Self-Localization Based on Visual Lane Marking Maps: An Accurate Low-Cost Approach for Autonomous Driving

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.

• Record URL:
  https://doi.org/10.1109/TITS.2017.2752461
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Vivacqua, Rafael Peixoto Derenzi
  • Bertozzi, Massimo
  • Cerri, Pietro
  • Martins, Felipe Nascimento
  • Vassallo, Raquel Frizera
• Publication Date: 2018-2

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 582-597
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicle guidance; Computer vision; Detection and identification systems; Digital maps; Global Positioning System; Lane lines; System architecture
• Uncontrolled Terms: Localization
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01663803
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Mar 22 2018 12:01PM