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.

翻译：冬季路面冰雪覆盖的特征更容易导致交通事故。本文通过一个1/5比例缩放的车辆平台，对低附着条件下的机动选择方案进行了实验性概念验证。在所提出的方法中，基于模型的估计器首先处理IMU、激光雷达和编码器的高维传感器数据，以估计具有物理意义的车辆与路面状态参数，例如车辆惯性速度$v$、摩擦系数$\mu$、黏聚力$c$以及内剪切角$\phi$。随后，训练一个数据驱动的预测器，用于在由估计参数表征的路况下预测应执行的最优机动动作。实验结果表明，该方法能够：1）实时估计关键的路面参数；2）在有限的机动动作集合中，根据估计参数确定最优机动策略。