Inertial　measurement　unit　(IMU)　fingerprinting　is　a　promising　physical　authentication　technique　based　on　hardware　imperfections　produced　during　sensor　manufacturing.　This　paper　presents　a　two-stage　feature　extraction　process　that　combines　feature　selection　and　mapping;　the　proposed　approach　is　tailored　for　the　lightweight　vehicle-to-everything　(V2X)　application　scenario.　Specifically,　the　selected　features　are　transformed　into　images　via　Gramian　angular　difference　field　(GADF),　Gramian　angular　summation　field　(GASF),　and　Markov　transition　field　(MTF)　mappings,　as　well　as　feature　extraction　implemented　via　a　convolutional　neural　network　(CNN).　Owing　to　the　advances　provided　by　the　proposed　scheme,　a　lightweight　feature　extraction　system　achieves　satisfactory　accuracy　levels　above　99.10%　with　fewer　sample　data　and　a　short　training　time.　The　effectiveness　and　robustness　of　the　developed　approach　were　validated　under　various　driving　conditions　via　20　IMU　sensors,　Arduino,　and　a　Raspberry　Pi　across　20　vehicles.　Additionally,　tests　conducted　across　different　deep　learning　models　demonstrated　the　generalizability　of　the　proposed　preprocessing　and　mapping　methods.