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Particle filter (PF) algorithm was used in vehicle states estimation. A vehicle dynamics system containing constant noise and nonlinear tire model was established. First, the particles were predicted through nonlinear state transition function; then the weights of the predicted particles were evaluated based on current measurements. Finally, the key states were estimated though resample step. The PF estimator was compared with other estimators based on extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of virtual experiment based on ADAMS/Car and real vehicle experiment demonstrated that PF was available in vehicle states estimation.