  Game-theoretic motion planners are a potent solution for controlling systems
of multiple highly interactive robots. Most existing game-theoretic planners
unrealistically assume a priori objective function knowledge is available to
all agents. To address this, we propose a fault-tolerant receding horizon
game-theoretic motion planner that leverages inter-agent communication with
intention hypothesis likelihood. Specifically, robots communicate their
objective function incorporating their intentions. A discrete Bayesian filter
is designed to infer the objectives in real-time based on the discrepancy
between observed trajectories and the ones from communicated intentions. In
simulation, we consider three safety-critical autonomous driving scenarios of
overtaking, lane-merging and intersection crossing, to demonstrate our
planner's ability to capitalize on alternative intention hypotheses to generate
safe trajectories in the presence of faulty transmissions in the communication
network.
