Path　tracking　plays　an　essential　role　in　autonomous　vehicles.　To　ensure　tracking　accuracy　and　improve　tracking　adaptability　in　different　velocities,　a　path　tracking　strategy　based　on　an　improved　model　predictive　control　(MPC)　method　is　presented　in　this　research.　First,　a　path　tracking　controller　based　on　improved　MPC　based　an　online　Updating　algorithm　is　constructed.　The　update　mechanism　is　triggered　by　using　the　cosine　similarity,　when　the　cosine　similarity　is　lower　than　the　predefined　threshold　value,　making　the　state　space　and　cost　function　of　MPC　match　real-time　conditions　to　rectify　the　sensitivity　of　MPC　to　vehicle　speed.　Additionally,　to　further　enhance　the　controller＇s　performance,　a　fuzzy　control　is　employed　to　determine　the　horizon　factor　for　optimizing　the　prediction　horizon　and　control　horizon　online.　Also,　the　weighting　factors　of　the　prediction　horizon　and　control　horizon　are　discussed　to　improve　the　adaptability　at　varying　velocities.　Next,　the　improved　MPC　controller　is　compared　with　the　traditional　MPC　controller　for　a　double　lane　change　maneuver.　The　validation　results　demonstrate　that　the　proposed　strategy　achieves　good　adaptability　with　satisfactory　tracking　accuracy　at　various　velocities.　Finally,　the　feasibility　of　the　proposed　strategy　is　verified　in　a　real　prototype　vehicle　test.