The　purpose　of　this　study　is　to　investigate　the　development　and　evaluation　of　a　high　integrity　navigation　system　for　vehicular　applications,　focusing　on　the　fusion　of　Global　Positioning　System　(GPS)　and　Inertial　Measurement　Unit　(IMU)　data.　The　study　compares　the　similarities　and　differences　in　performance　between　the　Kalman　filter　(a　traditional　GPS/IMU　integration　method)　and　machine　learning　models.　The　experiments　are　based　on　KITTI　GPS/IMU　sequences,　and　the　impact　of　these　methods　on　the　performance　of　the　navigation　system　is　evaluated　by　introducing　different　noise　levels.　First,　a　Kalman　filter　is　used　to　fuse　the　GPS/IMU　data　and　the　estimation　error　of　the　trajectories　is　investigated　by　adjusting　the　noise　level.　Second,　a　machine　learning　model　is　introduced　to　compare　its　performance　under　different　parameter　configurations　using　random　forest　regression　as　an　example.　In　addition,　the　effects　of　different　parameters　on　the　performance　of　the　two　methods　are　analyzed,　which　provides　an　important　reference　for　choosing　a　suitable　navigation　system.　The　results　show　that　the　Kalman　filter　model　basically　outperforms　the　machine　learning　model　in　terms　of　mean　square　error　(MSE)　and　mean　absolute　error　(MAE)　of　trajectory　estimation.　However,　the　random　forest　regression　model　performs　best　after　tuning.　The　paper　concludes　with　a　comparison　between　the　Kalman　filter　and　the　random　forest　regression　model,　emphasizing　the　robustness　and　adaptability　of　the　random　forest　regression　model　in　GPS/IMU　fusion.　The　research　results　provide　insights　for　the　selection　and　design　of　navigation　systems.　©　2024　SPIE.