Handling, stability, and the inclination to roll over are all significantly affected by vehicle factors. This research presents two techniques for real-time estimation of a ground vehicle's inertia values. The uncertain vehicle model provides a probability density function for each of the variables by using the Generalized Polynomial Chaos (gPC) method of prop agating the uncertainties. Many different statistical techniques may be used to these PDFs in order to estimate the parameters' values. Maximum A-Posteriori (MAP) estimation is utilized here. Where is the vector of PDFs of the parameters and z is the observable sensor comparison, the MAP estimate optimizes the distribution of P(|z)? One more approach is to use an adaptive filtering technique. An instance of an adaptive filter is the Kalman Filter. By combining it with the gPC theory, the PDFs of the parameter distributions may be updated at each time step. The filter adjusts the median values of these PDFs so that they are more closely in line with the true values.