PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

  title={PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings},
  author={Nicholas Rhinehart and Rowan Thomas McAllister and Kris Kitani and Sergey Levine},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information. [] Key Method We train our model on real and simulated data to forecast vehicle trajectories given past positions and LIDAR. Our evaluation shows that our model is substantially more accurate in multi-agent driving scenarios compared to existing state-of-the-art. Beyond its general ability…

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  • Computer Science
    2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2019
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