Autonomous Lane Keeping Solution Based On Adaptive Gray Threshold And Region Of Interest

Autonomous lane keeping is an important technology in intelligent transportation, which is used to avoid unnecessary traffic accidents caused by lane departure. To adapt different lighting environment, and make up ordinary Hough transform’s shortcomings of tardiness and poor immunity, we propose an improved algorithm by using adaptive gray threshold and setting Region of interest (ROI), to do the quick Hough transform for tracking lane line, implementing autonomous lane keeping. The dynamic adaptive threshold method can be suitable with different lighting conditions and quickly, accurately remove most of the information not relative to lane line. Meanwhile setting ROI can let the program only care about the specific region which can provide useful information and further reduce the processing data.And then on the basic of identification, we put forward some efficient innovation strategy about the control logic of straight state, curve state and the transition state. In straight status, we use a strategy of tracking middle point; in curve state, a strategy of tracking the outside arc is quoted. The strategy of transition state is combined with ROI, which will be migrated with, the changes of status.The experiment proves that this solution is robust so that can be suitable with different light conditions and greatly raises efficiency:...