Common bird-view Transformation for Robust Lane Detection

Deep learning based lane detection algorithms inevitably suffer performance drop brought by domain shift, which usually consists of difference image appearance, spatial layout, etc. To address this problem, we propose a Common Bird-view Transformation that eliminates the lane geometric differences between training and test datasets by projecting images in both domains into a common aerial viewpoint. Experiments show that by applying our methods, the performance on test datasets can be significantly improved.