  Humans navigate complex environments in an organized yet flexible manner,
adapting to the context and implicit social rules. Understanding these
naturally learned patterns of behavior is essential for applications such as
autonomous vehicles. However, algorithmically defining these implicit rules of
human behavior remains difficult. This work proposes a novel self-supervised
method for training a probabilistic network model to estimate the regions
humans are most likely to drive in as well as a multimodal representation of
the inferred direction of travel at each point. The model is trained on
individual human trajectories conditioned on a representation of the driving
environment. The model is shown to successfully generalize to new road scenes,
demonstrating potential for real-world application as a prior for socially
acceptable driving behavior in challenging or ambiguous scenarios which are
poorly handled by explicit traffic rules.
