Enhanced obstacle detection based on Data Fusion for ADAS applications

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dc.contributor.author García, Fernando
dc.contributor.author Escalera Hueso, Arturo de la
dc.contributor.author Armingol Moreno, José María
dc.date.accessioned 2016-09-26T09:49:22Z
dc.date.available 2016-09-26T09:49:22Z
dc.date.issued 2013
dc.identifier.bibliographicCitation Proceedings 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. 1370-1375
dc.identifier.isbn 978-1-4799-2914-6
dc.identifier.uri http://hdl.handle.net/10016/23634
dc.description.abstract Abstract: 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.
dc.description.sponsorship This 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.extent 7
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2013 IEEE
dc.subject.other Vehicles
dc.subject.other Laser modes
dc.subject.other Data integration
dc.subject.other Laser fusion
dc.subject.other Computer vision
dc.subject.other Roads
dc.title Enhanced obstacle detection based on Data Fusion for ADAS applications
dc.type conferenceObject
dc.type bookPart
dc.relation.publisherversion http://dx.doi.org/10.1109/ITSC.2013.6728422
dc.subject.eciencia Robótica e Informática Industrial
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TRA2010-20225-C03-01
dc.relation.projectID Gobierno de España. TRA2011-29454-C03-02
dc.relation.projectID Comunidad de Madrid. TRA2011-29454-C03-02
dc.relation.projectID Gobierno de España. TRA2013-48314-C3-1-R
dc.type.version acceptedVersion
dc.relation.eventdate 06-09 Oct 2013
dc.relation.eventplace The Hague, Netherlands
dc.relation.eventtitle 2013 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 1370
dc.identifier.publicationlastpage 1375
dc.identifier.publicationtitle Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, TheNetherlands, October 6-9, 2013
dc.identifier.uxxi CC/0000021785
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