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Predicting Human Trajectories by Learning and Matching Patterns

arXiv 2104.10241 ↗First posted 20 Apr 2021cs.AI cs.LGcited by 0

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Abstract (arXiv)

As more and more robots are envisioned to cooperate with humans sharing the same space, it is desired for robots to be able to predict others' trajectories to navigate in a safe and self-explanatory way. We propose a Convolutional Neural Network-based approach to learn, detect, and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly, the proposed approach unveils the obscurity in the previous use of a pooling layer, presenting a way to intuitively explain the decision-making process.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 20 Apr 2021.

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References · 18

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6 more
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