A lane detection algorithm is proposed using intersecting cortical model (ICM) in view of the weak universality and high complexity of the traditional methods in detecting the unstructured lane whose circumstance is complex and diverse.ICM has the superiority which is much closer to the information processing mechanism of biological vision.The ICM can distinguish objects and background dynamically according to the relevance between pixel and its neighbor pixels.As the best threshold and cyclic iterative times of ICM is artificially given it can not realize segmentation automatically.Therefore the decision mechanism of the minimum cross entropy is introduced to determine the cyclic iterative times and the best threshold automatically.The result of experiments show that the precision of the algorithm is high

and it also has very strong adaptability to some unconventional lanes.