Model-based lane recognition

A lane recognition is used in various driver assist systems. The lane recognition detects lane boundaries and gets vehicle position relative to the lane and lane structure. A vision system is good for lane recognition because the vision can detect lane marks. One of the subjects for the vision system is improvement of robustness. Various methods have been tried to achieve it. We tried to improve the model-based approach for the robustness. Our main idea is the noise reduction based on narrowing a width of search area. The proposed method uses the road model based on the space continuity of lane structure. An update of the model is calculated through extended Kalman filter based on lane structure restriction. A search area width is estimated from covariances of the model parameters. After model parameters and their covariances are updated from observed lane boundaries near the vehicle, search area width for distant lane boundaries becomes narrower than the previous update. After the several updates, distant lane boundaries can be detected robustly with narrow search area excluding noisy image features.