摘要
为建立酒后驾车的事故倾向预估模型,测量了18位驾驶员不同程度饮酒后的脑电信号和交通事故倾向指标,并分别根据左额叶区脑电的长时周期度和瞬时复杂度计算规范化脑电δ波功率增益和脑电模糊熵.引进一种混合型Sigma-Pi模糊神经网络,研究网络权值训练方法,构建脑电特征参数和事故倾向指标之间的预估模型.实验结果表明:模型估计值与实际值吻合较好,具有一致增减特性,在驾驶员饮酒量小于50%主观最大饮酒量时误差很小,在饮酒量大于50%主观最大饮酒量时误差随饮酒量增大有所增加.
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 8 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 8 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.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2015年第2期287-292,共6页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(61104225
61004114)
关键词
酒后驾车
交通事故倾向
脑电图
δ波功率增益
模糊熵
drunk driving
traffic accident proneness
electroencephalogram （EEG）
power gain of 8 wave
fuzzy entropy
