Three-dimensional　(3D)　object　detection　is　of　great　significance　for　avoiding　collisions　between　vehicles　and　obstacles　in　autonomous　driving.　In　particular,　the　recent　3D　object　detection　methods　based　on　supervised　learning　are　widely　studied　to　achieve　excellent　performance.　However,　the　3D　labels　for　training　in　such　methods　are　expensive　and　often　difficult　to　be　collected.　To　solve　this　issue,　we　propose　a　monocular　3D　vehicle　detection　method.　First,　we　propose　a　general　mathematical　K-means-like　method　for　clustering　arbitrary　object　contours　into　linear　equations.　Second,　the　position,　orientation　and　dimensions　of　the　vehicle　can　be　estimated　by　applying　K-means-like　method　without　the　need　for　3D　labels　in　the　contour　of　the　vehicle.　Finally,　given　the　2D　object　detection,　we　maximize　a　posterior　probability　of　vehicle　position,　orientation　and　dimensions　to　improve　the　accuracy　of　the　3D　object　detection　based　on　the　results　of　K-means-like　method.　We　evaluate　the　proposed　algorithm　on　the　dataset　collected　by　the　vehicle-side　and　road-side　cameras　in　the　cooperative　vehicle　infrastructure　system　(CVIS).　Compared　with　the　state-of-art　Deep3DBox　and　SMOKE　methods,　the　evaluated　results　show　that　the　detection　accuracy　of　3D　object　of　our　method　is　1.4%　higher　than　that　of　Deep3DBox　in　the　vehicle-side　system,　while　for　the　road-side　camera,　the　proposed　method　has　3.86%　and　4.37%　higher　accuracy　than　Deep3DBox　and　SMOKE,　respectively.　Thus,　the　proposed　method　can　be　seen　as　an　effective　3D　object　detection　method　in　the　intelligent　transportation　system　and　CVIS.　©　2001-2012　IEEE.