Deep Semantic Lane Segmentation for Mapless Driving

Resource type
Conference
Author(s)
Meyer, Annika and Salscheider, Niels and F. Orzechowski, Piotr and Stiller, Christoph
Year
2018
Month
10
Book title
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.
Online Sources
www.mrt.kit.edu/z/publ/.../2018/Meyer2018SemanticLanes.pdf
DOI
10.1109/IROS.2018.8594450
Research focus
Safe and Intelligent Vehicles
Project
Tech Center a-drive
Download .bib
Download .bib
Published by
Annika Meyer