Vision-based real-time road detection in urban traffic

Road detection is the major task of autonomous vehicle guidance. We notice that feature lines, which are parallel to the road boundaries, are reliable cues for road detection in urban traffic. Therefore we present a real-time method that extracts the most likely road model using a set of feature-line-pairs (FLPs). Unlike the traditional methods that extract a single line, we extract the feature lines in pairs. Working with a linearly parameterized road model, FLP appears some geometrical consistency, which allows us to detect each of them with a Kalman filter tracking scheme. Since each FLP determines a road model, we apply regression diagnostics technique to robustly estimate the parameters of the whole road model from all FLPs. Another Kalman filter is used to track road model from frame to frame to provide a more precise and more robust detection result. Experimental results in urban traffic demonstrate real-time processing ability and high robustness.