Estimation of Driver's Longitudinal Intention for the Preceding Car by Brain Current Distribution Estimation Method

The interaction between human and vehicle has been extensively researched to reduce the traffic accident in Japan. In the research of driver assistance system, human-friendly driver assistance systems have been researched using the information of driver and vehicle. This system requires to achieve a better relationship between human and vehicle. In addition, it is important to find a method to detect driver's operational intention. Therefore, we have focused on the brain activities in the biological information. 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 driver's EEG of during longitudinal operation. Consequently, all subjects have two common factors of the frequency component which exist in the 5-10 Hz and 8-13 Hz bandwidth. Those factors were changed by the driver's mental state during visual recognition and judgment. In this paper, we estimated the driver's longitudinal 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 60 % accuracy of all operation except the gas pedal operation of one's subject.