Roughness　is　one　of　the　main　technical　indexes　of　pavement　performance.　Accurate　and　rapid　IRI　detection　has　great　significance　for　pavement　maintenance　and　management.　In　this　paper,　the　self-developed　smart　phone　App　was　used　to　collect　driving　status　and　other　relevant　datas.　Driving　datas　such　as　vibration　acceleration　and　speed　were　collected　through　driving　experiments　in　real　road,　and　the　feasibility　of　detecting　road　roughness　IRI　by　using　these　driving　datas　was　studied.　It　proposed　a　method　to　take　the　composite　vibration　acceleration　as　the　index　of　driving　vibration　acceleration　and　established　the　normalized　kNN　eigenvector　space.　The　results　show　that　the　proposed　method　is　simple　and　easy　to　apply　and　it　improves　the　detection　accuracy　of　pavement　roughness　IRI　by　using　smart　phones.　The　absolute　evaluation　accuracy　of　IRI　detection　reaches　more　than　78%,　and　the　relative　accuracy　after　considering　adjacent　evaluations　reaches　more　than　96%,　which　meets　the　real-time　detection　and　monitoring　of　pavement　roughness　IRI　in　the　road　network.　It　has　a　promising　application　prospects　in　improving　the　pertinence　of　IRI　detection　of　pavement　roughness　and　reducing　the　overall　detection　amount　of　pavement　performance,　thus　can　provide　macroscopic　guidance　for　the　maintenance　decision　and　management　of　road　network　pavement.　©　2022,　Editorial　Department,　Journal　of　South　China　University　of　Technology.　All　right　reserved.