Detection of driver inattention from fluctuations in vehicle operating data

被引：4

作者：

Bando S. ^{[1
]}

Nozawa A. ^{[1
]}

机构：

[1] Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo-ku, Sagamihara, Kanagawa

来源：

Artificial Life and Robotics | 2015年 / 20卷 / 01期

关键词：

Cognitive stress; Driving; Fluctuation; Heart rate variability; Respiration; Vehicle operating data;

D O I：

10.1007/s10015-014-0191-8

中图分类号：

学科分类号：

摘要：

Drivers have to drive their vehicle safely by properly using steering, throttling, and braking controls. However, drivers do not always maintain their concentration on managing their vehicles. The primary causes of driver inattention are distracting activities and drowsiness. Such driver inattention is thought to be one of the major causes of traffic accidents, and so the objective of the present study is to develop a new method for detecting driver inattention from fluctuations in vehicle operating data. Steering and throttle operations while driving were measured by acceleration sensors and respiration rhythm and an electrocardiogram were measured as physiological indices. The result of correlation between the estimative value, peaks and exponent values of frequency characteristics of vehicle operating data, and physiological indices, an association of sympathetic nervous system activation was indicated. The performance of detecting normal state, under cognitive stress state, and drowsy state by this method was an average correct discriminatory rate of 70 %. © 2014, ISAROB.

引用

页码：28 / 33

页数：5

共 12 条

[1]

Klauer S.G., Dingus T.A., Neale V., Et al., The impact of driver Inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data, Technical Report on National Highway Traffic Safety Administration, DOT HS, 810, (2006)

[2]

Rodriguez-Ibanez N., Garcia-Gonzalez M.A., de la Cruz M.A.F., Et al., Changes in heart rate variability indexes due to drowsiness in professional drivers measured in a real environment, Computing in Cardiology, 2012, pp. 913-916, (2012)

[3]

Nozawa A., Mizawa H., Mizuno T., Et al., Evaluation of the driver’s mental workload by conversational form based on facial skin thermal image analysis, IEEJ Transactions on Sensors and Micromachines, 126, 8, pp. 412-418, (2006)

[4]

Sakamoto R., Nozawa A., Tanaka H., Et al., Evaluation of the driver’s temporary arousal level by facial skin thermogram-effect of surrounding temperature and wind on the thermogram, IEEJ Transactions on Electronics, Information and Systems, 126, 7, pp. 804-809, (2006)

[5]

Forsman P.M., Vila B.J., Short R.A., Et al., Efficient driver drowsiness detection at moderate levels of drowsiness, Accident Analysis & Prevention, 50, pp. 341-350, (2013)

[6]

Mortazavi A., Eskandarian A., Sayed R.A., Effect of drowsiness on driving performance variables of commercial vehicle drivers, International Journal of Automotive Technology, 10, 3, pp. 391-404, (2009)

[7]

Eskandarian A., Mortazavi A., Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection, pp. 553-559, (2007)

[8]

McDonald A.D., Schwarz C., Lee J.D., Et al., Real-time detection of drowsiness related lane departures using steering wheel angle, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56, 1, pp. 2201-2205, (2012)

[9]

Saito K., Uchida M., Nozawa A., Control of Handwriting based on Rhythm Perception, (2013)

[10]

Komatsu T., Miyake Y., Time-series analysis of anticipatory behavior in synchronization tapping task, Transactions of the Society of Instrument and Control Engineers (Transactions of SICE), 39, 10, pp. 952-960, (2003)

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