Abstract
Vehicle motion tracking and prediction over large areas is of significant importance in many industrial applications. This paper presents algorithms for long term vehicle prediction and tracking based on a model of the vehicle that incorporates the properties of the working environment. It uses a limited number of data collection points distributed around the field to update estimates when vehicles are in range of the collection points. The algorithm evaluates the prediction and tracking of vehicle positions using speed and timing profiles built for the particular environment and considering vehicle stopping probability. Positive and negative information from observers is also introduced in the fusion stage. Experimental results from a large scale mining operation using peer to peer communication system are presented to validate the algorithm.
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