Analysis of longitudinal driving behaviors during car following situation by driver's EEG using PARAFAC

Abstract

Vehicle technology of the interaction between human and machine has been called human-electronics in Japan. It is necessary to obtain better relationship between human and vehicle. A driver's information, which can be obtained from steering operation, pedal operation, camera images and physiological information, particularly is important to find a method to determine a driver's operational intention. Recently, some former researches haves been reported about the investigation of the brain activity of the driver. The traditional decomposition of the electroencephalogram (EEG) has been based on the time-frequency domain components such as FFT. However, these conventional methods can only use two-dimensional data. In this paper, we described that the driver's EEG during car following was decomposed by PARAFAC and we investigated the feature factor of longitudinal behavior for recognize and judgment from that decomposition result. As a multi-channel EEG analysis using multi-dimensional data, parallel factor analysis (PARAFAC) method is based on the report. Consequently, Common to the all subjects has two factors of the frequency component which were in the 5-10 Hz and 8-13 Hz. It was considered that those factors were changed by the driver's mental state, during visual recognition and judgment. And we estimated the feature factor from a new EEG data set using inverse solution of PARAFAC. From estimation results, the driver recognized preferentially shapeand color than distance and movement information in the car following situation. © 2013 The Japan Society of Mechanical Engineers.