Data　association　is　a　difficult　problem　for　simultaneous　localization　and　mapping　(SLAM)　of　intelligent　vehicle.　In　order　to　obtain　data　association　results　quickly　and　accurately,　a　new　fast　joint　data　association　(FJDA)　algorithm　was　proposed　in　this　paper.　The　advantages　of　the　sequential　compatibility　nearest　neighbor　(SCNN)　algorithm,　which　is　easy　to　implement,　and　the　concept　of　optimality　of　joint　compatibility　branch　and　bound　(JCBB)　algorithm　were　combined.　Firstly,　SCNN　algorithm　was　used　to　process　all　measurement-feature　pairs　in　the　local　map　and　the　association　results　were　obtained.　Secondly,　the　accuracy　of　the　association　result　was　judged.　If　the　association　failed,　DBSCAN　algorithm　was　applied　to　divide　the　current　measurement　into　several　groups,　and　then　JCBB　algorithm　was　performed　in　each　group.　Eventually,　the　associated　solution　of　each　group　was　fused　to　get　the　final　association　results.　The　performance　of　the　proposed　algorithm,　SCNN　algorithm　and　JCBB　algorithm　were　compared　through　simulation　experiments.　The　simulation　results　show　that　the　proposed　algorithm　has　high　real-time　ability　and　high　accuracy.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.