  We focus on navigation among rational, non-communicating agents at
unsignalized street intersections. Following collision-free motion under such
settings demands nuanced implicit coordination among agents. Often, the
structure of these domains constrains multiagent trajectories to belong to a
finite set of modes. Our key insight is that empowering agents with a model of
these modes can enable effective coordination, realized implicitly via intent
signals encoded in agents' actions. In this paper, we represent modes of joint
behavior in a compact and interpretable fashion using the formalism of
topological braids. We design a decentralized planning algorithm that generates
actions aimed at reducing the uncertainty over the mode of the emerging
multiagent behavior. This mechanism enables agents that individually run our
algorithm to collectively reject unsafe intersection crossings. We validate our
approach in a simulated case study featuring challenging multiagent scenarios
at a four-way unsignalized intersection. Our model is shown to reduce frequency
of collisions by >65% over a set of baselines explicitly reasoning over
trajectories, while maintaining comparable time efficiency.
