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We present a novel algorithm for motion planning in complex, multi-agentscenarios in which occlusions prevent all agents from seeing one another. Inthis setting, the fundamental information that each agent has, i.e., theinformation structure of the interaction, is determined by the preciseconfigurations in which agents come into view of one another. Occlusionsprevent the use of existing pure feedback solutions, which assume availabilityof the state information of all agents at every time step. On the other hand,existing open-loop solutions only assume availability of the initial agentstates. Thus, they do not fully utilize the information available to agentsduring periods of unhampered visibility. Here, we first introduce an algorithmfor solving an occluded, linear-quadratic (LQ) dynamic game, which computesNash equilibrium by using hybrid information and switching between feedback andopen-loop information structures. We then design an efficient iterativealgorithm 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 runningtimes, converging much faster than recent methods employing reachabilityanalysis.

Game-theoretic Occlusion-Aware Motion Planning: an Efficient Hybrid-Information Approach