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URN: urn:nbn:de:swb:90-449554

On Compositional Hierarchical Models for holistic Lane and Road Perception in Intelligent Vehicles

Töpfer, Daniel

This work is a contribution to the vision based perception of multi lane roads of urban intersections. Given multiple input features the proposed probabilistic hierarchical model infers the lane structure as well as the location of stoplines and the turn directions of individual lanes. Thereby, it expresses prior expectations on the road topology using weak probabilistic constraints which allows for the detection of parallel lanes as well as splitting and merging lanes.

Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Publikationstyp Hochschulschrift
Jahr 2014
Sprache Englisch
Identifikator KITopen ID: 1000044955
Verlag Karlsruhe
Abschlussart Dissertation
Fakultät Fakultät für Maschinenbau (MACH)
Institut Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Prüfungsdaten 18.11.2014
Referent/Betreuer Prof. C. Stiller
Schlagworte Multi-lane road perception, lane detection, hierarchical models, nonparametric belief propagation
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