Recursive State Estimation for Lane Detection Using a Fusion of Cooperative and Map Based Data

Modern automated and cooperative driver assistance systems (CoDAS) rely deeply on the position estimation. Regardless of absolute positioning accuracy, the relative position in regard to driving environment and other vehicles needs to be of high quality to enable sophisticated functions. Global Navigation Satellite Systems (GNSS) fulfill this demand only partially. In this paper the authors present an algorithm to accurately infer the driving lane by utilizing Dedicated Short Range Communication (DSRC) and map data alone. The authors evaluate their approach against simulated and real-life data from Europe's largest cooperative vehicle Field Operational Test (FOT): simTD. This lane detection algorithm will be an enabler for CoDAS functions like collaborative driving and merging developed in the TEAM IP project.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2180-2185
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01599814
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:22PM