@Inproceedings{IvanovicPavone2019,
  author       = {Ivanovic, B. and Pavone, M.},
  title        = {The {Trajectron}: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs},
  booktitle    = {{IEEE/CVF International Conference on Computer Vision (ICCV)}},
  year         = {2019},
  note         = {In Press},
  abstract = {Developing safe human-robot interaction systems is a necessary step towards the widespread integration of autonomous agents in society. A key component of such systems is the ability to reason about the many potential futures (e.g. trajectories) of other agents in the scene. Towards this end, we present the Trajectron, a graph-structured model that predicts many potential future trajectories of multiple agents simultaneously in both highly dynamic and multimodal scenarios (i.e. where the number of agents in the scene is time-varying and there are many possible highly-distinct futures for each agent). It combines tools from recurrent sequence modeling and variational deep generative modeling to produce a distribution of future trajectories for each agent in a scene. We demonstrate the performance of our model on several datasets, obtaining state-of-the-art results on standard trajectory prediction metrics as well as introducing a new metric for comparing models that output distributions.},
  address  = {Seoul, South Korea},
  month    = {10},
  url      = {https://arxiv.org/pdf/1810.05993.pdf},
  keywords  = {press},
  timestamp = {2019-07-22},
}