AbstractsEngineering

With modern day computer power, developing intelligent vehicles is fast becoming a reality. An Intelligent Vehicle is a vehicle equipped with sensors and computing that allow it to perceive the world around it, and to decide on appropriate action. Vision cameras are a good choice to sense the environment. One key task of the camera in an intelligent vehicle is to detect and localise the obstacles, which is the preparation of path planning. Stereo vision based obstacle detection is used in this research. It does not analyse semantic meaning of image features, but directly measures the 3-D coordinates of image pixels, and thus is suitable for obstacle detection in an unknown environment. In this research, a novel correlation based stereo vision method is developed which greatly improves its accuracy while maintaining its real-time performance. Since a vision system provides a large amount of data, extracting refined information may sometimes be complex. In obstacle detection tasks, the purpose is to distinguish the obstacle pixels from the ground pixels in the disparity image. V-Disparity image approach is used in this research to detect the ground plane, however this approach relies heavily on sufficient road features. In this research, a correlation method to locate the ground plane in the disparity image, even without significant road features, is developed. Moreover, traditional V-Disparity images have difficulties detecting non-flat ground, thus having limited applications. This research also develops a method to detect non-flat ground using V-Disparity images, thus greatly widening its application.