csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps

TL;DR

csBoundary is a novel system that automatically detects city-scale road boundaries from aerial images, enabling efficient high-definition map annotation for autonomous driving.

Contribution

It introduces a direct graph inference network that predicts continuous road-boundary graphs from aerial images, overcoming pixel-level segmentation limitations.

Findings

Outperforms existing methods on benchmark datasets.

Effectively stitches local graphs into a city-scale boundary map.

Demonstrates high accuracy and efficiency in boundary detection.

Abstract

High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one of the most important information contained 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. The accompanied demonstration video is available at our project page \url{https://sites.google.com/view/csboundary/}.