Applying multi level processing for robust geometric lane feature extraction

Lane feature extraction is a function which is needed for example for autonomous driving and driver assistance systems. For example Lane Departure Warning and Lane Keeping rely on information provided by a lane estimation algorithm. One important step of the lane estimation procedure is the extraction of measurements or detections which can be used to estimate the shape of the road or lane. These detections are generated by white lane markers or the road border itself. Additionally traffic rules can be derived if a system is able to distinguish for example between solid and dashed lane marks as well as between the different types of lane marks itself (length, thickness). Every state estimation filter needs a properly defined model and reliable measurements to work correctly. This paper presents an approach to extract reliable lane mark measurements using multi level feature extraction and classification. Geometric features will be generated for lane mark candidates and used for the distinction between true and false lane mark detections.