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Holistic Temporal Situation Interpretation for Traffic Participant Prediction

Kuhnt, Florian

Abstract (englisch):

For a profound understanding of traffic situations including a prediction of traf-
fic participants’ future motion, behaviors and routes it is crucial to incorporate all
available environmental observations. The presence of sensor noise and depen-
dency uncertainties, the variety of available sensor data, the complexity of large
traffic scenes and the large number of different estimation tasks with diverging
requirements require a general method that gives a robust foundation for the de-
velopment of estimation applications.
In this work, a general description language, called Object-Oriented Factor Graph
Modeling Language (OOFGML), is proposed, that unifies formulation of esti-
mation tasks from the application-oriented problem description via the choice
of variable and probability distribution representation through to the inference
method definition in implementation. The different language properties are dis-
cussed theoretically using abstract examples.
The derivation of explicit application examples is shown for the automated driv-
ing domain. A domain-specific ontology is defined which forms the basis for
four exemplary applications covering the broad spectrum of estimation tasks in
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Volltext §
DOI: 10.5445/IR/1000118076
Veröffentlicht am 23.04.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Publikationsdatum 23.04.2020
Sprache Englisch
Identifikator KITopen-ID: 1000118076
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XIV, 253 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdatum 06.02.2020
Schlagwörter automated driving, advanced driver assistance systems, probabilistic modelling, factor graphs
Referent/Betreuer Zöllner, J. M.
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