Vision-centric　motion　prediction　concentrates　on　accurately　determining　the　instance　mask　and　its　future　trajectory　from　surround-view　cameras,　which　manifests　inherent　merits　such　as　holistic　perspective　and　fully-differentiable　spirit.　Nonetheless,　it　is　still　impeded　by　sparse　bird＇s-eye　view　(BEV)　representation　and　unfavorable　temporal　context　across　frames,　resulting　in　a　sub-optimal　solution　to　decision-making　and　vehicle　navigation.　In　this　work,　we　propose　a　novel　Difference-guide　Motion　Prediction　for　vision-centric　autonomous　driving,　that　is　DMP,　where　it　integrates　BEV　map　refinement　with　spatial-temporal　relation　modeling　in　a　hierarchical　manner.　Specifically,　a　bidirectional　view　projection　strategy　is　introduced　for　the　complementary　BEV　feature　generation　via　depth-consistency　correction.　To　promote　spatiotemporal　context　aggregation,　we　design　a　difference-guided　motion　approach　by　offset　approximation　to　align　motion-aware　cues　between　adjacent　frames,　and　a　dual-stream　pyramid　module　is　further　developed　for　historical　information　fusion　and　future　instance　segmentation　during　specific　durations.　Extensive　experiments　on　the　large-scale　nuScenes　dataset　demonstrate　that　it　outperforms　the　baselines　by　a　remarkable　margin　and　delivers　competitive　motion　prediction　across　diverse　scenarios　and　range　settings,　suggesting　its　effectiveness　and　superiority.　The　details　will　be　available　at　https://github.com/pupu-chenyanyan/DMP-VAD.