In　autonomous　driving,　accurately　predicting　the　future　trajectories　of　surrounding　vehicles　is　essential　for　reliable　navigation　and　planning.　Unlike　previous　approaches　that　relied　on　high-definition　maps　and　vehicle　coordinates,　recent　research　seeks　to　predict　the　future　trajectories　of　both　surrounding　and　ego　vehicles　from　a　bird＇s-eye　view　(BEV)　perspective,　leveraging　data　from　multiple　sensors　on　the　vehicle　in　an　end-to-end　manner.　A　key　challenge　in　this　context　is　effectively　modeling　the　spatiotemporal　interactions　between　vehicles.　In　this　paper,　we　propose　a　multi-scale　spatiotemporal　Transformer　network　that　extracts　multi-scale　features　from　images　and　aligns　them　using　a　dedicated　feature　alignment　module.　We　develop　a　divided　space-time　attention　mechanism　to　capture　spatiotemporal　dependencies　in　the　feature　sequence.　Extensive　experiments　on　the　nuScenes　dataset　demonstrate　that　the　proposed　framework　achieves　superior　prediction　accuracy　compared　to　prior　methods,　with　further　performance　gains　as　more　historical　information　is　incorporated.　©　2025　IEEE.