Advanced　driver　assistance　systems　(ADAS)　based　on　monocular　vision　are　rapidly　becoming　a　popular　research　subject.　In　ADAS,　inter-vehicle　distance　estimation　from　an　in-car　camera　based　on　monocular　vision　is　critical.　At　present,　related　methods　based　on　a　monocular　vision　for　measuring　the　absolute　distance　of　vehicles　ahead　experience　accuracy　problems　in　terms　of　the　ranging　result,　which　is　low,　and　the　deviation　of　the　ranging　result　between　different　types　of　vehicles,　which　is　large　and　easily　affected　by　a　change　in　the　attitude　angle.　To　improve　the　robustness　of　a　distance　estimation　system,　an　improved　method　for　estimating　the　distance　of　a　monocular　vision　vehicle　based　on　the　detection　and　segmentation　of　the　target　vehicle　is　proposed　in　this　paper　to　address　the　vehicle　attitude　angle　problem.　The　angle　regression　model　(ARN)　is　used　to　obtain　the　attitude　angle　information　of　the　target　vehicle.　The　dimension　estimation　network　determines　the　actual　dimensions　of　the　target　vehicle.　Then,　a　2D　base　vector　geometric　model　is　designed　in　accordance　with　the　image　analytic　geometric　principle　to　accurately　recover　the　back　area　of　the　target　vehicle.　Lastly,　area-distance　modeling　based　on　the　principle　of　camera　projection　is　performed　to　estimate　distance.　The　experimental　results　on　the　real-world　computer　vision　benchmark,　KITTI,　indicate　that　our　approach　achieves　superior　performance　compared　with　other　existing　published　methods　for　different　types　of　vehicles　(including　front　and　sideway　vehicles).