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• W4317496719 abstract "Game-theoretic motion planners are a potent solution for controlling systems of multiple highly interactive robots. Most existing game-theoretic planners unrealistically assume <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>a priori</i> 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 which incorporates their intentions. A discrete Bayesian filter is designed to infer the objectives in real-time based on the discrepancy between observed trajectories and predicted solutions to non-cooperative games under available hypotheses. 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." @default.
• W4317496719 created "2023-01-20" @default.
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• W4317496719 date "2023-03-01" @default.
• W4317496719 modified "2023-10-16" @default.
• W4317496719 title "Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning" @default.
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• W4317496719 doi "https://doi.org/10.1109/lra.2023.3238131" @default.
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