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Road network graphs provide critical information for autonomous-vehicleapplications, such as drivable areas that can be used for motion planningalgorithms. To find road network graphs, manually annotation is usuallyinefficient and labor-intensive. Automatically detecting road network graphscould alleviate this issue, but existing works still have some limitations. Forexample, segmentation-based approaches could not ensure satisfactory topologycorrectness, and graph-based approaches could not present precise enoughdetection results. To provide a solution to these problems, we propose a novelapproach based on transformer and imitation learning in this paper. In view ofthat high-resolution aerial images could be easily accessed all over the worldnowadays, we make use of aerial images in our approach. Taken as input anaerial image, our approach iteratively generates road network graphsvertex-by-vertex. Our approach can handle complicated intersection points withvarious numbers of incident road segments. We evaluate our approach on apublicly available dataset. The superiority of our approach is demonstratedthrough the comparative experiments. Our work is accompanied with ademonstration video which is available at\url{https://tonyxuqaq.github.io/projects/RNGDet/}.

RNGDet: Road Network Graph Detection by Transformer in Aerial Images