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This paper deals with a vehicle detection method for realizing a blind spot warning function, under various environmental conditions. We introduced a method that is capable of discriminating the target object vehicles, under poor lighting conditions and in cases where the lens may be exposed to splashes in wet, snow and dirt roads. The image sensing of the vehicle detection consists of four functional components: obstacle detection, velocity estimation, vertical edge detection, and final classification. Such componets allow robust performances resembling geometry based approaches, with low calculation power as an apperance based approach. This paper describes the functional components, and furthermore methods to enhance the performances under low contrast conditions and also suppress false detections caused by residue on the lens, which becomes essential for installation on vehicles driven in actual road conditions.

Various driver assistance systems have been developed for maintaining a vehicle in its lane. For example, systems that apply force to the steering gear to help the driver keep the vehicle in the center of its lane are becoming popular. On the other hand, direct yaw-moment control systems have been developed for use in stabilizing the vehicle in extremer conditions where loss of control is detected. This paper deals with a system for aiding drivers in recovering from lane departure and maintaining the vehicle in its lane by utilizing direct yaw-moment control. The system uses a CCD camera to recognize lane makers and detects lane departures based on the positional relationship between the vehicle and the lane. When a lane departure is detected, the system applies force to the wheels to generate a yaw moment in the vehicle, so that it turns towards the original lane.