Learning Behavior Models for Interpreting and Predicting Traffic Situations
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 |
| Schlagworte | state estimation and prediction, dynamic Bayesian networks, machine learning, decision trees, autonomous driving |