Autonomous cars establish driving strategies by employing detection of the road. Most of the previous methods detect road with image semantic segmentation, which identifies pixel-wise class labels and predicts segmentation masks. We propose U-net<sup>1</sup> , a novel segmentation network by learning deep convolution and deconvolution features. The architecture consists of an encoder and decoder network. The encoder network is trainable with a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the convolutional layers. The novelty of U-net lies in the manner in which the decoder deconvolves its lower resolution input feature maps. Specifically, the decoder network conjoins the encoder convolution features and decoder deconvolution features using the "concat" function, which achieves a good mapping between classes and filters at the expansion side of the network. The network is trained end-to-end and yields precise pixel-wise predictions at the original input resolution.