Deep Semantic Lane Segmentation for Mapless Driving

Publikationstyp
Konferenz
Autor(en)
Meyer, Annika and Salscheider, Niels and F. Orzechowski, Piotr and Stiller, Christoph
Jahr
2018
Monat
10
Buchtitel
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Abstract
In autonomous driving systems a strong relation to highly accurate maps is taken to be inevitable, although street scenes change frequently. However, a preferable system would be to equip the automated cars with a sensor system that is able to navigate urban scenarios without an accurate map. We present a novel pipeline using a deep neural network to detect lane semantics and topology given RGB images. On the basis of this classification, the information about the road scene can be extracted just from the sensor setup supporting mapless autonomous driving. In addition to superseding the huge effort of creating and maintaining highly accurate maps, our system reduces the need for precise localization. Using an extended Cityscapes dataset, we show accurate ego lane detection including lane semantics on challenging scenarios for autonomous driving.
Link
www.mrt.kit.edu/z/publ/.../2018/Meyer2018SemanticLanes.pdf
DOI
10.1109/IROS.2018.8594450
Forschungsfelder
Sichere und intelligente Fahrzeuge
Projekt
Tech Center a-drive
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Eingetragen von
Annika Meyer