Spatiotemporal Learning of Directional Uncertainty in Urban Environments With Kernel Recurrent Mixture Density Networks

@article{Zhi2019SpatiotemporalLO,
  title={Spatiotemporal Learning of Directional Uncertainty in Urban Environments With Kernel Recurrent Mixture Density Networks},
  author={Weiming Zhi and Ransalu Senanayake and Lionel Ott and F{\'a}bio Ramos},
  journal={IEEE Robotics and Automation Letters},
  year={2019},
  volume={4},
  pages={4306-4313}
}
Autonomous vehicles operating in urban environments need to deal with an abundance of other dynamic objects, such as pedestrians and vehicles. This requires the development of predictive models that capture the complexity and long-term patterns of motion in the environment. We approach this problem by modeling movement directions of a typical object in the environment over time. We propose a method to build a continuous map to capture spatiotemporal movements. At a given coordinate in space and… CONTINUE READING

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  • ArXiv
  • 2019
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CITES METHODS & BACKGROUND

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