Road Friction Estimation for Connected Vehicles using Supervised Machine Learning
This addresses safety and efficiency for connected vehicles by improving road condition forecasting, though it is incremental as it applies existing machine learning methods to this domain.
The paper tackled road friction prediction for connected vehicles by using historical friction and weather data to classify road segments as slippery or non-slippery, finding that neural networks provided more stable results across prediction horizons up to 120 minutes.
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.