According　to　the　requirement　of　recognizing　traffic　police　gestures　for　driver　assistance　systems　and　intelligent　vehicles,　a　universal　model　for　dynamic　traffic　police　gesture　recognition　is　firstly　introduced,　of　which　can　accurately　present　the　spatial　context　(such　as　the　relative　lengths　of　skeletons,　the　angles　between　each　skeleton　w.　r.　t.　gravity,　and　part　features)　of　the　traffic　police　gestures.　Secondly,　an　architecture　which　can　respectively　extract　spatial　context　and　temporal　features　of　dynamic　traffic　police　gesture　is　proposed.　Meanwhile,　deep　neural　network　and　LSTM　are　introduced　to　build　a　high-resolution　traffic　police　gestures　recognizer　(namely　HRTPGR).　At　last,　the　open　Police　Gesture　Dataset　is　used　to　train　and　test　TPGR,　and　the　experimental　results　show　that　the　TPGR　achieves　a　state-of-the-art　accuracy　with　98.7%　for　dynamic　traffic　police　gestures　recognition,　and　has　strong　anti-interference　ability　to　light,　background　and　gesture　shape　changes.　©　2023　IEEE.