Abstract
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.
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Acknowledgements
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.
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Zhao, Xh., Xu, Wx., Yao, Y., Rong, J. (2019). Research on Psychological Reaction of Driving Distraction Based on Sample Entropy. In: Wang, W., Bengler, K., Jiang, X. (eds) Green Intelligent Transportation Systems. GITSS 2017. Lecture Notes in Electrical Engineering, vol 503. Springer, Singapore. https://doi.org/10.1007/978-981-13-0302-9_26
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DOI: https://doi.org/10.1007/978-981-13-0302-9_26
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