LANE MARKING DETECTION IN CLUTTERED ENVIRONMENT

Achallengingtaskoffutureintelligentvehiclesisroadfollowing .Itincludeslanedetection (whichmainlystudiesthelocalizationoftheroad ,thede terminationoftherelativepositionbetweenvehicleandroad ,andtheanalysisofthevehicle’sheadingdirection) ,andobstacledetection (whichmainlystudieslocalizationofpossibleobstaclesonthevehi cle’spath) .Lanedetectionistheproblemoflocat inglaneboundarieswithoutpriorknowledgeoftheroadgeometry .Moregenerally ,however,lanede tectionhasbeenreducedtothelocalizationofspecif…

LANE MARKING DETECTION IN CLUTTERED ENVIRONMENT

Objective To determine the positions of marking in the presence of distracting shadows, highlight, pavement cracks, etc. Methods RGB color space is transformed into I 1 I 2 I 3 color space and I 2 component was used to form a new image with less effect of the clutter. Using an improved edge detection operator, an edge strength map was produced, and binarilized by adaptive thresholds. The binary image was labeled and circularity of all connected components is calculated. The Self Organizing Mapping is adopted to extract regions which imply potential marking. Finally the position of marking was obtained by curve fitting. Results Color information was utilized fully, all thresholds were set adaptively and lane marking could be detected in challenging images with shadows, highlight or other cars. Conclusion The method based on circularity of connected components shows its outstanding robustness to lane marking detection and has a wide variety of applications in the areas of vehicle autonomous navigation and driver assistance system.