Negative information provides important additional knowledge that is not exploited for sensor data fusion tasks by default. This paper presents a new approach to incorporate such information about unoccupied, observed areas or missing measurements in the Kalman filtering process. For this purpose, a combination with a grid-based method is proposed to generate a visibility map. This enables a plausibility check and an enhanced understanding for the collaborative perception of the environment with multiple cognitive vehicles. Results from a realistic traffic simulation are presented.


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    Title :

    Data fusion considering 'negative' information for cooperative vehicles


    Additional title:

    Datenzusammenführung unter Berücksichtigung von 'Negativinformationen' für teilnehmende Fahrzeuge


    Contributors:


    Publication date :

    2007


    Size :

    5 Seiten, 3 Bilder, 4 Tabellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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