10 September 2007 Lane detection system for autonomous vehicle navigation
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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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amit Bhatia, "Lane detection system for autonomous vehicle navigation", Proc. SPIE 6764, Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, 67640S (10 September 2007); doi: 10.1117/12.752627; https://doi.org/10.1117/12.752627

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