Number:

U461.91

Qiang HUA,Li-sheng JIN,Bai-cang GUO,Shun-ran ZHANG,Yu-han WANG. A recognition method for driver's cognitive distraction in simulated mixed traffic environment[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1800-1807.

Figures/Tables 10

Table 1

Fig.1

Fig.2

Table 2

Table 3

Fig.3

Fig.4

Table 4

Table 5

Fig.5

References 21

1	Nee M, Contrand B, Orriols L, et al. Road safety and distraction, results from a responsibility case-control study among a sample of road users interviewed at the emergency room[J]. Accident Analysis and Prevention, 2019, 122: 19-24.
2	Metz B, Landau A, Just M. Frequency of secondary tasks in driving-results from naturalistic driving data[J]. Safety Science, 2014, 68: 195-203.
3	Klauer S G, Guo F, Simons-Morton B G, et al. Distracted driving and risk of road crashes among novice and experienced drivers[J]. The New England Journal of Medicine, 2014, 370(1): 54-59.
4	Haque M M, Washington S. A parametric duration model of the reaction times of drivers distracted by mobile phone conversations[J]. Accident Analysis and Prevention, 2014, 62: 42-53.
5	Ross V, Jongen E M, Wang W, et al. Investigating the influence of working memory capacity when driving behavior is combined with cognitive load: an LCT study of young novice drivers[J]. Accident Analysis and Prevention, 2014, 62: 377-387.
6	Strayer D L, Watson J M, Drews F A. Cognitive distraction while multitasking in the automobile[J], Psychology of Learning and Motivation, 2011, 54: 29-58.
7	Young K L, Salmon P M. Examining the relationship between driver distraction and driving errors: a discussion of theory, studies and methods[J]. Safety Science, 2012, 50(2): 165-174.
8	Sharma A, Ali Y, Saifuzzaman M. Human factors in modelling mixed traffic of traditional, connected, and automated vehicles[C]∥International Conference on Applied Human Factors and Ergonomics Springer, Brisbane, Australia, 2017: 262-273.
9	Strayer D L, Drews F A. Cell phone induced driver distraction[J]. Current Directions in Psychological Science, 2007, 16(3): 128-131.
10	孙健,张一豪,王俊骅. 基于自然驾驶数据的分心驾驶行为识别方法[J]. 中国公路学报, 2020, 33(9): 225-235.
	Sun Jian, Zhang Yi-hao, Wang Jun-hua. Detecting driver distraction behavior with naturalistic driving data[J]. China Journal of Highway and Transport, 2020, 33(9): 225-235.
11	张辉,钱大琳,邵春福,等.模拟驾驶环境下驾驶人分心状态判别[J]. 中国公路学报, 2018, 31(4): 47-55.
	Zhang Hui, Qian Da-lin, Shao Chun-fu, et al. Driver's distraction states identification in simulation driving environment[J]. China Journal of Highway and Transport, 2018, 31(4): 47-55.
12	Liao Y, Li S E, Wang W, et al. Detection of driver cognitive distraction: a comparison study of stop-controlled intersection and speed-limited highway[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(6): 1628-1637.
13	Liang Y, Lee J D. A hybrid bayesian network approach to detect driver cognitive distraction[J]. Transportation Research Part C, 2014, 38(1): 146-155.
14	Hua Q, Jin L S, Jiang Y Y, et al. Cognitive distraction state recognition of drivers at a nonsignalized intersection in a mixed traffic environment[J]. Advances in Civil Engineering, 2021(5): 1-16.
15	Zhou P, Shi W, Tian J. Attention-based bidirectional long short-term memory networks for relation classification[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016: 207-213.
16	张健,吴坤润,杨敏,等.智能网联环境下交叉口双环自适应控制模型[J]. 吉林大学学报: 工学版, 2021, 51(2): 541-548.
	Zhang Jian, Wu Kun-run, Yang Min, et al. Double-ring adaptive control model of intersection during intelligent and connected environment[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 541-548.
17	Wu G, Sanderson B, Bittner V. The 6-minute walk test: how important is the learning effect?[J]. American Heart Journal, 2003, 146(1): 129-133.
18	Liao Y, Li S E, Wang W. The impact of driver cognitive distraction on vehicle performance at stop-controlled intersections[C]∥Intelligent Vehicles Symposium, Seoul, South Korea, 2015: 946-951.
19	裴玉龙,金英群,陈贺飞.基于脑电信号分析的不同年龄驾驶人疲劳特性[J]. 中国公路学报, 2018, 31(4): 59-65.
	Pei Yu-long, Jin Ying-qun, Chen He-fei. Fatigue characteristics in drivers of different ages based on analysis of EEG[J]. China Journal of Highway and Transport, 2018, 31(4): 59-65.
20	卢运梅. SVM-RFE算法在数据分析中的应用[D]. 长春: 吉林大学计算机科学与技术学院, 2009.
	Lu Yun-mei. The application of SVM-RFE algorithm to data analysis[D]. Changchun: College of Computer Science and Technology, Jilin University, 2009.
21	王芳荣,郭柏苍,金立生,等.次任务驾驶安全评价指标筛选及其权值计算[J]. 吉林大学学报: 工学版, 2017, 47(6): 1710-1715.
	Wang Fang-rong, Guo Bai-cang, Jin Li-sheng, et al. Selection of safe evaluation indexes and weight calculation for secondary task driving[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(6): 1710-1715.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

