In　this　paper,　we　propose　SYGNet　to　strengthen　the　scene　parsing　ability　of　autonomous　driving　under　complicated　road　conditions.　The　SYGNet　includes　feature　extraction　component　and　SVD-YOLO　GhostNet　component.　The　SVD-YOLO　GhostNet　component　combines　Singular　Value　Decomposition　(SVD),　You　Only　Look　Once　(YOLO)　and　GhostNet.　In　the　feature　extraction　component,　we　propose　an　algorithm　based　on　VoxelNet　to　extract　point　cloud　features　and　image　features.　In　SVD-YOLO　GhostNet　component,　the　image　data　is　decomposed　by　SVD,　and　we　obtain　data　with　stronger　spatial　and　environmental　characteristics.　YOLOv3　is　used　to　obtain　the　future　map,　then　convert　to　GhostNet,　which　is　used　to　realize　the　real-time　scene　parsing.　We　use　KITTI　data　set　to　perform　our　experiments　and　the　results　show　that　the　SYGNet　is　more　robust　and　can　further　enhance　the　accuracy　of　real-time　driving　scene　parsing.　The　model　code,　data　set,　and　results　of　the　experiments　in　this　paper　are　available　at:　https://github.com/WangHewei16/SYGNetfor-Real-time-Driving-Scene-Parsing.　©　2022　IEEE.