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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Navigation with Uncertain Map Data for Automated Vehicles
verfasst von : Christopher Diehl, Niklas Stannartz, Torsten Bertram
Erschienen in: Automatisiertes Fahren 2021
Verlag: Springer Fachmedien Wiesbaden
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Using external map information for automated driving is beneficial, as this follows an extension of the sensor range and global navigation to a priori specified goal. However, common systems use high definition maps, which are expensive to construct and hard to maintain. Therefore, the paper at hand proposes an sensor-independent approach for navigation based on uncertain map data. This work first builds an environment model and plans a global route based on publicly available OpenStreetMap-Data. Afterward, it plans a trajectory considering the uncertainty in the map. Experiments in simulation and on real-world data show the efficiency of the approach.