  Human beings cooperatively navigate rule-constrained environments by adhering
to mutually known navigational patterns, which may be represented as
directional pathways or road lanes. Inferring these navigational patterns from
incompletely observed environments is required for intelligent mobile robots
operating in unmapped locations. However, algorithmically defining these
navigational patterns is nontrivial. This paper presents the first
self-supervised learning (SSL) method for learning to infer navigational
patterns in real-world environments from partial observations only. We explain
how geometric data augmentation, predictive world modeling, and an
information-theoretic regularizer enables our model to predict an unbiased
local directional soft lane probability (DSLP) field in the limit of infinite
data. We demonstrate how to infer global navigational patterns by fitting a
maximum likelihood graph to the DSLP field. Experiments show that our SSL model
outperforms two SOTA supervised lane graph prediction models on the nuScenes
dataset. We propose our SSL method as a scalable and interpretable continual
learning paradigm for navigation by perception. Code is available at
https://github.com/robin-karlsson0/dslp.