驾驶人特征		数量	比例/%	2019年中国司机总数的百分比/%
性别	男	42	70.0	70.00*
性别	女	18	30.0	30.00*
年龄	18~25	14	23.3	24.10*
	26~35	20	33.3	34.12*
	36~50	24	40.0	38.88*
	>60	2	3.3	2.90*
驾龄/年			4~15，均值=8，标准差=3.34
驾驶里程/万公里			均值=3.57，标准差=1.78

序号	符号	特征指标	指标类别
1	R_（_α₊_β_）	脑电指标R_（_α₊_β_）	脑电指标
2	R_（_α_/_β_）	脑电指标R_（_α_/_β_）
3	R_（_θ₊_β_）	脑电指标R_（_θ₊_β_）
4	Pupil_Mean	瞳孔直径均值	眼动指标
5	Fixation_X_Mean	注视点的水平位置均值
6	SacSP_Mean	扫视速度均值
7	Fixation_Y_Mean	注视点的垂直位置均值
8	SDLP	横向位移标准差	驾驶绩效指标
9	SDLA	横向加速度标准差
10	SDSWA	方向盘转角标准差
11	SP_Mean	车速均值
12	LonA_Mean	纵向加速度均值
13	LonD_Mean	纵向减速度均值
14	ThrottleP_Mean	油门开度均值
15	SWV_Mean	方向盘转角角速度均值
16	YawV_Mean	横摆角速度均值
17	BrakeP_Mean	制动踏板开度均值

重要度序号	指标描述	符号
1	脑电指标R_（_θ₊_β_）	R_（_θ₊_β_）
2	瞳孔直径均值	Pupil_Mean
3	横向位移标准差	SDLP
4	方向盘转角标准差	SDSWA
5	扫视速度均值	SacSP_Mean
6	车速均值	SP_Mean
7	注视点的水平位置均值	Fixation_X_Mean
8	注视点的垂直位置均值	Fixation_Y_Mean
9	横向加速度标准差	SDLA
10	脑电指标R_（_α₊_β_）	R_（_α₊_β_）
11	纵向加速度均值	LonA_Mean
12	方向盘转角角速度均值	SWV_Mean
13	横摆角速度均值	YawV_Mean
14	制动踏板开度均值	BrakeP_Mean
15	脑电指标R_（α/_β_）	R_（_α/β_）
16	纵向减速度均值	LonD_Mean
17	油门开度均值	ThrottleP_Mean

特征个数	准确率/%	精确率/%	召回率/%	F₁值/%
5	89.75	87.70	89.81	88.74
6	90.13	89.62	90.75	90.18
7	93.00	92.57	93.50	93.03
8	94.75	94.08	95.50	94.78
9	96.58	95.28	97.22	96.24
10	95.25	93.99	96.00	93.84
11	93.75	92.47	95.25	93.43

特征个数	准确率 /%	精确率 /%	召回率 /%	F₁值 /%	算法类别
9	96.58	95.28	97.22	96.24	Bi-LSTM+ Attention
9	93.36	92.38	92.94	92.66	SVM
9	92.17	90.84	91.85	91.34	决策树

A recognition method for driver's cognitive distraction in simulated mixed traffic environment

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 10

References 21

Related Articles 1

Metrics

Comments

Recommended 0