Lane-Level Map Matching Based on HMM

Lane-level map matching is essential for autonomous driving. In this paper, the authors propose a Hidden Markov Model (HMM) for matching a trajectory of noisy global positioning system (GPS) measurements to the road lanes in which the vehicle records its positions. To our knowledge, this is the first time that HMM is used for lane-level map matching. Apart from GPS values, the model is further assisted by yaw rate data (converted to a lane change indicator signal) and visual cues in the form of the left and right lane marking types (dashed, solid, etc.). Having defined expressions for the HMM emission and transition probabilities, the authors evaluate their model to demonstrate that it achieves 95.1% recall and 3.3% median path length error for motorway trajectories.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2020.3035329
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Hansson, Anders
  • Korsberg, Ellen
  • Maghsood, Roza
  • Nordén, Eliza
  • Selpi
• Publication Date: 2021-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 430-439
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 6
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Global Positioning System; Markov chains; Traffic lanes; Trajectory
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01785358
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 22 2021 5:16PM