  This paper provides a comprehensive comparison of model-based and data-driven
approaches and analyses the benefits of using measured tyre forces for vehicle
sideslip angle estimation. The model-based approaches are based on an extended
Kalman filter and an unscented Kalman filter, in which the measured tyre forces
are utilised in the observation model. An adaptive covariance matrix is
introduced to minimise the tyre model mismatch during evasive manoeuvres. For
data-driven approaches, feed forward and recurrent neural networks are
evaluated. Both approaches use the standard inertial measurement unit and the
tyre force measurements as inputs. Using the large-scale experimental dataset
of 216 manoeuvres, we demonstrate a significant improvement in accuracy using
data-driven vs. model-based approaches. Tyre force measurements improve the
performance of both model-based and data-driven approaches, especially in the
non-linear regime of tyres.
