Lane detection of multi-visual-features fusion based on D-S theory

A novel lane detection algorithm based on multi-visual-features fusion by using D-S evidence theory is introduced to improve the robustness against illumination variations, shadows and road surface cracks, etc. First, the gradient magnitude, gradient direction, hue and value detection operators are chosen to construct the evidence bodies, for which the basic probability assignment functions are designed respectively. Then, after the pretreatment of conflict focal elements, the evidences are combined to obtain the weights of each pixel as lane candidate points according to the maximum reliability criterion. Finally, the parameters of piecewise linear lane model are calculated by weighted Hough transform with constraint and KF is used for lane tracking. The experimental results show that this method can achieve higher reliability and adaptability for lane detection than the algorithm simply using the edge or color feature, and satisfies the real-time requirement for navigation.