LAUSR

Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is still of great challenge. In this study, a vision-based lane detection system with dynamic integration and online evaluation is proposed. To increase the robustness of the lane detection system, the integration system dynamically processes two lane detection modules. First, a primary lane detection module is designed based on the steerable filter and Hough transform algorithm. Then, a secondary algorithm, which combines the Gaussian mixture model for image segmentation and random sample consensus for lane model fitting, will be activated when the primary algorithm encounters a low detection confidence. To detect the colour and line style of the ego lanes and evaluate the lane detection system in real time, a lane sampling and voting technique is proposed. By combining the sampling and voting system system with prior lane geometry knowledge, the evaluation system can efficiently recognise the false detections. The system works robustly in various complex situations (e.g. shadows, night, and lane missing scenarios) with a monocular camera.