抄録
Vehicle technology retarding 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. It is important to find a method to determine a driver's operational intention. Therefore, we have focused on the brain activities in the biological information. 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 our previous research, we investigated that the driver's EEG at the preceding car avoidance maneuver 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. In the previous research (Ikenishi et al., 2010), we investigated the driver's EEG of during lane change maneuver using the parallel factor (PARAFAC) analysis. Consequently, all subjects have two common factors of the frequency component which exist in the 5-10 Hz and 8-13 Hz region. Those factors were changed by the driver's mental state during visual recognition and judgment. In this paper, we estimated the driver's intention from a driver's EEG using source current distribution estimation with Hierarchical Bayesian method and the sparse logistic regression. From the estimation results, the estimation accuracy of driver's intention was higher than about 70 % of three subject's in the lateral operation.
