This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.
Issues of stability and bifurcation phenomena in vehicle lateral dynamics are presented. Based on the assumption of constant driving speed, a second-order nonlinear lateral dynamics model is obtained. Local stability and existence conditions for saddle-node bifurcation appearing in vehicle dynamics with respect to the variations in front wheel steering angle are then derived via system linearization and local bifurcation analysis. Bifurcation phenomena occurring in vehicle lateral dynamics might result in spin and/or system instability. A perturbation method is employed to solve for an approximation of system equilibrium near the zero value of the front wheel steering angle, which reveals the relationship between sideslip angle and the applied front wheel angle. Numerical simulations from an example model demonstrate the theoretical results.
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