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 $μ$, the cohesion $c$ and the internal shear angle $φ$. 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.