To face the nowadays challenges of mobility, the study of vehicle dynamics is of paramount importance since it provides insight on the quantities characterizing vehicle motion, called states. States may be measured precisely and reliably by employing the proper equipment but this is often expensive and very challenging. In order to overcome this obstacle, virtual sensing is used: this technique combines a reduced set of measured data with process models to obtain knowledge on other non-measured quantities. An estimator was built to estimate forces and sideslip angle by using a tire model coupled with an Extended Kalman Filter: it is able to reach a promisingly good performance even in low lateral acceleration scenarios.