  Vehicular control systems are required to be both extremely reliable and
robust to different environmental conditions, e.g. load or tire-road friction.
In this paper, we extend a new paradigm for state estimation, called
Twin-in-the-Loop filtering (TiL-F), to the estimation of the unknown parameters
describing the vehicle operating conditions. In such an approach, a
digital-twin of the vehicle (usually already available to the car manufacturer)
is employed on-board as a plant replica within a closed-loop scheme, and the
observer gains are tuned purely from experimental data. The proposed approach
is validated against experimental data, showing to significantly outperform the
state-of-the-art solutions.
