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Learning Behavior Models for Interpreting and Predicting Traffic Situations

Gindele, Tobias

Abstract: In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees.


Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Hochschulschrift
Jahr 2014
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-486189
KITopen ID: 1000048618
Verlag Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik (IFA)
Prüfungsdaten 30.07.2014
Referent/Betreuer Prof. R. Dillmann
Bemerkung zur Veröffentlichung Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 3.0 Deutschland Lizenz.
Schlagworte state estimation and prediction, dynamic Bayesian networks, machine learning, decision trees, autonomous driving
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