> Details

Méneroux, Yann [Author]; Kanasugi, Hiroshi [Author]; Saint Pierre, Guillaume [Author]; Le Guilcher, Arnaud [Author]; Mustière, Sébastien [Author]; Shibasaki, Ryosuke [Author]; Kato, Yugo [Author] ; Yann Méneroux and Hiroshi Kanasugi and Guillaume Saint Pierre and Arnaud Le Guilcher and Sébastien Mustière and Ryosuke Shibasaki and Yugo Kato [Contributor]

Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest

Reference
management

Bookmarks

Remove from
bookmarks

Share this on Whatsapp

Close

Bookmarks



You can manage bookmarks using lists, please log in to your user account for this.
  • Media type: Text; E-Article; Electronic Conference Proceeding
  • Title: Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest
  • Contributor: Méneroux, Yann [Author]; Kanasugi, Hiroshi [Author]; Saint Pierre, Guillaume [Author]; Le Guilcher, Arnaud [Author]; Mustière, Sébastien [Author]; Shibasaki, Ryosuke [Author]; Kato, Yugo [Author]
  • imprint: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2018
  • Language: English
  • DOI: https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.11
  • Keywords: Map Inference ; GPS Traces ; Traffic Signal ; Machine Learning
  • Origination:
  • Footnote: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Description: As Floating Car Data are becoming increasingly available, in recent years many research works focused on leveraging them to infer road map geometry, topology and attributes. In this paper, we present an algorithm, relying on supervised learning to detect and localize traffic signals based on the spatial distribution of vehicle stop points. Our main contribution is to provide a single framework to address both problems. The proposed method has been experimented with a one-month dataset of real-world GPS traces, collected on the road network of Mitaka (Japan). The results show that this method provides accurate results in terms of localization and performs advantageously compared to the OpenStreetMap database in exhaustivity. Among many potential applications, the output predictions may be used as a prior map and/or combined with other sources of data to guide autonomous vehicles.
  • Access State: Open Access