뇌파 데이터를 이용한 뉴럴 네트워크 기반의 운전자 이상상태 분석

Abstract

As the amount of automobile usage increases, carelessness of drivers incur more big traffic accidents and casualties. According to the causal analysis index of big traffic accidents, drowsiness and drinking accounts for a high percentage of causes of traffic accidents, and it is thought that the incidence of big traffic accidents is directly related to the driver's abnormal condition. In this paper, the abnormal condition of the driver is analyzied by collecting brainwave data, and Power Spectral Analysis to quantitatively determine each vibration component in the brainwave data. Accordingly, the analyzed data are collected for learning and test data, and classify drivers’ abnormal condition with high accuracy through learning modeling for drowsy driving and drunken driving by using DNN(Deep Neural Network), which is a kind of machine learning technique. In conclusion, the performance evaluation of learning modeling about the comparable subjects’ data is discussed.