Abstract
Smart tires are systems that are able to measure temperature, inflation pressure, footprint dimensions, and, importantly, tire contact forces. The integration of this additional information with the signals ob-tained from more conventional vehicle sensors, e.g., inertial measure-ment units, can enhance state estimation in production cars. This paper evaluates the use of smart tires to improve the estimation performance of an Unscented Kalman filter (UKF) based on a nonlinear vehicle dynam-ics model. Two UKF implementations, excluding and including smart tire information, are compared in terms of estimation accuracy of vehicle speed, sideslip angle and tire-road friction coefficient, using experi-mental data obtained on a high performance passenger car.