Semantic　segmentation　of　complex　traffic　scenes　is　a　challenging　research　topic　in　the　field　of　computer　vision.　In　order　to　reduce　the　dependence　of　the　segmentation　model　on　the　pixel-level　annotation　data　of　traffic　scenes,　we　propose　a　semi-supervised　semantic　segmentation　algorithm　model　based　on　knowledge　distillation.　The　self-correcting　module　is　used　to　iteratively　optimize　the　weakly　labeled　data　and　generate　pseudo-labels.　The　collaborative　learning　of　multiple　students　enhances　the　ability　of　students　to　accept　potential　knowledge　online.　The　proposed　method　uses　the　knowledge　distillation　structure　of　the　teacher-student　network　to　transfer　semantic　structured　information.　It　solves　the　problem　of　insufficient　fine　label　samples　in　the　Cityscapes　dataset.　The　network　performance　obtained　by　training　with　the　original　label　data　combined　with　the　pseudo　label　data　can　be　further　improved.　©　2021　IEEE.