In　autonomous　driving,　the　fusion　of　multiple　sensors　is　considered　essential　to　improve　the　accuracy　and　safety　of　3D　object　detection.　Currently,　a　fusion　scheme　combining　low-cost　cameras　with　highly　robust　radars　can　counteract　the　performance　degradation　caused　by　harsh　environments.　In　this　paper,　we　propose　the　IRBEVF-Q　model,　which　mainly　consists　of　BEV　(Bird＇s　Eye　View)　fusion　coding　module　and　an　object　decoder　module.The　BEV　fusion　coding　module　solves　the　problem　of　unified　representation　of　different　modal　information　by　fusing　the　image　and　radar　features　through　3D　spatial　reference　points　as　a　medium.　The　query　in　the　object　decoder,　as　a　core　component,　plays　an　important　role　in　detection.　In　this　paper,　Heat　Map-Guided　Query　Initialization　(HGQI)　and　Dynamic　Position　Encoding　(DPE)　are　proposed　in　query　construction　to　increase　the　a　priori　information　of　the　query.　The　Auxiliary　Noise　Query　(ANQ)　then　helps　to　stabilize　the　matching.　The　experimental　results　demonstrate　that　the　proposed　fusion　model　IRBEVF-Q　achieves　an　NDS　of　0.575　and　a　mAP　of　0.476　on　the　nuScenes　test　set.　Compared　to　recent　state-of-the-art　methods,　our　model　shows　significant　advantages,　thus　indicating　that　our　approach　contributes　to　improving　detection　accuracy.