A new effective approach to vision-based guidance for autonomous land vehicle (ALV) navigation in outdoor road environments with static and moving cars using model matching and color information clustering techniques is proposed. The conventional way of detecting obstacles and cars in the navigation route, which is in general difficult, is avoided; instead, collision-free road area detection, which is usually easier, is adopted. Road boundaries are used to construct the reference model, and road surface intensity is selected as the visual feature. The pixels in a road image near the two lines representing the road boundary shape, which are estimated at the beginning of each navigation cycle, are checked to judge whether the left or right lane width has changed due to occlusion caused by nearby static or moving cars on the road. If both lane widths have not changed, model matching is performed immediately to find the ALV location. If either or both lane widths have changed, corresponding processes are performed to find the width of the occluded road, and a model is recreated if the new road width is different from the old one in the previous navigation cycle. Model matching is then performed to locate the ALV on the occluded road. To save computing time, only partial model matching is performed. A turn angle is then computed to guide the ALV to follow the central path line on the extracted road for safe navigation. Various color information on roads is used to extract road surfaces, and a clustering algorithm is used to solve the problem caused by great changes of intensity in navigation environments. Successful navigation tests show that the proposed approach is effective for ALV guidance on common roads with static and moving cars.
|Number of pages||12|
|Journal||Proceedings of the National Science Council, Republic of China, Part A: Physical Science and Engineering|
|State||Published - 1 Sep 1998|