Research on Psychological Reaction of Driving Distraction Based on Sample Entropy

  • Xiao-hua ZhaoEmail author
  • Wen-xiang Xu
  • Ying Yao
  • Jian Rong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)


Driving distraction is a task affected by an increasing number of Driving Assistance Systems, which have been a main reason of traffic accident. Understanding the nature of driver distraction and to find a way to analysis the driver distraction we can reduce the traffic accident. This chapter applied the electrocardiography (ECG) for identifying driving situation. ECG data such as heart rate variability (HRV), QRS wave were used to represent driving distraction, the method of sample entropy used to indicate the difference between normal driving and driving distraction. The data have interviewed 34 subjects during two weeks based on driving simulation experiment. The result showed that sample entropy of ECG data on distracted driving is higher than that on normal driving. Especially, the driver who send message during driving has the biggest difference in sample entropy. Driver’s QRS waveform showing a greater degree of confusion on the distracted driving.


Driving distraction Driving simulator experiment Driver behavior ECG 



Supported by National Natural Science Foundation of China(No. 61672067), A Study on the Eco-Driving Behavior Classification Model and Optimization Based on Deep Learning Theory; Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety in China: Study on Intervention Method of Illegal Driving Behavior Based on Risk Prediction Education (2016ZDSYSKFKT01).

Ethics Statement: The research involving human participants in this study has been approved by the Beijing University of technology’s research committee (per IRB). The written informed consent form for the experiment was also signed by each participant in this study.


  1. 1.
    Yerkes RM, Dodson JD (1908) The relation of strength of stimulus to rapidity of habit-ormation. J Comp Neurol Psychol 18(5):459–482CrossRefGoogle Scholar
  2. 2.
    Wu Q (2009) An overview of driving distraction measure methods. In: Institute of electrical and electronics engineers of distracted driving, vol 53. pp 78–83Google Scholar
  3. 3.
    Sheridan TB (1970) Big research brother as driver: new demands and problems for the man at the wheel. Hum Factors 12(1):95–101CrossRefGoogle Scholar
  4. 4.
    Michon JA (1989) Explanatory pitfalls and rule-based driver models. Accid Anal Prev 21(4):341–353CrossRefGoogle Scholar
  5. 5.
    Ranney TA (1994) Models of driving behavior-a review of their evolution. Accid Anal Prev 26(6):733–750CrossRefGoogle Scholar
  6. 6.
    Dingus TA, Klauer SG, Neale VL et a1 (2006) The 100 car naturalistic driving study phase II of the 100 car field results experiment (Report No. DOTHS810593). Washington, D.C, National Highway Traffic Safety AdministrationGoogle Scholar
  7. 7.
    Picard R (1995) Affective Computing. Technical Report 321, Cambridge, Massachusetts, MIT Media Laboratory, Perceptual Computing Section, Cambridge, MassachusettsGoogle Scholar
  8. 8.
    Riener A, Ferscha A (2009) Heart on the road: HRV analysis for monitoring a driver’s affective state. In: Proceedings of the first international conference on automotive user interfaces and interactive vehicular applicationsGoogle Scholar
  9. 9.
    Kim D, Seo Y, Kim S, Jung S (2008) Short term analysis of long term patterns of heart rate variability in subjects under mental stress. In: International conference on Bio medical engineering and informatics, 2008. BMEI 2008, vol 2Google Scholar
  10. 10.
    Clifford G, Azuaje F, McSharry P (2006) Advanced methods and tools for ECG data analysis. Artech HouseGoogle Scholar
  11. 11.
    Korsakas S et al (2005) Electrocardiosignals and motion signals telemonitoring and analysis system for sportsmen. In: Computers in cardiology. IEEE 2005, pp 363–366.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xiao-hua Zhao
    • 1
    Email author
  • Wen-xiang Xu
    • 1
  • Ying Yao
    • 1
  • Jian Rong
    • 1
  1. 1.College of Metropolitan TransportationBeijing University of TechnologyBeijingChina

Personalised recommendations