  Road network graphs provide critical information for autonomous-vehicle
applications, such as drivable areas that can be used for motion planning
algorithms. To find road network graphs, manually annotation is usually
inefficient and labor-intensive. Automatically detecting road network graphs
could alleviate this issue, but existing works still have some limitations. For
example, segmentation-based approaches could not ensure satisfactory topology
correctness, and graph-based approaches could not present precise enough
detection results. To provide a solution to these problems, we propose a novel
approach based on transformer and imitation learning in this paper. In view of
that high-resolution aerial images could be easily accessed all over the world
nowadays, we make use of aerial images in our approach. Taken as input an
aerial image, our approach iteratively generates road network graphs
vertex-by-vertex. Our approach can handle complicated intersection points with
various numbers of incident road segments. We evaluate our approach on a
publicly available dataset. The superiority of our approach is demonstrated
through the comparative experiments. Our work is accompanied with a
demonstration video which is available at
\url{https://tonyxuqaq.github.io/projects/RNGDet/}.
