  Interconnected road lanes are a central concept for navigating urban roads.
Currently, most autonomous vehicles rely on preconstructed lane maps as
designing an algorithmic model is difficult. However, the generation and
maintenance of such maps is costly and hinders large-scale adoption of
autonomous vehicle technology. This paper presents the first self-supervised
learning method to train a model to infer a spatially grounded lane-level road
network graph based on a dense segmented representation of the road scene
generated from onboard sensors. A formal road lane network model is presented
and proves that any structured road scene can be represented by a directed
acyclic graph of at most depth three while retaining the notion of intersection
regions, and that this is the most compressed representation. The formal model
is implemented by a hybrid neural and search-based model, utilizing a novel
barrier function loss formulation for robust learning from partial labels.
Experiments are conducted for all common road intersection layouts. Results
show that the model can generalize to new road layouts, unlike previous
approaches, demonstrating its potential for real-world application as a
practical learning-based lane-level map generator.
