Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics

Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics

For safe navigation in dynamic environments, an autonomous vehicle must be able to identify and predict the future behaviors of other mobile agents. A promising data-driven approach is to learn motion patterns from previous observations using Gaussian process (GP) regression, which are then used for...

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Bibliographic Details
Main Authors: Chen, Yu Fan (Contributor), Liu, Miao (Contributor), Liu, Shih-Yuan (Contributor), Miller, Justin Lee (Contributor), How, Jonathan P (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
Format: Article
Language:English
Published: American Institute of Aeronautics and Astronautics (AIAA), 2018-04-13T18:32:35Z.
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