Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866914990420656128 |
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| author | Chahine, Makram Firoozi, Roya Xiao, Wei Schwager, Mac Rus, Daniela |
| author_facet | Chahine, Makram Firoozi, Roya Xiao, Wei Schwager, Mac Rus, Daniela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_12968 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning Chahine, Makram Firoozi, Roya Xiao, Wei Schwager, Mac Rus, Daniela Robotics Computer Science and Game Theory Systems and Control I.2.8; I.2.9; I.2.11 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. |
| title | Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning |
| topic | Robotics Computer Science and Game Theory Systems and Control I.2.8; I.2.9; I.2.11 |
| url | https://arxiv.org/abs/2209.12968 |