Enhancing Vehicle Positioning Data Through Map-Matching

  • Mohammed A. Quddus
  • Nagendra R. Velaga


In-vehicle navigation systems usually rely on the integration of data from a range of positioning sensors/systems such as GPS or GPS integrated with other positioning sensors. Even with very robust sensor calibration and sensor fusion methods, positioning inaccuracies are sometimes unavoidable. In addition, there are inaccuracies with a digital road map due to errors in map creation, projection and digitization. As a result of such imprecision in the positioning systems and the faulty digital base map, actual vehicle positions do not always match with the spatial road map although the vehicle is known to be restricted on the road network. This phenomenon is referred to as spatial mismatch. The spatial mismatch is often more severe at junctions, roundabouts, complicated flyovers and built-up urban areas with complex route structures. However, an intelligent algorithm can be formulated by taking into account the historical trajectory of the vehicle and topological information of the road network (e.g., connectivity and orientation of links) to precisely identify the correct link on which a vehicle is traveling. Furthermore, an estimation of the vehicle location on the link can also be determined by taking into account all error sources associated with the positioning systems and digital map database. This is known as a map-matching algorithm. This chapter discusses the considerable momentum in research and development activities in map-matching, especially map data quality, methods and reliability issues surrounding map-matching algorithms. Future developments of map-matching algorithms and how such algorithms can tackle the positioning and navigation requirements of autonomous navigation are also discussed.


Road Segment Intelligent Vehicle Multipath Error Candidate Link Integrity Monitoring 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  • Mohammed A. Quddus
    • 1
  • Nagendra R. Velaga
    • 2
  1. 1.Department of Civil and Building EngineeringLoughborough UniversityLeicestershireUK
  2. 2.ITS, dot.rural Digital Economy Research HubUniversity of AberdeenAberdeenUK

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