Previous　camera　self-calibration　methods　have　exhibited　certain　notable　shortcomings.　On　the　one　hand,　they　either　exclusively　emphasized　scene　cues　or　solely　focused　on　vehicle-related　cues,　resulting　in　a　lack　of　adaptability　to　diverse　scenarios　and　a　limited　number　of　effective　features.　Furthermore,　these　methods　either　solely　utilized　geometric　features　within　traffic　scenes　or　exclusively　extracted　semantic　information,　failing　to　comprehensively　consider　both　aspects.　This　limited　the　comprehensive　feature　extraction　from　scenes,　ultimately　leading　to　a　decrease　in　calibration　accuracy.　Additionally,　conventional　vanishing　point-based　self-calibration　methods　often　required　the　design　of　additional　edge-background　models　and　manual　parameter　tuning,　thereby　increasing　operational　complexity　and　the　potential　for　errors.　Given　these　observed　limitations,　and　in　order　to　address　these　challenges,　we　propose　an　innovative　roadside　camera　self-calibration　model　based　on　the　Transformer　architecture.　This　model　possesses　a　unique　capability　to　simultaneously　learn　scene　features　and　vehicle　features　within　traffic　scenarios　while　considering　both　geometric　and　semantic　information.　Through　this　approach,　our　model　can　overcome　the　constraints　of　prior　methods,　enhancing　calibration　accuracy　and　robustness　while　reducing　operational　complexity　and　the　potential　for　errors.　Our　method　outperforms　existing　approaches　on　both　real-world　dataset　scenarios　and　publicly　available　datasets,　demonstrating　the　effectiveness　of　our　approach.