Semantic　segmentation　in　complex　traffic　scenes　is　a　challenging　research　topic　in　the　field　of　computer　vision.　Algorithms　based　on　convolutional　neural　network　has　achieved　more　outstanding　results　than　traditional　algorithms,　but　their　segmentation　performance　needs　to　be　further　improved　when　faced　with　real　scenes　with　complex　backgrounds　and　variable　scales.　In　response　to　this　issue,　this　study　proposes　a　fully　convolutional　network　architecture　based　on　an　multi-scale　attention　pyramid　to　improve　the　performance　of　the　semantic　segmentation　algorithm　from　several　perspectives.　Firstly,　a　lightweight　dual　attention　module　based　on　depth　separable　convolution　is　designed.　This　module　uses　depth　separable　convolution　to　simplify　the　modeling　of　semantic　correlation　between　the　spatial　dimension　and　the　channel　dimension,　and　reduces　the　parameter　quantity　of　the　original　dual　attention　module.　Secondly,　we　constructed　a　multi-scale　attention　pyramid　module,　which　uses　feature　maps　of　different　receptive　fields　or　different　scales　to　output　multiple　prediction　results.　Finally,　an　adaptive　multi-scale　prediction　fusion　module　is　designed.　This　module　adaptively　fuses　the　prediction　results　of　multiple　different　receptive　fields　or　different　scales.　It　further　enhances　the　network＇s　predictive　capabilities　and　generates　detailed　high-resolution　predictive　maps.　Compared　to　the　baseline　DANet,　we　have　achieved　better　results　on　the　Cityscapes,　PASCAL　VOC　2012,　and　COCO　Stuff　datasets.　We　make　the　code　publicly　available　at　https://github.com/Exception-star/AMDANet.　(c)　2021　Elsevier　B.V.　All　rights　reserved.