June 4-6, 2008, Eindhoven University of Technology, Eindhoven, NL
  

IV'08 Paper Abstract

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Paper ThET.10

Noyer, Ulf (German Aerospace Center), Schomerus, Jan (German Aerospace Center), Mosebach, Henning (German Aerospace Center), Gacnik, Jan (DLR-TS), Löper, Christian (German Aerospace Center (DLR)), Lemmer, Karsten (German Aerospace Center)

Generating High Precision Maps for Advanced Guidance Support

Scheduled for presentation during the Poster Session "Poster TPM" (ThET), Thursday, June 5, 2008, 15:10−16:20, Room T1

2008 IEEE Intelligent Vehicles Symposium, June 4-6, 2008, Eindhoven University of Technology, Eindhoven, The Netherlands

This information is tentative and subject to change. Compiled on April 20, 2014

Keywords Automated Vehicles, Telematics, Sensors

Abstract

Modern driver assistance systems increasingly support safer and more fuel-efficient driving.To fulfill these tasks information about the vehicle environment is essential. Besides extracting this information from sensors to perceive the environment, it is also possible to use stored static data. These models allow a reliable and fast access to the environmental data without the typical problems faced with the recognition by sensors, like noise or glitches. Nowadays street maps produced for navigation purposes reach a precision of several meters. To support the driver during manoeuvres however a precision one magnitude better is necessary. Positioning technology has steadily improved over the years and will further improve in the near future. Alongside this development it can be expected, that more precise digital maps will gain more importance. In this paper a method is presented to create these maps mainly automatically. The primary goal for this method is to achieve the best precision possible investing a very small effort. Data is recorded using a test vehicle equipped with a navigation system featuring high precision DGPS and inertial sensors. Lateral deviations are compensated by using a image processing lane-detection sensor. Several measurement iterations are made and merged into a digital map of the street using statistical methods. In order to show the suitability of the generated map for all kinds of ADAS, a test track was surveyed and the digital map was used for automatic guidance of another test vehicle. It could be shown that the map generation algorithm is generally able to produce high precision maps even in challenging environments.

 

 

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