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We present a lane detection algorithm that robustly detects and tracks various lane markings in real-time. The first part is a feature detection algorithm that transforms several input images into a top view perspective and analyzes local histograms. For this part we make use of state-of-the-art graphics hardware. The second part fits a very simple and flexible lane model to these lane marking features. The algorithm was thoroughly tested on an autonomous vehicle that was one of the finalists in the 2007 DARPA Urban Challenge. In combination with other sensors, i.e. a lidar, radar and vision based obstacle detection and surface classification, the autonomous vehicle is able to drive in an urban scenario at up to 15 mp/h.