The primary method of improving steering dynamic response performance is expected to involve position control. However the driver uses not only the position of the steering wheel but also torque for control. It has been pointed out that particularly in regions that are close to straight-line driving, torque is the primary means of steering control. Therefore in order to achieve better high-quality dynamic response, it will be necessary to set higher natural frequencies and damping ratios for force control. A universal method for achieving this can be achieved by examining the symbolic expressions for these factors. Force control natural frequencies and damping ratios have been formularized for stable vehicles at all driving speeds. However these formulas cannot be used for vehicles which have unstable regions, and in fact there are vehicles which have such unstable regions. This paper examines a method of setting higher natural frequencies and damping ratios in order to improve the quality of dynamic response characteristics for vehicles that have unstable regions. I first envision a vehicle with neutral steering and steering system damping of 0, and confirm that the characteristic formula is a fourth-order equation for the Laplacian operator s. Next I show that when s is converted to a certain variable, the characteristic formula is written as a biquadratic equation for that variable. By solving this biquadratic equation, the damped natural frequencies and damping ratios are formularized. By considering these formulas, I show that increasing the cornering coefficient is a method that can simultaneously increase the damped natural frequency and damping ratio. I also show that this method can be applied to under-steer vehicles and vehicles which have steering system damping, and finally demonstrate the utility of this method with a time history response in transitional steering.

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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.

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