In the past decade, autonomous driving has experienced rapid development in both
academia and industry. However, its limited interpretability remains a significant unsolved …

Vectorized high-definition (HD) maps contain detailed information about surrounding road
elements, which are crucial for various downstream tasks in modern autonomous driving …

Road extraction from high-resolution remote sensing images has long been a focal and
challenging research topic in the field of computer vision. Accurate extraction of road …

This paper presents a novel approach to computing vector road maps from satellite remotely
sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation …

Topological road-boundary detection using remote sensing imagery plays a critical role in
creating high-definition (HD) maps and enabling autonomous driving. Previous approaches …

Abstract Knowledge of lane topology is a core problem in autonomous driving. Aerial
imagery can provide high resolution quickly updatable lane source data but detecting lanes …

With the fast development of autonomous driving technologies, there is an increasing
demand for high-definition (HD) maps, which provide reliable and robust prior information …

Road occlusion seriously affects the connectivity of extracted roads, and has a negative
effect on practical applications. The dense road occlusion problem is caused by high-rise …

With the advancements of deep learning methodologies, there have been significant strides
in automating vector extraction. However, existing methods are often tailored to specific …

Direct image-to-graph transformation is a challenging task that solves object detection and
relationship prediction in a single model. Due to the complexity of this task, large training …