Nighttime　semantic　segmentation　is　an　important　but　challenging　research　problem　for　autonomous　driving.　The　major　challenges　lie　in　the　small　objects　or　regions　from　the　under-/over-exposed　areas　or　suffer　from　motion　blur　caused　by　the　camera　deployed　on　moving　vehicles.　To　resolve　this,　we　propose　a　novel　hard-class-aware　module　that　bridges　the　main　network　for　full-class　segmentation　and　the　hard-class　network　for　segmenting　aforementioned　hard-class　objects.　In　specific,　it　exploits　the　shared　focus　of　hard-class　objects　from　the　dual-stream　network,　enabling　the　contextual　information　flow　to　guide　the　model　to　concentrate　on　the　pixels　that　are　hard　to　classify.　In　the　end,　the　estimated　hard-class　segmentation　results　will　be　utilized　to　infer　the　final　results　via　an　adaptive　probabilistic　fusion　refinement　scheme.　Moreover,　to　overcome　over-smoothing　and　noise　caused　by　extreme　exposures,　our　model　is　modulated　by　a　carefully　crafted　pretext　task　of　constructing　an　exposure-aware　semantic　gradient　map,　which　guides　the　model　to　faithfully　perceive　the　structural　and　semantic　information　of　hard-class　objects　while　mitigating　the　negative　impact　of　noises　and　uneven　exposures.　In　experiments,　we　demonstrate　that　our　unique　network　design　leads　to　superior　segmentation　performance　over　existing　methods,　featuring　the　strong　ability　of　perceiving　hard-class　objects　under　adverse　conditions.　©　2024　Copyright　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.