Trajectory　tracking　and　state　estimation　are　significant　in　the　motion　planning　and　intelligent　vehicle　control.　This　article　focuses　on　the　model　predictive　control　approach　for　the　trajectory　tracking　of　the　intelligent　vehicles　and　state　estimation　of　the　nonlinear　vehicle　system.　The　constraints　of　the　system　states　are　considered　when　applying　the　model　predictive　control　method　to　the　practical　problem,　while　4-degree-of-freedom　vehicle　model　and　unscented　Kalman　filter　are　proposed　to　estimate　the　vehicle　states.　The　estimated　states　of　the　vehicle　are　used　to　provide　model　predictive　control　with　real-time　control　and　judge　vehicle　stability.　Furthermore,　in　order　to　decrease　the　cost　of　solving　the　nonlinear　optimization,　the　linear　time-varying　model　predictive　control　is　used　at　each　time　step.　The　effectiveness　of　the　proposed　vehicle　state　estimation　and　model　predictive　control　method　is　tested　by　driving　simulator.　The　results　of　simulations　and　experiments　show　that　great　and　robust　performance　is　achieved　for　trajectory　tracking　and　state　estimation　in　different　scenarios.