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The development of an integrated controller for a 4WS/4WD electric bus is investigated. The front wheel steering angle is assumed to be controlled by the human driver. The vehicle is controlled by the rear wheel steering and the yaw moment that can be generated by the differential torque/brake control on each wheel. The high speed cornering is used as the testing scenario to validate the designed controller. Due to the highly nonlinear and the multiple-input and multiple-output nature, the control design is separated into different stages using the hierarchical layer control concept. The longitudinal speed is controlled using a PI controller together with a rule-based speed modification. The other two control inputs, namely the rear wheel steering and the DYC moment, are then designed using the state-dependent Riccati equation method. The designed controllers are evaluated using computer simulations first, and the simulations showed promising results.

Vehicle Rollover Prevention/Warning Systems have recently been an important topic in Advanced Driver Assistance Systems (ADAS) of automotive electronics field. This paper will propose a rollover-prevention system implementation with vehicle dynamic model, video-detection technique and rollover index to help the driver avoid accidents as driving into a curve. Due to the reason that vehicle rollover motion analysis needs complicated computation and accurate parameters of vehicle stability in real time, in the first stage a vehicle dynamic model based on Extended Kalman Filter (EKF) algorithm is built, which can estimate vehicle roll/yaw motion in the curve by vehicle sensors. And then the image-based technique will be employed in detecting the front road curvature, and combined in the system to predict vehicle steering status. The final stage is to apply the vehicle rollover index with estimated vehicle motion to predict the dangerous level to drivers for warning.

Rollover accidents are a serious and too frequent incident at many locations on the road. Especially in the condition of trucks or Sport Utility Vehicles (SUV) travelling at high speeds require a greatly reduced speed when meeting the exit ramps and tight curves. The usual cause of rollovers is driving behavior, typically excessive speed while cornering which adversely affects the stability of the vehicle. Sudden or severe changes in direction can create a potential risk to rollover. By the time drivers see or feel something wrong, it is usually too late to prevent a rollover. Although some papers discuss many methods to eliminate the rollover phenomenon, it could not provide early warning for the driver. These systems usually work in the condition of slipping or near rollover. To overcome the problem, an alternate approach is to incorporate an image-based detection technique with rollover prediction model.

This study proposes an anti-counterfeiting system of drunk driving, which prevents drivers from drunken driving and cheat of driver's alcohol detection. The study develops the technology of driver's facial image match by a serial image processes. The methodology of facial image match uses the Adaboost algorithm, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to extract the facial features of drivers, is suitable to be applied under the internal environments of cabin. By analyzing the facial features of drivers, the time of driver's exchange is detected, and the driver's identity is indentified. When the cheat act of driver's alcohol detection occurred, the system will generate warning signals through a buzzer to notice the driver to take alcohol detection.

Side crash tests indicate that side impact bars play an important role of providing protection to occupants during the crashing processing. It can provide lateral stiffness of side structure and get more human live space. Therefore a good design of side impact bars will improve the crashworthiness. To improve side impact bars, generally, “try and error” is common way to use but not efficient to reach the requirement. In this paper, a numerical optimization of side impact bars is presented, instead of trying process. The load conditions are meet FMVSS214 static test. The study was performed using LS-DYNA and Hyper Study code. Thickness and shape will be considered as varieties of optimization. The object is to minimize the displacement at loaded area.