A method for controlling a vehicle by jointly estimating a state of a vehicle and a state of stiffness of tires of the vehicle, wherein the state of the vehicle includes a velocity and a heading rate of the vehicle, and wherein the state of stiffness includes at least one parameter defining an interaction of at least one tire of the vehicle with a road on which the vehicle is traveling, comprising: retrieving from a memory a motion model of the vehicle and a measurement model of the vehicle, wherein the motion model of the vehicle includes a combination of a deterministic component of the motion and a probabilistic component of the motion, wherein the deterministic component of the motion is independent from the state of stiffness and defines the motion of the vehicle as a function of time, wherein the probabilistic component of the motion includes the state of stiffness having an uncertainty and defines disturbance on the motion of the vehicle, wherein the measurement model of the vehicle includes a combination of a deterministic component of the measurement model independent from the state of stiffness and a probabilistic component of the measurement model that includes the state of stiffness; representing the state of stiffness with a set of particles , each particle includes a mean and a variance of the state of stiffness defining a feasible space of the parameters of the state of stiffness; updating iteratively the mean and the variance of at least some particles using a difference between an estimated state of stiffness estimated using the motion model of the vehicle including the state of stiffness with parameters sampled on the feasible space of the particle and the measured state of stiffness determined according to the measurement model using measurements of the state of the vehicle; and outputting a mean and a variance of the state of stiffness determined as a function of the updated mean and the updated variance in at least one particle, wherein steps of the method are performed using at least one processor operatively connected to the memory. retrieving from a memory a motion model of the vehicle and a measurement model of the vehicle, wherein the motion model of the vehicle includes a combination of a deterministic component of the motion and a probabilistic component of the motion, wherein the deterministic component of the motion is independent from the state of stiffness and defines the motion of the vehicle as a function of time, wherein the probabilistic component of the motion includes the state of stiffness having an uncertainty and defines disturbance on the motion of the vehicle, wherein the measurement model of the vehicle includes a combination of a deterministic component of the measurement model independent from the state of stiffness and a probabilistic component of the measurement model that includes the state of stiffness; representing the state of stiffness with a set of particles , each particle includes a mean and a variance of the state of stiffness defining a feasible space of the parameters of the state of stiffness; updating iteratively the mean and the variance of at least some particles using a difference between an estimated state of stiffness estimated using the motion model of the vehicle including the state of stiffness with parameters sampled on the feasible space of the particle and the measured state of stiffness determined according to the measurement model using measurements of the state of the vehicle; and outputting a mean and a variance of the state of stiffness determined as a function of the updated mean and the updated variance in at least one particle, wherein steps of the method are performed using at least one processor operatively connected to the memory. <EOS>