  The robust and safe operation of automated vehicles underscores the critical
need for detailed and accurate topological maps. At the heart of this
requirement is the construction of lane graphs, which provide essential
information on lane connectivity, vital for navigating complex urban
environments autonomously. While transformer-based models have been effective
in creating map topologies from vehicle-mounted sensor data, their potential
for generating such graphs from aerial imagery remains untapped. This work
introduces a novel approach to generating successor lane graphs from aerial
imagery, utilizing the advanced capabilities of transformer models. We frame
successor lane graphs as a collection of maximal length paths and predict them
using a Detection Transformer (DETR) architecture. We demonstrate the efficacy
of our method through extensive experiments on the diverse and large-scale
UrbanLaneGraph dataset, illustrating its accuracy in generating successor lane
graphs and highlighting its potential for enhancing autonomous vehicle
navigation in complex environments.
