As the basis of scenes understanding for autonomous driving, lane segmentation is always a challenge due to the various illumination conditions, heavy traffics and richly-textured roads. Because of the heavily biased distribution of lane/non-lane pixels, it is hard to achieve satisfying results by using image segmentation networks such as fully convolution neural networks (FCN). In this paper, we propose a new loss function to tackle the unbalanced data distribution problem. It has shown that the loss function significantly improves the performance of available segmentation networks such as FCN on the lane segmentation task.