A model-based sensor fault detection algorithm is proposed in this paper to detect and isolate the faulty sensor. Wheel speeds are validated using the wheel speed deviations before being employed to check the sensor measurements of the vehicle dynamics. Kinematic models are employed to estimate yaw rate, lateral acceleration, and steering wheel angle. A Kalman filter based on a point mass model is employed to estimate longitudinal speed and acceleration. The estimated vehicle dynamics and sensor measurements are used to calculate the residuals. Adaptive threshold values are employed to identify the abnormal increments of residuals. Recursive least square method is used to design the coefficients of the expressions for adaptive threshold values, such that the false alarms caused by model uncertainties can be prevented. Different combinations of estimations are employed to obtain 18 residuals. The simulation results show that when a sensor fault occurs, the corresponding residuals are abnormally increased and larger than the adaptive threshold values. The proposed algorithm can successfully detect the sensor fault for the test scenarios.