Road Friction Estimation for Connected Vehicles using Supervised Machine Learning

TL;DR

This paper develops a machine learning framework to predict road friction levels for connected vehicles using vehicle data and weather information, aiding in road safety and traffic management.

Contribution

It introduces a classification-based approach combining vehicle and weather data, comparing logistic regression, SVM, and neural networks for future friction prediction.

Findings

Neural networks outperform other models in stability across conditions.

Prediction accuracy varies with the forecast horizon, up to 120 minutes.

Incorporating weather data improves friction prediction accuracy.

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.