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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.