  Semantic segmentation is an essential technology for self-driving cars to
comprehend their surroundings. Currently, real-time semantic segmentation
networks commonly employ either encoder-decoder architecture or two-pathway
architecture. Generally speaking, encoder-decoder models tend to be
quicker,whereas two-pathway models exhibit higher accuracy. To leverage both
strengths, we present the Spatial-Assistant Encoder-Decoder Network (SANet) to
fuse the two architectures. In the overall architecture, we uphold the
encoder-decoder design while maintaining the feature maps in the middle section
of the encoder and utilizing atrous convolution branches for same-resolution
feature extraction. Toward the end of the encoder, we integrate the asymmetric
pooling pyramid pooling module (APPPM) to optimize the semantic extraction of
the feature maps. This module incorporates asymmetric pooling layers that
extract features at multiple resolutions. In the decoder, we present a hybrid
attention module, SAD, that integrates horizontal and vertical attention to
facilitate the combination of various branches. To ascertain the effectiveness
of our approach, our SANet model achieved competitive results on the real-time
CamVid and cityscape datasets. By employing a single 2080Ti GPU, SANet achieved
a 78.4 % mIOU at 65.1 FPS on the Cityscape test dataset and 78.8 % mIOU at 147
FPS on the CamVid test dataset. The training code and model for SANet are
available at https://github.com/CuZaoo/SANet-main
