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In this paper, we propose an encoder-decoder based deep convolutional network for semantic segmentation in autonomous driving scenarios. The architecture of the proposed model is based on VGG16 . Residual learning is introduced to preserve the context while decreasing the size of feature maps between the stacks of convolutional layers. Also, the resolution is preserved through shortcuts from the encoder stage to the decoder stage. Experiments are conducted on popular benchmark datasetsdoi:10.23919/eusipco.2018.8553161 dblp:conf/eusipco/NareshLO18 fatcat:uuy76zrzcjcjzb2qyrmklb3ziu