In　response　to　the　growing　demand　for　pedestrian　crosswalk　safety　and　visibility　in　the　era　of　autonomous　driving,　this　paper　proposes　and　designs　a　crosswalk　environment　monitoring　system　based　on　virtual-physical　twin　technology.　Leveraging　Unreal　Engine　(UE)　and　Carla　simulation　software,　the　system　constructs　a　high-precision　virtual　environment,　integrating　real-time　data　from　on-site　sensors　and　weather　APIs　to　achieve　comprehensive　and　dynamic　monitoring　of　crosswalk　conditions.　A　key　innovation　of　the　system　is　the　application　of　a　Vision-Language　Model　(VLM),　which　automatically　translates　monitoring　data　into　understandable　natural　language　text　while　also　enabling　threshold-based　warnings　and　safety　alerts.　This　enhances　the　intelligence　and　automation　of　the　monitoring　system.　Compared　to　traditional　monitoring　methods　that　primarily　rely　on　graphical　visualization,　the　proposed　approach　significantly　improves　data　processing　efficiency　and　safety　alert　capabilities,　offering　a　more　efficient,　real-time,　and　intelligent　solution　for　traffic　safety　management　in　the　context　of　autonomous　driving.　©　2025　The　Authors.