Corpus ID: 221739095

MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control

@article{Ivanovic2020MATSAI,
  title={MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control},
  author={B. Ivanovic and Amine Elhafsi and G. Rosman and Adrien Gaidon and M. Pavone},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.07517}
}
  • B. Ivanovic, Amine Elhafsi, +2 authors M. Pavone
  • Published 2020
  • Computer Science, Engineering
  • ArXiv
  • Reasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses of behavior prediction in autonomous systems is to inform ego-robot motion planning and control. However, a majority of planning and control algorithms reason about system dynamics rather than the predicted agent tracklets that are commonly output by trajectory forecasting methods, which can hinder their integration. Towards this end, we propose Mixtures of Affine Time… CONTINUE READING
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