Lane Detection Using Fuzzy Logic

In order to implement the edge enhancement and lane line detection of the shadowy and strong illumination et al,an image processing method based on fuzzy logic is proposed.The maximum information entropy principle is introduced to obtain the parameter of the membership function through the histogram.The fuzzy logic method is used to enhance the contrast between lane line pixels and road pixels.During the lane detection,the HT (Hough transform) is employed to implement the lane straight line detection and the 3rd order curve model is used to curve line fitting after image preprocessing using fuzzy logic.In order to reduce computational time,the Kalman filter is used to establish the dynamic ROI (region of interest) and predict the lane line parameters for lane line fitting according to the lane parameters of former image for real-time detection.By comparison,it indicates that the method can intensify lane lines information effectively and enhance the contrast between the lane line pixels and road pixels.Experiments results show that lane line detection using fuzzy logic can estimate the lane line parameters accurately and detect the lane lines stably under different illumination conditions.