  Winter conditions, characterized by the presence of ice and snow on the
ground, are more likely to lead to road accidents. This paper presents an
experimental proof of concept, with a 1/5th scale car platform, of a maneuver
selection scheme for low adhesion conditions. In the proposed approach, a
model-based estimator first processes the high-dimensional sensors data of the
IMU, LIDAR and encoders to estimate physically relevant vehicle and ground
conditions parameters such as the inertial velocity of the vehicle $v$, the
friction coefficient $\mu$, the cohesion $c$ and the internal shear angle
$\phi$. Then, a data-driven predictor is trained to predict the optimal
maneuver to perform in the situation characterized by the estimated parameters.
Experimental results show that it is possible to 1) produce a real-time
estimate of the relevant ground parameters, and 2) determine an optimal
maneuver based on the estimated parameters between a limited set of maneuvers.
