This paper introduces a model-based fault diagnosis approach that combines Bayesian Networks with an Extended Kalman Filter (EKF) to detect and diagnose faults in a vehicle's lateral dynamic system. Model-based techniques, particularly Bayesian Networks, have gained popularity in engineering applications due to their robustness and ability to reduce the computational cost associated with empirical models. The proposed method leverages the strengths of these techniques by calculating residuals for yaw rate, wheel slip rate, and steering angle, comparing sensor data with data obtained from analytical models. This comparison enables the identification of discrepancies that may indicate faults within the system. The EKF plays a crucial role in estimating the vehicle's speed by fusing data from GPS and accelerometer sensors. This estimation allows the system to detect potential errors in the wheel speed sensors, which are critical for maintaining accurate vehicle dynamics. In the event of an incorrect wheel speed measurement, the system detects the error, and the erroneous data is replaced with the speed value derived from sensor fusion. The proposed fault diagnosis method was implemented in C++ within the Robot Operating System (ROS) framework. To enhance usability and provide real-time error visualization during tests, a Human-Machine Interface (HMI) was developed. Real-time testing of the system has been performed on a test vehicle in a controlled traffic-free area. To highlight the impact of the EKF on system performance test scenarios for the left and right wheel speed sensors were chosen where faults have been injected to the measurements. The results clearly indicate that the designed algorithm can accurately detect and diagnose the faults correctly while ensuring the reliability of the dynamic model, demonstrating its effectiveness and potential for real-world automotive applications.