Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions

@article{Ferguson2014RealTimePM,
  title={Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions},
  author={Sarah Ferguson and Brandon Luders and Robert C. Grande and J. How},
  journal={ArXiv},
  year={2014},
  volume={abs/1405.5581}
}
To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning… Expand
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