Accurate　prediction　of　vehicle　trajectories　is　crucial　to　the　safety　and　comfort　of　autonomous　vehicles.　Although　several　graph-based　models　have　exhibited　substantial　progress　in　acquiring　spatiotemporal　dependencies　among　vehicles　in　the　driving　environment,　the　potential　for　additional　exploration　in　this　domain　persists.　The　main　reason　is　that　they　concentrated　on　independently　capturing　the　spatial　relations　and　temporal　dependencies,　neglecting　to　incorporate　the　temporal　feature　into　the　spatial　feature　for　co-training,　which　limits　their　ability　to　yield　satisfactory　predictive　accuracy.　Typically,　spatial　and　temporal　correlations　are　coupled　and　should　be　modeled　jointly.　Inspired　by　this,　a　novel　dynamic　graph　neural　network　with　spatial-temporal　synchronization　(STS-DGNN)　for　vehicle　trajectory　prediction　is　proposed,　which　constructs　the　driving　scene　as　dynamic　graphs　and　can　jointly　extract　spatial-temporal　features.　Specifically,　low-order　and　high-order　dynamics　of　vehicle　trajectories　are　considered　collaboratively　in　a　one-stage　framework　rather　than　independently　modeling　the　spatial　relationship　and　temporal　correlations　of　vehicles　in　two-stage　models.　The　proposed　model　also　considers　the　dynamic　nature　of　graph　sequence　by　utilizing　gate　recurrent　unit　(GRU)　to　update　the　graph　neural　network　(GNN)　parameters　dynamically.　The　spatial-temporal　features　are　subsequently　conveyed　to　convolutional　neural　networks　(CNNs)　and　processed　by　a　multilayer　perceptron　(MLP)　to　generate　the　ultimate　trajectories.　Finally,　to　illustrate　the　effectiveness　of　the　STSDGNN　model,　the　model　is　assessed　on　three　well-known　datasets,　namely　highD,　EWAP,　and　UCY.　The　results　confirm　that　our　model　performs　better　at　making　predictions　than　cuttingedge　models.　The　visualization　results　intuitively　explain　that　our　method　can　extract　sophisticated　and　subtle　multivehicle　interactions,　resulting　in　accurate　predictions.