Navigation maps provide critical information for advanced driving assistance systems and autonomous vehicles. When these maps are refined to lane-level, ambiguities may occur during the map-matching process, particularly when positioning estimates are inaccurate. This paper presents a dead-reckoning method implementing a particle filter to estimate a set of likely map-matched hypotheses containing the correct solution with a high probability. Our method uses lane-level maps that feature dedicated attributes such as connectedness and adjacency. The vehicle position is essentially estimated by dead-reckoning sensors and lane detection using an intelligent camera. We also describe an integrity monitoring method for assessing the coherence of the set of hypotheses, using the fix of a global navigation satellite system receiver. The method provides in real-time a “Use/Don’t Use” characterization of the vehicle positioning information that is transmitted to safety functions, where integrity is fundamental. The performance of the proposed map-aided dead-reckoning method with integrity monitoring is evaluated using data acquired by an experimental car on suburban public roads. The results obtained validate the approach.


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    Title :

    Map-Aided Dead-Reckoning With Lane-Level Maps and Integrity Monitoring


    Contributors:

    Published in:

    Publication date :

    2018-03-01


    Size :

    1463155 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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