publication . Doctoral thesis . Other literature type . 2014

Learning Behavior Models for Interpreting and Predicting Traffic Situations

Gindele, Tobias;
Open Access English
  • Published: 01 Jan 2014
  • Publisher: KIT-Bibliothek, Karlsruhe
  • Country: Germany
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.
Subjects
free text keywords: state estimation and prediction, dynamic Bayesian networks, machine learning, decision trees, autonomous driving, DATA processing & computer science, ddc:004
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Doctoral thesis . 2014
Provider: KITopen
http://dx.doi.org/10.5445/ir/1...
Other literature type . 2014
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publication . Doctoral thesis . Other literature type . 2014

Learning Behavior Models for Interpreting and Predicting Traffic Situations

Gindele, Tobias;