A model for vehicle motion on a road network is developed using an enumeration of feasible routes. Combined with a generic stochastic model of distance travelled, a predicted pdf of vehicle position is derived as a mixture. This approach allows prior information on vehicle intent and behaviour to be included via the mixture weights. Illustrative examples are given using a second-order linear-Gaussian model for vehicle road speed. The value of road map data is shown via a tracking example with poor quality measurements and a substantial period prior to sensor activation. The tracking algorithm is implemented using a standard particle filter. In particular, the scheme has potential for revealing the likely paths taken by the vehicle.


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    Title :

    Ground target modelling, tracking and prediction with road networks


    Contributors:


    Publication date :

    2007-07-01


    Size :

    475994 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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