论文标题
在低粘附条件下车辆的机动选择算法的实验研究
Experimental investigation of a maneuver selection algorithm for vehicles in low adhesion conditions
论文作者
论文摘要
冬季条件以地面上存在冰和雪为特征，更有可能导致道路事故。本文提供了一个实验性的概念证明，该概念具有1/5刻度的汽车平台，该概念是针对低粘附条件的机动选择方案。在拟议的方法中，基于模型的估计器首先处理IMU，LIDAR和编码器的高维传感器数据，以估计与物理相关的车辆和地面条件参数，例如车辆$ V $的惯性速度，摩擦系数$ $ $，胶合$ C $ C $和内部剪切角$ $ c。然后，对数据驱动的预测变量进行了训练，以预测以估计参数为特征的情况下执行的最佳操作。实验结果表明，可能是1）对相关地面参数的实时估计，以及2）根据有限的操作组之间的估计参数确定最佳操作。
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.
