Self-localization based on visual lane marking maps: an accurate low-cost approach for autonomous driving

Rafael Peixoto Derenzi Vivacqua, Massimo Bertozzi, Pietro Cerri, Felipe Nascimento Martins, Raquel Frizera Vassallo

Research output: Contribution to journalArticleAcademicpeer-review


Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best localization systems based on GNSS cannot always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Recent works have shown the advantage of using maps as a precise, robust, and reliable way of localization. Typical approaches use the set of current readings from the vehicle sensors to estimate its position on the map. The approach presented in this paper exploits a short-range visual lane marking detector and a dead reckoning system to construct a registry of the detected back lane markings corresponding to the last 240 m driven. This information is used to search in the map the most similar section, to determine the vehicle localization in the map reference. Additional filtering is used to obtain a more robust estimation for the localization. The accuracy obtained is sufficiently high to allow autonomous driving in a narrow road. The system uses a low-cost architecture of sensors and the algorithm is light enough to run on low-power embedded architecture.
Original languageEnglish
Article number2
Pages (from-to)582-597
Number of pages16
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number2
Publication statusPublished - 1 Feb 2018


  • autonomous driving
  • computer vision
  • dead reckoning
  • lane marking detectors
  • map matching
  • mapping and localization


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