Safety systems claim an in-depth study of vehicle motion and tire-ground interaction for the design of a partially automated driving vehicle. This article addresses the quasi-linear parameter-varying Luenberger interconnected fuzzy observer to estimate both longitudinal and lateral vehicle dynamics simultaneously. In a different manner from the commonplace state of the art of vehicle state observers that consider single driving motion, the proposed approach considers the coupled dynamics with the tire-ground interaction to estimate the most important states while reducing the complexity related to the observability and conservatism. This consideration leads to a nonlinear parameter-dependent interconnected model with unmeasured premise variables. Then, the Takagi-Sugeno fuzzy form is considered to deal with the nonlinearities of the vehicle longitudinal and lateral speeds and the slip velocities as well as the steering angle. The concept of “input to state stability” is exploited using fuzzy nonquadratic Lyapunov stability arguments to guarantee the boundness of the estimation errors. A refinement has been proposed through the so-called Polya's theorem aiming to further reduce the conservativeness. Finally, performances and effectiveness of the suggested approach are evaluated through hardware experiments performed with the well-known Sherpa dynamic car simulator under real-world driving situations.