[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.


@inproceedings {
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)}",
pages="a paraitre",
address="Paris, France",