Typically, path planning and control systems for autonomous driving are developed individually. While these systemsmay guarantee commendable individual performance, their integration into the context of autonomous driving may notnecessarily ensure a satisfactory overall performance. To address this issue, this study proposes a model predictivepath control algorithm designed for autonomous ground vehicles (AGVs). In essence, the proposed method seeks todetermine a control input that minimizes a cost function, thereby addressing the requirements of desired waypointtracking, minimal lateral velocity, and control input optimization simultaneously. Furthermore, the contribution oflateral dynamic and kinematic models of the AGV is achieved through the incorporation of by equality constraints,while the consideration of and minimum and maximum control values is enforced as an inequality constraint. Thesolution for the method is obtained using a QP solver in Matlab. Subsequently, the CarMaker vehicle model, coupledwith the Matlab Simulink, is developed to facilitate autonomous navigation simulation. Finally, the proposed methodis utilized for the simulation and the results are analyzed.