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[SBM18] Predicting Vehicles’ Positions using Roadside Units: a Machine-Learning Approach
Conférence Internationale avec comité de lecture : IEEE Conference on Standards for Communications and Networking (IEEE CSCN'2018), October 2018, pp.a paraitre, Paris, France,Mots clés: VANET, machine learning, positioning
Résumé:
We study positioning systems using Vehicular Ad Hoc
Networks (VANETs) to predict the position of vehicles. We use
the reception power of the packets received by the Road Side
Units (RSUs) and sent by the vehicles on the roads. In fact, the
reception power is strongly influenced by the distance between
a vehicle and a RSU. The machine-learning methodology is
adopted in this paper. In first phase the vehicles know their
positions and the vehicles send their positions in the packets.
The positioning system can thus perform a training sequence and
build a model. The system is then able to handle a prediction
request. In this request, a vehicles without external positioning
will request its position from the neighboring RSUs. The RSUs
which receive this request message from the vehicle will know
the power at which the message was received and will treat the
positioning request using the training set. In this study we use
and compare three widely recognized techniques : K Nearest
Neighbors (KNN), Support Vector Machine (SVM) and Random
Forest. We study these techniques in various configurations and
discuss their respective advantages and drawbacks. Our results
show that these three techniques provide very good results in
terms of position predictions when the error on the transmission
power is small.
BibTeX
| @inproceedings { | |||
| SBM18, | |||
| title | = | "{Predicting Vehicles’ Positions using Roadside Units: a Machine-Learning Approach}", | |
| author | = | " M. Sangare and S. Banerjee and P. Mühlethaler and S. Bouzefrane ", | |
| booktitle | = | "{IEEE Conference on Standards for Communications and Networking (IEEE CSCN'2018)}", | |
| year | = | 2018, | |
| month | = | "October", | |
| pages | = | "a paraitre", | |
| address | = | "Paris, France", | |
| } | |||
