  State-of-the-art vehicle dynamics estimation techniques usually share one
common drawback: each variable to estimate is computed with an independent,
simplified filtering module. These modules run in parallel and need to be
calibrated separately. To solve this issue, a unified Twin-in-the-Loop (TiL)
Observer architecture has recently been proposed: the classical simplified
control-oriented vehicle model in the estimators is replaced by a full-fledged
vehicle simulator, or digital twin (DT). The states of the DT are corrected in
real time with a linear time invariant output error law. Since the simulator is
a black-box, no explicit analytical formulation is available, hence classical
filter tuning techniques cannot be used. Due to this reason, Bayesian
Optimization will be used to solve a data-driven optimization problem to tune
the filter. Due to the complexity of the DT, the optimization problem is
high-dimensional. This paper aims to find a procedure to tune the
high-complexity observer by lowering its dimensionality. In particular, in this
work we will analyze both a supervised and an unsupervised learning approach.
The strategies have been validated for speed and yaw-rate estimation on
real-world data.
