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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
Referent/Betreuer Prof. J. M. Zöllner
Schlagwörter automated driving, advanced driver assistance systems, probabilistic modelling, factor graphs
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