A Prediction Model for Traffic Accident Proneness Caused by Drunk Driving via Numerical Characteristics of Drivers’ EEGs

Abstract

Abstract:

Abstract： This paper introduces the establishment of a predicting model for normalized traffic accident proneness (NAP) after drinking. Eighteen drivers’ EEGs and traffic accident proneness were measured respectively in different drunken states. Considering the instant complexity and longterm periodicity of EEGs measured from the left frontal lobe, the power gain of δ wave and fuzzy entropy of EEG were invented and calculated. A hybrid SigmaPi neural network was introduced and studied to help building the predict model for NAP from the aspects of both power gain of δ wave and fuzzy entropy of EEG. Experiments proved that the predicted values of NAP accord with the actual values, with a consistent increase and decrease characteristic. When the amount of the alcohol drunk is less than 50% of the subjective maximum alcohol to drink, the errors were very small, but when the amount of the alcohol drunk is more than 50%, the errors became bigger along with the increase volume of alcohol to drink.

Key words: drunk driving, traffic accident proneness, electroencephalogram (EEG), power gain of &delta, wave, fuzzy entropy

CLC Number:

U 491.2

ZHONG Mingen1,WU Pingdong2，PENG Junqiang3,HONG Hanchi1. A Prediction Model for Traffic Accident Proneness Caused by Drunk Driving via Numerical Characteristics of Drivers’ EEGs[J]. Journal of Shanghai Jiaotong University, 2015, 49(02): 287-292.

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