9,407 research outputs found
Road Friction Estimation for Connected Vehicles using Supervised Machine Learning
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.Comment: Published at IV 201
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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