In　this　paper,　we　propose　vertical　attention　and　spatial　attention　network　(VSANet),　which　is　a　semantic　segmentation　method　based　on　Deeplabv3+　and　attention　module,　for　improving　semantic　segmentation　effect　for　autonomous　driving　road　scene　images.　The　improvement　of　this　paper　is　primarily　on　the　following　two　aspects.　One　is　that　this　paper　introduces　the　spatial　attention　module　(SAM)　after　the　atrous　convolution,　which　effectively　obtains　more　spatial　context　information.　Second,　by　studying　the　road　scene　image,　it　is　found　that　there　are　considerable　differences　in　the　pixel-level　distribution　of　the　horizontal　segmentation　area　in　the　image.　For　this　reason,　this　paper　introduces　the　vertical　attention　module　(VAM),　which　can　better　segment　the　road　scene　image.　A　large　number　of　experimental　results　indicate　that　the　segmentation　accuracy　of　the　proposed　model　is　improved　by　1.94%　compared　with　the　Deeplabv3+　network　model　on　the　test　dataset　of　Cityscapes　dataset.　©　2021　IEEE.