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

Abstract

Abstract Vehicle technology of the interaction between human and the machine has been called human-electronics in Japan. It is necessary to achieve a 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 crucial to find a method to determine a driver's operational intention. Recently, some former researches have been reported about the investigation of the brain activity of the driver. The time frequency analysis such as FFT has been major method in the traditional decomposition of the electroencephalogram (EEG). 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 parallel factor analysis (PARAFAC), and we investigated the feature factor of longitudinal behavior for recognize and judgment from that decomposition result. PARAFAC analysis has known as a multi-channel EEG analysis of multi-dimensional data. Consequently, Common to all subjects has two factors of the frequency component which were in the 5-10 Hz and 8-13 Hz. Those factors were changed by the driver's mental state during visual recognition and judgment. In addition, we estimated the feature factor from a new EEG data set using inverse solution of PARAFAC. From estimation results, the driver recognized preferentially shape and color than distance and movement information in the car following situation.