Recently,　the　3D　object　detection　problem　in　the　autonomous　driving　vehicle　based　on　the　look-around　camera　has　introduced　the　spatio-temporal　consistency　to　improve　the　detection　or　instance　prediction　performance.　Dense　BEV　spatial　features　and　rich　temporal　information　are　expected　to　make　up　for　the　lack　of　object　attention,　ignoring　the　importance　of　dynamic　target　motion　and　position　information　in　future　image　frames.　We　propose　a　novel　framework　called　PreBEV.　PreBEV　introduces　the　predictive　stream　method　to　construct　the　query　anchor　box　and　learns　the　implicit　motion　characteristics　of　sequence　images　through　the　close　association　with　pixels,　which　effectively　utilizes　the　adaptability　of　BEV　and　image　views　to　alleviate　the　computational　pressure　caused　by　dense　queries.　In　addition,　this　framework　proposes　BEV　feature　fusion　based　on　predictive　flow　guidance,　which　avoids　the　limitations　of　traditional　simple　time-series　fusion　strategies　(weighted　sum,　series)　set　by　hand,　makes　full　use　of　the　motion　characteristics　of　objects　and　improves　the　attention　to　dynamic　objects.　PreBEV　effectively　simplifies　the　multi-task　objective　and　improves　the　prediction　stability　and　detection　accuracy.　The　experimental　results　show　that　PreBEV　performs　superior　on　the　NuScenes　dataset,　which　brings　a　new　research　paradigm　to　the　field　of　BEV　detection.　©　2024　IEEE.