Pedestrian　trajectory　prediction　plays　a　pivotal　role　in　ensuring　the　safety　and　efficiency　of　various　applications,　including　autonomous　vehicles　and　traffic　management　systems.　This　paper　proposes　a　novel　method　for　pedestrian　trajectory　prediction,　called　multi-stage　goal-driven　network　(MGNet).　Diverging　from　prior　approaches　relying　on　stepwise　recursive　prediction　and　the　singular　forecasting　of　a　long-term　goal,　MGNet　directs　trajectory　generation　by　forecasting　intermediate　stage　goals,　thereby　reducing　prediction　errors.　The　network　comprises　three　main　components:　a　conditional　variational　autoencoder　(CVAE),　an　attention　module,　and　a　multi-stage　goal　evaluator.　Trajectories　are　encoded　using　conditional　variational　autoencoders　to　acquire　knowledge　about　the　approximate　distribution　of　pedestrians＇　future　trajectories,　and　combined　with　an　attention　mechanism　to　capture　the　temporal　dependency　between　trajectory　sequences.　The　pivotal　module　is　the　multi-stage　goal　evaluator,　which　utilizes　the　encoded　feature　vectors　to　predict　intermediate　goals,　effectively　minimizing　cumulative　errors　in　the　recursive　inference　process.　The　effectiveness　of　MGNet　is　demonstrated　through　comprehensive　experiments　on　the　JAAD　and　PIE　datasets.　Comparative　evaluations　against　state-of-the-art　algorithms　reveal　significant　performance　improvements　achieved　by　our　proposed　method.　　©　2024　IEEE.