Deriving HD maps for highly automated driving from vehicular probe data Deriving HD maps for highly automated driving from vehicular probe data
Paper summary A high definition (HD) map in the context of highly automated vehicles is a digital map which contains precise information (error < 1m) necessary for localization and for driving behavior: * **road geometry**: Where is the road, where are lanes? * **[street furniture](https://en.wikipedia.org/wiki/Street_furniture)**: street signs, traffic lights * **dynamic data**: end of traffic jam, construction work This kind of information can be stored in the [OpenDRIVE format](http://www.opendrive.org/docs/OpenDRIVEFormatSpecRev1.1D.pdf), which is an XML format with the extension `.xodr`. Creating and maintaining those maps is costly with dedicated cars, so we would like to have an alternative which works automatically. This paper proposes one. **The best summary of the paper is Figure 3.** ## Possible Sensors The following kind of sensors can be equipped in many cars: * stereo cameras * radar * GPS ## Active Players * **Continental** Road Database: Automatic Recording and Processing of Highly Accurate Route Data * [HERE](https://www.here.com/en): map provider which has published a sensor interface specification: “Vehicle Sensor Data Cloud Ingestion Interface Specification (v2.0.2) ## See also * [Inferring road maps from global positioning system traces - Survey and comparative evaluation](https://www.cs.uic.edu/~jakob/papers/biagioni-trr12.pdf) * [Hochgenaue Fahrzeugeigenlokalisierung und kollektives Erlernen hochgenauer digitaler Karten]()
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Deriving HD maps for highly automated driving from vehicular probe data
Massow, Kay and Kwella, B. and Pfeifer, N. and Hausler, Florian and Pontow, Jens and Radusch, Ilja and Hipp, Jochen and Dölitzscher, Frank and Haueis, Martin
IEEE ITSC - 2016 via Local Bibsonomy
Keywords: dblp


Summary by Martin Thoma 4 months ago
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