Modern driver assistance systems increasingly support the driver to drive safer and more efficient. Besides extracting the needed information about the vehicle environment from sensors, it is also possible to use stored static data from a map. The positioning technology has steadily improved over the years. The quality and the level of service of future positioning solutions will be further improved. As a result, high-precision street maps will also become more relevant in the future for advanced driver assistance systems. In the following a method and algorithms are described that are able to generate such street maps for freeways largely automated. The basis for the map generation is measured street data which is collected by the test vehicle ViewCar. This vehicle has a high-precision coupled navigation. Disturbances, errors and so on are reduced by taking several test sets from the same roadway lane. All measurement data is stored in a spatially enabled database for efficient access. The focus of this paper is the discussion how to process the gathered and pre-processed data. The generated result is a model, which precisely describes the course of the street. Freeways are designed using clothoid models. But because the mathematical handling of clothoids is complex, the approximation of the highway is not trivial. Furthermore for the map generation process uniquely identifiable objects along the street are of interest. If such an object is marked in a map and its relative position is later detected, a vehicle can reconstruct its position more precisely.


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    Title :

    Modeling freeways for digital high precision maps


    Contributors:


    Publication date :

    2008


    Size :

    10 Seiten, 10 Bilder, 17 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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