Kalman filters are used for motion state estimation of an autonomous vehicle-trailer system, which can be utilized directly to motion control and autonomous navigation. The autonomous vehicle-trailer system consists of an autonomous vehicle and a passive trailer coupled to the vehicle by a trailer hitch. The vehicle-trailer system is equipped with the global positioning system (GPS), encoder-based odometry, and hitch angle sensors. A Simulink model is first developed for the system kinematics. The vehicle states are then estimated using extended Kalman filter (EKF) and unscented Kalman filter (UKF). Simulation results are compared and discussed based on the root mean square error (RMSE) and the simulation time. The results indicate that both EKF and UKF algorithms have very close RMSE for the position x and y, whereas the processing time is increased by 17.7% for the UKF.