Most　autonomous　vehicles　build　their　perception　systems　on　expensive　sensors,　such　as　LIDAR,　RADAR,　and　high-precision　Global　Positioning　System　(GPS).　However,　cameras　can　provide　richer　sensing　at　a　considerably　lower　cost,　this　makes　them　a　more　appealing　alternative.　A　driving　assistance　system　(DAS)　based　on　monocular　vision　has　gradually　become　a　research　hotspot,　and　inter-vehicle　distance　estimation　based　on　monocular　vision　is　an　important　technology　in　DAS.　There　are　still　constrains　in　the　existing　methods　for　estimating　the　inter-vehicle　distance　based　on　monocular　vision,　such　as　low　accuracy　when　distance　is　larger,　unstable　accuracy　for　different　types　vehicles,　and　significantly　poor　performance　on　distance　estimation　for　severely　occluded　vehicles.　To　improve　the　accuracy　and　robustness　of　ranging　results,　this　study　proposes　a　monocular　vision　end-to-end　inter-vehicle　distance　estimation　method　based　on　3D　detection.　The　actual　area　of　the　rare　view　of　the　vehicle　and　the　corresponding　projection　area　in　the　image　are　obtained　by　3D　detection　method.　An　area-distance　geometric　model　is　then　established　on　the　basis　of　the　camera　projection　principle　to　recover　distance.　Our　method　shows　its　potential　in　complex　traffic　scenarios　by　testing　the　test　set　data　provided　on　the　real-world　computer　vision　benchmark,　KITTI.　The　experimental　results　have　superior　performance　than　the　existing　published　methods.　Moreover,　the　accuracy　of　occluded　vehicle　ranging　results　can　reach　approximately　98%,　while　the　accuracy　deviation　between　vehicles　with　different　visual　angles　is　less　than　2%.