This paper represents the vision processing solution used for lane detection by the Insight Racing team, for
DARPA Grand Challenge 2007. The problem involves detecting the lane markings for maintaining the position
of the autonomous vehicle within the lane, at usable frame rate. This paper describes a method based on color
interpretation and scanning based edge detection for quick and reliable results. First the color information is
extracted from the image using RGB to HSV transform and mapped to the Munsell color system. Next, the
regions of useful color are scanned adaptively to do an equivalent of single pixel edge detection in one stage.
These edges are then processed using Hough Transform to yield lines, which are then segmented, grouped and
approximated to reduce the number of lines representing straight and curved lane markings. The final lines
are then numbered and sent to the master controller for each frame. This allows the master controller to pick
the bounding lane markings and center the vehicle accordingly and navigate autonomously. OpenGL is used to
display the results. The solution has been tested and is being used by Insight Racing team for their entry to the
DARPA Grand Challenge 2007.