With　the　potential　to　increase　road　safety　and　provide　economic　benefits,　intelligent　vehicles　have　elicited　a　significant　amount　of　interest　from　both　academics　and　industry.　A　robust　and　reliable　vehicle　detection　and　tracking　system　is　one　of　the　key　modules　for　intelligent　vehicles　to　perceive　the　surrounding　environment.　The　millimeter-wave　radar　and　the　monocular　camera　are　two　vehicular　sensors　commonly　used　for　vehicle　detection　and　tracking.　Despite　their　advantages,　the　drawbacks　of　these　two　sensors　make　them　insufficient　when　used　separately.　Thus,　the　fusion　of　these　two　sensors　is　considered　as　an　efficient　way　to　address　the　challenge.　This　paper　presents　a　collaborative　fusion　approach　to　achieve　the　optimal　balance　between　vehicle　detection　accuracy　and　computational　efficiency.　The　proposed　vehicle　detection　and　tracking　design　is　extensively　evaluated　with　a　real-world　data　set　collected　by　the　developed　intelligent　vehicle.　Experimental　results　show　that　the　proposed　system　can　detect　on-road　vehicles　with　92.36%　detection　rate　and　0%　false　alarm　rate,　and　it　only　takes　ten　frames　(0.16　s)　for　the　detection　and　tracking　of　each　vehicle.　This　systemis　installed　on　Kuafu-II　intelligent　vehicle　for　the　fourth　and　fifth　autonomous　vehicle　competitions,　which　is　called　＂Intelligent　Vehicle　Future　Challenge＂　in　China.