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Papers

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

2022-09-20 · Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele

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.

📄 PDF Abstract BibTeX arXiv:2209.10033

Code (2)

sshaoshuai/mtr 공식 구현 pytorch
vita-epfl/unitraj pytorch

Tasks

motion predictionPrediction

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음
Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Residual Connection 설명 없음
Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…
Position-Wise Feed-Forward Layer 설명 없음
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Adam 설명 없음

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