Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning

@article{chahine_intention_2023,
  title = {Intention {Communication} and {Hypothesis} {Likelihood} in {Game}-{Theoretic} {Motion} {Planning}},
  volume = {8},
  issn = {2377-3766, 2377-3774},
  url = {https://ieeexplore.ieee.org/document/10021670/},
  abstract = {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 gametheoretic 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, lanemerging 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.},
  language = {en},
  number = {3},
  urldate = {2023-08-23},
  journal = {IEEE Robotics and Automation Letters},
  author = {Chahine, Makram and Firoozi, Roya and Xiao, Wei and Schwager, Mac and Rus, Daniela},
  month = mar,
  year = {2023},
  pages = {1223--1230},
  month_numeric = {3}
}