Lane-level map-matching with integrity on high-definition maps

Navigation maps provide important information for Advanced Driving Assistance Systems (ADAS) and Autonomous Vehicles. This paper presents a method estimating a set of likely map-matched hypotheses containing the correct solution with a high probability. This addresses the problems encountered when using a high definition map when a large number of ambiguities arise. These occur for instance, when only inaccurate prior information on position is available at initialization. The method uses lane-level accurate maps with dedicated attributes, such as connectedness and adjacency, and an automotive Global Navigation Satellite System (GNSS) receiver assisted with dead-reckoning (DR) sensors. GNSS can be so inaccurate that map-matching relies mainly on DR estimates, the GNSS fixes being used as uncertain estimates with protection levels. This paper proposes a formalization of the map-matching integrity problem as well as a sequential method using a Particle Filter providing a reliable set of map-matched hypotheses. The performance is evaluated using data acquired in public roads.