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MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction

MPS-Authors
/persons/resource/persons265837

Shi,  Shaoshuai
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

/persons/resource/persons265835

Jiang,  Li
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

/persons/resource/persons261420

Dai,  Dengxin
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

/persons/resource/persons45383

Schiele,  Bernt       
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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フルテキスト (公開)

arXiv:2209.10033.pdf
(プレプリント), 231KB

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引用

Shi, S., Jiang, L., Dai, D., & Schiele, B. (2022). MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction. Retrieved from https://arxiv.org/abs/2209.10033.


引用: https://hdl.handle.net/21.11116/0000-000C-184C-5
要旨
In this report, we present the 1st place solution for motion prediction track
in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer
framework for multimodal motion prediction, which introduces a small set of
novel motion query pairs for generating better multimodal future trajectories
by jointly performing the intention localization and iterative motion
refinement. A simple model ensemble strategy with non-maximum-suppression is
adopted to further boost the final performance. Our approach achieves the 1st
place on the motion prediction leaderboard of 2022 Waymo Open Dataset
Challenges, outperforming other methods with remarkable margins. Code will be
available at https://github.com/sshaoshuai/MTR.