A Novel Approach for Mining Road Information from Low Precision GPS Data

Zhang, Siqie; Li, Changle; Zhou, Xun

doi:10.1109/nana.2017.36

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2018

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(2 citation statements)

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“…[a.kasmi, d.denis] @sherpa-eng.com FirstName.Lastname@uce.fr Discrete Model (DV-RWDM) [9]. These methods depend highly on the number of traces and only an accuracy of 60% was achieved [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Map Matching and Lanes Number Estimation with Openstreetmap

Kasmi¹,

Denis²,

Aufrère³

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Road information, like lanes number, play an important role for intelligent vehicles (IV). Traditionally such road information are obtained through a vision-based measurement or by using a digital detailed map. In this paper, we present a new method for estimating the number of lanes using a low precision GPS receiver and OpenSteetMap (OSM). The method includes the integration of the GPS traces and OSM for a map matching. To this end we developed a probabilistic multicriteria algorithm for map matching that takes into account the accuracy of the GPS data and the attribute information of the road from OSM. Afterward we estimate the number of lanes from OSM. We tested our algorithm on a set of GPS data collected in an urban area near Paris for a total distance of 50km and the overall estimation accuracy reached 83.64%

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Section: Introductionmentioning

confidence: 99%

“…For the number of lanes estimation, in contrast to [9], our method does not require the collection of several GPS traces. In addition, our algorithm was performed on a set of 6596 GPS points collected in an urban area for a total distance of 50km.…”

Section: Introductionmentioning

confidence: 99%