MultiXNet: Multiclass Multistage Multimodal Motion Prediction
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future. To address this task we propose MultiXNet, an end-to-end approach for detection and motion prediction based di...
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Main Authors | , , , , , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
02.06.2020
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Subjects | |
Online Access | Get full text |
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Summary: | One of the critical pieces of the self-driving puzzle is understanding the
surroundings of a self-driving vehicle (SDV) and predicting how these
surroundings will change in the near future. To address this task we propose
MultiXNet, an end-to-end approach for detection and motion prediction based
directly on lidar sensor data. This approach builds on prior work by handling
multiple classes of traffic actors, adding a jointly trained second-stage
trajectory refinement step, and producing a multimodal probability distribution
over future actor motion that includes both multiple discrete traffic behaviors
and calibrated continuous position uncertainties. The method was evaluated on
large-scale, real-world data collected by a fleet of SDVs in several cities,
with the results indicating that it outperforms existing state-of-the-art
approaches. |
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DOI: | 10.48550/arxiv.2006.02000 |