Road friction estimation for connected vehicles using supervised machine learning

G. Panahandeh, Erik Ek, N. Mohammadiha
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引用次数: 24

Abstract

In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. The problem is formulated as a classification task where the available data is used to train three machine learning models including logistic regression, support vector machine, and neural networks to predict the friction class (slippery or non-slippery) in the future for specific road segments. In addition to the friction values, which are measured by moving vehicles, additional parameters such as humidity, temperature, and rainfall are used to obtain a set of descriptive feature vectors as input to the classification methods. The proposed prediction models are evaluated for different prediction horizons (0 to 120 minutes in the future) where the evaluation shows that the neural networks method leads to more stable results in different conditions.
基于监督机器学习的网联车辆道路摩擦估计
本文研究了基于网联车辆的道路摩擦预测问题。提出了一种利用互联汽车历史摩擦数据和气象站数据预测道路摩擦水平的框架,并给出了不同方法的比较结果。这个问题被制定为一个分类任务,其中可用的数据被用来训练三种机器学习模型,包括逻辑回归、支持向量机和神经网络,以预测未来特定路段的摩擦等级(湿滑或不湿滑)。除了通过移动车辆测量的摩擦值外,还使用其他参数(如湿度、温度和降雨量)来获得一组描述性特征向量,作为分类方法的输入。在不同的预测时段(未来0 ~ 120分钟)对所提出的预测模型进行了评价,结果表明,在不同的条件下,神经网络方法的预测结果更加稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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