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dc.contributor.author Shan, Mao
dc.contributor.author Worrall, Stewart
dc.contributor.author Masson, Favio Roman
dc.contributor.author Nebot, Eduardo
dc.date.available 2017-01-23T21:22:04Z
dc.date.issued 2014-06
dc.identifier.citation Shan, Mao ; Worrall, Stewart ; Masson, Favio Roman; Nebot, Eduardo ; Using Delayed Observations for Long-Term Vehicle Tracking in Large Environments; Institute of Electrical and Electronics Engineers; Ieee Transactions On Intelligent Transportation Systems; 15; 3; 6-2014; 967-981
dc.identifier.issn 1524-9050
dc.identifier.issn 1558-0016
dc.identifier.uri http://hdl.handle.net/11336/11751
dc.description.abstract The tracking of vehicles over large areas with limited position observations is of significant importance in many industrial applications. This paper presents algorithms for long-term vehicle motion estimation based on a vehicle motion model that incorporates the properties of the working environment, and information collected by other mobile agents and fixed infrastructure collection points. The prediction algorithm provides long-term estimates of vehicle positions using speed and timing profiles built for a particular environment, and considering the probability of a vehicle stopping. A limited number of data collection points distributed around the field are used to update the estimates, with negative information (no communication) also used to improve the prediction. The paper introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates to be relayed among vehicles, and finally conveyed to the collection point for an improved position estimate. Positive and negative communication information is incorporated into the fusion stage, and a particle filter is used to incorporate the delayed observations harvested from vehicles in the field to improve the position estimates. The contributions of this work enable the optimization of fleet scheduling using discrete observations. Experimental results from a typical large scale mining operation are presented to validate the algorithms.
dc.format application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject DELAYED OBSERVATIONS
dc.subject INTERVEHICLE COMMUNICATION
dc.subject LONG-TERM MOTION PREDICTION
dc.subject PARTICLE FILTERING
dc.subject VEHICLE TRACKING
dc.subject.classification Otras Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification INGENIERÍAS Y TECNOLOGÍAS
dc.title Using Delayed Observations for Long-Term Vehicle Tracking in Large Environments
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2017-01-19T19:54:24Z
dc.journal.volume 15
dc.journal.number 3
dc.journal.pagination 967-981
dc.journal.pais Estados Unidos
dc.journal.ciudad Nueva York
dc.description.fil Fil: Shan, Mao . University Of Sidney; Australia
dc.description.fil Fil: Worrall, Stewart . University Of Sidney; Australia
dc.description.fil Fil: Masson, Favio Roman. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación En Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur; Argentina
dc.description.fil Fil: Nebot, Eduardo . University Of Sidney; Australia
dc.journal.title Ieee Transactions On Intelligent Transportation Systems
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://ieeexplore.ieee.org/document/6701131/
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1109/TITS.2013.2292934


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info:eu-repo/semantics/restrictedAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)