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Nowadays, it is common that road vehicle navigation systems employ maps to represent the vehicle positions in a local reference. The most usual process to do that consists in the estimation of the vehicle positioning by fusing the Global Navigation Satellite System (GNSS) and some other aiding sensors data, and the subsequent projection of these values on the map by applying map-matching techniques. However, it is possible to benefit from map information also during the process of fusing data for positioning. This paper presents an algorithm for lane-level road vehicle navigation that integrates GNSS, dead-reckoning (odometry and gyro), and map data in the fusion process. Additionally, the proposed method brings some benefits for map-matching at lane level because, on the one hand, it allows the tracking of multiple hypothesis and on the other hand, it provides probability values of lane occupancy for each candidate segment. To do this, a new paradigm that describes lanes as piece-wise sets of clothoids was applied in the elaboration of an enhanced map (Emap). Experimental results in real complex scenarios with multiple lanes show the suitability of the proposed algorithm for the problem under consideration, presenting better results than some state-of-the-art methods of the literature.