We present a novel algorithm for motion planning in complex, multi-agent
scenarios in which occlusions prevent all agents from seeing one another. In
this setting, the fundamental information that each agent has, i.e., the
information structure of the interaction, is determined by the precise
configurations in which agents come into view of one another. Occlusions
prevent the use of existing pure feedback solutions, which assume availability
of the state information of all agents at every time step. On the other hand,
existing open-loop solutions only assume availability of the initial agent
states. Thus, they do not fully utilize the information available to agents
during periods of unhampered visibility. Here, we first introduce an algorithm
for solving an occluded, linear-quadratic (LQ) dynamic game, which computes
Nash equilibrium by using hybrid information and switching between feedback and
open-loop information structures. We then design an efficient iterative
algorithm for decision-making which exploits this hybrid information structure.
Our method is demonstrated in overtaking and intersection traffic scenarios.
Results confirm that our method outputs trajectories with favorable running
times, converging much faster than recent methods employing reachability
analysis