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Journal Articles IEEE Transactions on Intelligent Vehicles Year : 2018

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


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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hal-01713251 , version 1 (20-02-2018)



Franck Li, Philippe Bonnifait, Javier Ibanez-Guzman. Map-Aided Dead-Reckoning With Lane-Level Maps and Integrity Monitoring. IEEE Transactions on Intelligent Vehicles, 2018, 3 (1), pp.81-91. ⟨10.1109/TIV.2018.2792843⟩. ⟨hal-01713251⟩
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