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Enhanced obstacle detection based on Data Fusion for ADAS applications

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dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.contributor.authorGarcía, Fernando
dc.contributor.authorEscalera Hueso, Arturo de la
dc.contributor.authorArmingol Moreno, José María
dc.date.accessioned2016-09-26T09:49:22Z
dc.date.available2016-09-26T09:49:22Z
dc.date.issued2013
dc.description.abstractAbstract: Fusion is a common topic nowadays in Advanced Driver Assistance Systems (ADAS). The demanding requirements of safety applications require trustable sensing technologies. Fusion allows to provide trustable detections by combining different sensor devices, fulfilling the requirements of safety applications. High level fusion scheme is presented; able to improve classic ADAS systems by combining different sensing technologies i.e. laser scanner and computer vision. By means of powerful Data Fusion (DF) algorithms, the performance of classic ADAS detection systems is enhanced. Fusion is performed in a decentralized scheme (high level), allowing scalability; hence new sensing technologies can easily be added to increase the trustability and the accuracy of the overall system. Present work focus in the Data Fusion scheme used to combine the information of the sensors at high level. Although for completeness some details of the different detection algorithms (low level) of the different sensors is provided. The proposed work adapts a powerful Data Association technique for Multiple Targets Tracking (MTT): Joint Probabilistic Data Association (JPDA) to improve the trustability of the ADAS detection systems. The final application provides real time detection of road users (pedestrians and vehicles) in real road situations. The tests performed proved the improvement of the use of Data Fusion algorithms. Furthermore, comparison with other classic algorithms such as Global Nearest Neighbors (GNN) proved the performance of the overall architecture.en
dc.description.sponsorshipThis work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02). CAM through SEGAUTO-II (S2009/DPI-1509) .
dc.format.extent7
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationProceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, TheNetherlands, October 6-9, 2013. : Ieee - The Institute Of Electrical And Electronics Engineers, Inc. Pp. 1370-1375en
dc.identifier.isbn978-1-4799-2914-6
dc.identifier.publicationfirstpage1370
dc.identifier.publicationlastpage1375
dc.identifier.publicationtitleProceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, TheNetherlands, October 6-9, 2013en
dc.identifier.urihttps://hdl.handle.net/10016/23634
dc.identifier.uxxiCC/0000021785
dc.language.isoeng
dc.publisherIEEE
dc.relation.eventdate06-09 Oct 2013
dc.relation.eventplaceThe Hague, Netherlandsen
dc.relation.eventtitle2013 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013)en
dc.relation.projectIDGobierno de España. TRA2010-20225-C03-01es
dc.relation.projectIDGobierno de España. TRA2011-29454-C03-02es
dc.relation.projectIDComunidad de Madrid. TRA2011-29454-C03-02es
dc.relation.projectIDGobierno de España. TRA2013-48314-C3-1-Res
dc.relation.publisherversionhttp://dx.doi.org/10.1109/ITSC.2013.6728422
dc.rights© 2013 IEEE
dc.rights.accessRightsopen access
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherVehiclesen
dc.subject.otherLaser modesen
dc.subject.otherData integrationen
dc.subject.otherLaser fusionen
dc.subject.otherComputer visionen
dc.subject.otherRoadsen
dc.titleEnhanced obstacle detection based on Data Fusion for ADAS applicationsen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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