Autonomous　vehicles　have　become　a　hot　spot　of　the　automotive　industry,　many　cities　have　claimed　that　autonomous　vehicles　should　be　capable　of　recognizing　gestures　used　by　traffic　police.　Traditional　traffic　police　gesture　recognition　methods　rely　on　depth-sensor　or　wearable-devices,　which　limits　their　availability　in　the　domain　of　the　intelligent　vehicle.　Vision-based　methods　have　fewer　requirements　for　distance,　but　the　modeling　process　is　challenging　due　to　the　complexity　of　the　visual　scenes.　Inspired　by　the　recent　success　in　vision-based　pose　estimation　networks　such　as　Convolutional　Pose　Machine　(CPM),　in　this　paper,　we　propose　a　novel　vision-based　human-machine　interface　to　recognize　eight　kinds　of　Chinese　traffic　police　gestures　and　apply　it　in　the　real-time　recognition　tasks.　This　method　integrates　a　modified　CPM　network　and　two　kinds　of　handcrafted　features:　Relative　Bone　Length　and　Angle　with　Gravity　as　spatial　domain　features,　and　adopt　a　Long　short-term　memory　(LSTM)　network　to　extract　temporal　domain　features.　To　train　and　validate　our　method,　we　create　a　gestures　dataset　with　two　hours　of　traffic　police　gesture　videos,　which　has　3354　gesture　instances.　The　experiment　results　show　that　the　proposed　method　is　capable　of　recognizing　traffic　police　gestures,　and　is　fast　enough　for　online　gesture　prediction.　(C)　2019　Elsevier　B.V.　All　rights　reserved.