Pedestrian　trajectory　prediction　has　become　a　crucial　task　in　the　field　of　autonomous　driving.　To　improve　the　accuracy　of　pedestrian　trajectory　prediction,　researchers　primarily　concentrate　on　tackling　two　key　challenges.　One　is　to　extract　the　intricate　interactions　between　pedestrians,　and　the　other　is　to　simulate　the　diverse　decision-making　intentions　displayed　by　pedestrians.　However,　most　existing　methods　utilize　the　distance　attribute　to　build　the　relationship　of　pedestrians　only,　but　ignore　other　features　such　as　the　steering.　Besides,　some　generation　theory　based　methods　would　lead　to　substantial　deviations　in　the　generated　trajectory　distribution　since　they　always　refine　the　variational　likelihood　lower　bound　of　observed　data.　In　this　paper,　we　adopt　Graph　theory　and　propose　a　Spatial-Temporal　Dual　Graph　neural　network　for　pedestrian　trajectory　prediction.　In　the　proposed　method,　we　construct　a　pedestrian　graph　structure　by　utilizing　pedestrian　distance　and　steering　features　to　extract　more　comprehensive　interaction　information.　Additionally,　we　introduces　the　flow-based　Glow-PN　module　to　predict　multi-modal　trajectories　of　pedestrians.　Experimental　results　on　two　public　benchmark　datasets　show　that　our　model　achieves　superior　prediction　performance　and　operates　effectively　in　diverse　scenarios.　©　2024　IEEE.