Compressive　Sensing　(CS)　is　a　promising　approach　to　compress　data　with　spatial-temporal　correlation　to　reduce　the　communication　cost　in　Vehicular　Sensor　Network　(VSN).　However,　the　current　research　on　the　application　of　CS　in　VSN　does　not　consider　dynamic　changes　in　data　sparsity　and　vehicle　distribution,　which　may　lead　to　unacceptable　reconstruction　accuracy.　At　the　same　time,　the　loss　of　compressed　data　in　transmission　process　also　seriously　affects　data　reconstruction.　A　dynamic　compressive　data　acquisition　in　VSN　is　proposed　in　this　paper.　This　paper　first　proposes　a　VSN　data　acquisition　framework　based　on　CS.　Then,　for　the　problem　of　data　sparsity　and　vehicle　distribution　changes,　a　measurement　rate　adjustment　method　is　proposed.　Finally,　aiming　at　the　problem　of　degradation　of　reconstruction　accuracy　caused　by　the　loss　of　measurements,　this　paper　presents　a　method　to　determine　the　priority　of　measurement,　and　a　data　transmission　strategy　based　on　priority　is　put　forward.　The　experiment　shows　that　the　proposed　dynamic　compressive　data　acquisition　in　VSN　improves　the　reconstruction　accuracy　by　18.4%　compared　with　the　fixed　CS　approach　in　the　VSN.