Passengers are more susceptible to experiencing motion sickness (MS) than drivers. The difference in the severity of MS is due to their different head movement behavior during curve driving. When negotiating a curve, the passengers tilt their heads towards the lateral acceleration direction while the drivers tilt their heads against it. Thus, to reduce the passengers’ level of MS, they need to reduce their head’s tilting angle towards the lateral acceleration direction. Designing MS minimization strategies is easier if the correlation between the head movement and lateral acceleration is known mathematically. Therefore, this paper proposes the utilization of a time delay neural network (TDNN) to model the correlation of the occupant’s head movement and lateral acceleration. An experiment was conducted to gather real-time data for the modeling process. The results show that TDNN manages to model the correlation by producing a similar output response to the actual response. Thus, it is expected that the correlation model could be used as an occupant’s head movement predictor tool in future studies of MS.
Passengers are more susceptible to motion sickness (MS) than the drivers because during cornering, they tilt their heads according to lateral acceleration direction, while the drivers tilt their heads against it. During slalom driving, high lateral acceleration that resulted from inappropriate wheel's turning will increase the severity level of MS as it contributes to a larger passenger's head roll angle towards the lateral acceleration direction. Thus, for an autonomous vehicle, it is necessary to design a smooth lateral control to obtain appropriate wheel angle to prevent high lateral acceleration. This study proposes an inner-loop lateral control strategy which utilized head roll angle as the controlled variable to generate corrective wheel angle to reduce the lateral acceleration. Firstly, an estimation model of driver's and passenger's head roll angle is developed by radial basis function network method based on the correlation between lateral acceleration and occupant's head roll angle. The driver's and passenger's models are considered as the reference and the controlled subject, respectively. Secondly, a fuzzy logic controller is adopted to generate corrective wheel angle based on the head roll angle responses. The reduction of the lateral acceleration caused by the corrective wheel angle minimized the passenger's head roll angle and hence mitigated their MS level. Simulation results show 3.25% and 10.86% reduction of motion sickness incidence in a single lap and ten laps after the proposed control strategy is applied. It is expected that the proposed control strategy will contribute to the MS mitigation study in autonomous vehicle field.
In terms of vehicle dynamics, motion sickness (MS) occurs because of the large lateral acceleration produced by inappropriate wheel turning. In terms of passenger behavior, subjects experience MS because they normally tilt their heads towards the direction of lateral acceleration. Relating these viewpoints, the increment of MS originates from the large lateral acceleration produced by the inappropriate wheel’s turn, which then causes greater head movement with respect to the lateral acceleration direction. Therefore, this study proposes the utilization of fuzzy-proportional integral derivative (PID) controller for an MS minimization control structure, where the interaction of the lateral acceleration and head tilt concept is adopted to diminish the lateral acceleration. Here, the head movement is used as the controlled variable to compute the corrective wheel angle. The estimation of the head movement is carried out by an estimation model developed by the radial basis function neural network (RBFNN) method. An experiment involving a driving simulator was conducted, to verify the proposed control system’s performance in regard to the autonomous vehicle’s passengers. The results show that the averages of motion sickness incidence (MSI) index can be lowered by 3.95% for single lap and 11.49% for ten laps.
<p>One of the dominant virtue of Steer-By-Wire (SBW) vehicle is its capability to enhance handling<br />performance by installing Active Front Steering (AFS) system without the driver’s interferences. Hence,<br />this paper introduced an AFS control strategy using the combination of Composite Nonlinear Feedback<br />(CNF) controller and Disturbance Observer (DOB) to achieve fast yaw rate tracking response which is also<br />robust to the existence of disturbance. The proposed control strategy is simulated in J-curve and Lane<br />change manoevres with the presence of side wind disturbance via Matlab/Simulink sotware. Futhermore,<br />comparison with Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controllers<br />are also conducted to evaluate the effectiveness of the proposed controller. The results showed that the<br />combined CNF and DOB strategy achieved the fastest yaw rate tracking capability with the least impact of<br />disturbance in the AFS system installed in SBW vehicle.</p>
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