In　autonomous　driving,　perception　plays　a　critical　role　as　it　serves　as　a　fundamental　requirement　for　both　planning　and　control.　Currently,　most　perception　tasks　are　processed　independently,　which　requires　designing　multiple　models　and　networks　to　handle　multiple　tasks.　This　division　leads　to　multiple　sub-tasks　in　real-world　environments,　making　it　difficult　to　ensure　real-time　performance　and　communication　between　tasks.　The　paper　introduces　an　innovative　neural　network　architecture　termed　CUTransNet,　which　presents　a　unified　approach　capable　of　simultaneously　detecting　drivable　areas,　lane　markings,　and　traffic　objects,　achieving　multi-task　processing　with　a　single　model.　To　build　a　more　robust　feature　encoder,　we　proposed　the　CUT　module　that　combines　the　global　context　ability　of　Transformers.　The　module　is　integrated　into　the　convolutional　neural　network＇s　backbone　to　compensate　for　the　low-level　visual　clues　lost　by　Transformers　and　achieve　higher　detection　accuracy.　Experimental　results　on　BDD100K　demonstrate　that　CUT　model　surpasses　traditional　multi-task　networks　in　both　task　accuracy　and　computational　efficiency　while　maintaining　high　real-time　performance.