Locating　vehicles　in　the　surrounding　environment　and　distinguishing　their　poses　are　crucial　in　autonomous　driving　systems.　An　intelligent　transportation　system　expects　a　vision-based　object　detector　not　only　to　detect　and　classify　vehicles　but　also　to　recognize　the　poses　of　them　in　estimating　their　future　movement　direction.　However,　most　of　the　current　object　detection　algorithms　only　classify　different　classes　of　vehicles;　and　there　are　rare　studies　on　classifying　different　poses　within　the　same　class　of　vehicles.　In　addition,　the　currently　available　datasets　have　no　annotation　data　that　are　specific　to　different　poses　of　intra-class　vehicles.　To　achieve　the　vehicles　pose　recognition,　we　build　a　vehicle　pose　dataset　based　on　the　Cityscapes　dataset.　Furthermore,　we　also　improve　the　network　model　of　the　YOLO　v2　detector　to　support　simultaneous　vehicle　detection　and　pose　classification　with　an　improvement　of　10.71%　mean　average　precision　(mAP)　comparing　to　the　default　network　which　runs　in　real-Time　with　29　FPS.　©　2019　IEEE.