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Conference Papers Year : 2017

Using High Definition Maps to Estimate GNSS Positioning Uncertainty


Map-matching can be used to estimate the Horizontal Uncertainty Level (HUL) of GNSS position fixes. Integrity monitoring is indeed an important issue for autonomous vehicles navigation. The method is based on the use of a high definition map that stores accurate information about the road network. This additional source of information is crucial for autonomous navigation. The matched position is computed using proprioceptive sensors from the car and GNSS fixes that are handled using a precautionary principle with Horizontal Protection Levels (HPL). A Particle Filter is used for its ability to manage multiple hypotheses if needed. Estimating different likely map-matched hypotheses allows to determine the level of uncertainty of the GNSS which is defined as the maximum distance between a map-matched hypothesis and a given GNSS position. This distance can be seen as a Map-Aided Horizontal Uncertainty Level (MA-HUL), providing a confidence indicator to the vehicle for integrity monitoring. This paper presents the multi-hypotheses map-matching algorithm and a method to compute the MA-HUL values in real-time. Experimental results carried out in open road conditions support the evaluation and show that this metric provides reliable confidence information.
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hal-01569092 , version 1 (26-07-2017)


  • HAL Id : hal-01569092 , version 1


Franck Li, Philippe Bonnifait, Javier Ibañez-Guzmán. Using High Definition Maps to Estimate GNSS Positioning Uncertainty. European Navigation Conference (ENC 2017), May 2017, Lausanne, Switzerland. ⟨hal-01569092⟩
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