TY - JOUR
T1 - csBoundary
T2 - City-Scale Road-Boundary Detection in Aerial Images for High-Definition Maps
AU - Xu, Zhenhua
AU - Liu, Yuxuan
AU - Gan, Lu
AU - Hu, Xiangcheng
AU - Sun, Yuxiang
AU - Liu, Ming
AU - Wang, Lujia
PY - 2022/4
Y1 - 2022/4
N2 - High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one important information presented in HD maps since it distinguishes between road areas and off-road areas, which can guide vehicles to drive within road areas. But it is labor-intensive to annotate road boundaries for HD maps at the city scale. To enable automatic HD map annotation, current work uses semantic segmentation or iterative graph growing for road-boundary detection. However, the former could not ensure topological correctness since it works at the pixel level, while the latter suffers from inefficiency and drifting issues. To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation. Our network takes as input an aerial image patch, and directly infers the continuous road-boundary graph (i.e., vertices and edges) from this image. To generate the city-scale road-boundary graph, we stitch the obtained graphs from all the image patches. Our csBoundary is evaluated and compared on a public benchmark dataset. The results demonstrate our superiority. © 2022 IEEE.
AB - High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one important information presented in HD maps since it distinguishes between road areas and off-road areas, which can guide vehicles to drive within road areas. But it is labor-intensive to annotate road boundaries for HD maps at the city scale. To enable automatic HD map annotation, current work uses semantic segmentation or iterative graph growing for road-boundary detection. However, the former could not ensure topological correctness since it works at the pixel level, while the latter suffers from inefficiency and drifting issues. To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation. Our network takes as input an aerial image patch, and directly infers the continuous road-boundary graph (i.e., vertices and edges) from this image. To generate the city-scale road-boundary graph, we stitch the obtained graphs from all the image patches. Our csBoundary is evaluated and compared on a public benchmark dataset. The results demonstrate our superiority. © 2022 IEEE.
KW - Autonomous driving
KW - city-scale road-boundary detection
KW - HD map
KW - self-attention
UR - http://www.scopus.com/inward/record.url?scp=85125737983&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85125737983&origin=recordpage
U2 - 10.1109/LRA.2022.3154052
DO - 10.1109/LRA.2022.3154052
M3 - RGC 21 - Publication in refereed journal
SN - 2377-3766
VL - 7
SP - 5063
EP - 5070
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 2
ER -
