Vehicle running track reconstruction method based on multiple probability matching under sparse sampling
A sparse sampling and probability matching technology, applied in directions such as road network navigators, can solve the problems of unsatisfactory vehicle driving trajectory reconstruction, affecting the vehicle driving trajectory reconstruction accuracy, and difficulty in ensuring the accuracy of sampling points, avoiding positioning The effect of repeated switching, strong road network applicability, and low data requirements
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[0026] The present invention will be further described below in conjunction with the drawings, but the embodiments of the present invention are not limited to this.
[0027] Sampling generally refers to the process of converting continuous quantities in the time domain or spatial domain into discrete quantities. Sampling in the present invention refers to a process of sampling survey data as a sample amount in time domain or space domain at intervals. Different sampling rates correspond to different sparseness of the sampling results. Through sampling processing, the number of sampling points and the amount of reconstruction operations can be reduced while ensuring that the data is within the fidelity range. The present invention combines multiple probability matching models to reconstruct the vehicle trajectory under different sampling rates. The invention can not only deal with the path reconstruction problem of general data volume, but also achieve better trajectory reconstruc...
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