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AutorMuntzinger, Marc M.dc.contributor.author
Aufnahmedatum2016-03-15T06:23:00Zdc.date.accessioned
In OPARU verfügbar seit2016-03-15T06:23:00Zdc.date.available
Jahr der Erstellung2011dc.date.created
ZusammenfassungFuture driver assistance systems are based on environment perception and situation analysis that must be reliable and fast. Sensor fusion techniques carry with them the problem of measurements from different sensors arriving at the processing unit out-of-sequence, i.e., the original temporal order of measurements is lost. This poses a problem in usual sensor fusion algorithms, which is normally solved via buffering of measurements to ensure the correct temporal ordering. However, buffering causes severe temporal delays that have to be avoided by all means in time-critical applications. Therefore the presented work analyses algorithms to handle out-of-sequence measurements (OOSM) by directly incorporating them into the state estimation without buffering. The performance, temporal gain and costs of different approaches are evaluated. A detailed complexity analysis of the algorithms is presented. The presented work proposes a novel data association technique as well, which incorporates out-of-sequence measurements. The Joint Probabilistic Data Association (JPDA) is extended in order to associate (possibly delayed) measurements with tracks in a cluttered environment. In addition, a novel risk assessment with incorporating covariance propagation into a real-time pre-crash application is presented. A powerful, yet applicable method for using not only state but also covariance information for triggering actuators is proposed. The performance of the different OOSM algorithms is demonstrated using a test vehicle setup with several radar sensors, where an impact sensor is used to evaluate the estimated time to collision (TTC). Furthermore, the results from the OOSM algorithms are evaluated against reference data from a highly accurate laser scanner. All in all, out-of-sequence algorithms are shown to significantly improve the tracking of vehicles especially in time-critical applications.dc.description.abstract
Sprachededc.language.iso
Verbreitende StelleUniversität Ulmdc.publisher
LizenzStandard (Fassung vom 01.10.2008)dc.rights
Link zum Lizenztexthttps://oparu.uni-ulm.de/xmlui/license_v2dc.rights.uri
SchlagwortOOSMdc.subject
SchlagwortOut-of-Sequencedc.subject
DDC-SachgruppeDDC 620 / Engineering & allied operationsdc.subject.ddc
LCSHDriver assistance systemsdc.subject.lcsh
LCSHMultisensor data fusiondc.subject.lcsh
LCSHSensor fusiondc.subject.lcsh
LCSHTracking (Engineering)dc.subject.lcsh
TitelZustandsschätzung mit chronologisch ungeordneten Sensordaten für die Fahrzeugumfelderfassungdc.title
RessourcentypDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-1770dc.identifier.doi
PPN359039391dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-77902dc.identifier.urn
GNDFahrerassistenzsystemdc.subject.gnd
FakultätFakultät für Ingenieurwissenschaften und Informatikuulm.affiliationGeneral
Datum der Freischaltung2011-12-05T15:02:34Zuulm.freischaltungVTS
Peer-Reviewneinuulm.peerReview
Signatur DruckexemplarZ: J-H 14.261; W: W-H 12.713uulm.shelfmark
DCMI MedientypTextuulm.typeDCMI
VTS-ID7790uulm.vtsID
KategoriePublikationenuulm.category
Ulmer SchriftenreiheSchriftenreihe des Instituts für Mess-, Regel- und Mikrotechnikuulm.seriesUlmName


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