MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction Article Swipe

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Yuning Chai , Benjamin Sapp , Mayank Bansal , Dragomir Anguelov ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1910.05449

Predicting human behavior is a difficult and crucial task required for motion planning. It is challenging in large part due to the highly uncertain and multi-modal set of possible outcomes in real-world domains such as autonomous driving. Beyond single MAP trajectory prediction, obtaining an accurate probability distribution of the future is an area of active interest. We present MultiPath, which leverages a fixed set of future state-sequence anchors that correspond to modes of the trajectory distribution. At inference, our model predicts a discrete distribution over the anchors and, for each anchor, regresses offsets from anchor waypoints along with uncertainties, yielding a Gaussian mixture at each time step. Our model is efficient, requiring only one forward inference pass to obtain multi-modal future distributions, and the output is parametric, allowing compact communication and analytical probabilistic queries. We show on several datasets that our model achieves more accurate predictions, and compared to sampling baselines, does so with an order of magnitude fewer trajectories.

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Artificial Intelligence

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Trajectory Computer science Multipath propagation Probabilistic logic Inference Parametric statistics Modal Set (abstract data type) Gaussian Gaussian process Sequence (biology) Probability distribution Algorithm Artificial intelligence Mathematics Statistics Biology Astronomy Genetics Computer network Chemistry Quantum mechanics Polymer chemistry Physics Programming language Channel (broadcasting)

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1910.05449
PDF: https://arxiv.org/pdf/1910.05449
OA Status: green
Cited By: 137
References: 28
Related Works: 20
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DOI: https://doi.org/10.48550/arxiv.1910.05449

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Title: MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-10-12

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Authors: Yuning Chai, Benjamin Sapp, Mayank Bansal, Dragomir Anguelov

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Landing page: https://arxiv.org/abs/1910.05449

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Concepts: Trajectory, Computer science, Multipath propagation, Probabilistic logic, Inference, Parametric statistics, Modal, Set (abstract data type), Gaussian, Gaussian process, Sequence (biology), Probability distribution, Algorithm, Artificial intelligence, Mathematics, Statistics, Biology, Astronomy, Genetics, Computer network, Chemistry, Quantum mechanics, Polymer chemistry, Physics, Programming language, Channel (broadcasting)

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Cited by: 137

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countries_distinct_count	0
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