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TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction

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arxiv 2207.00170 v1 pith:BVMPSJZF submitted 2022-06-30 cs.CV cs.AI

Yuting Wang , Hangning Zhou , Zhigang Zhang , Chen Feng , Huadong Lin , Chaofei Gao , Yizhi Tang , Zhenting Zhao
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Shiyu Zhang Jie Guo Xuefeng Wang Ziyao Xu Chi Zhang
This is my paper · ORCID
classification cs.CVcs.AI
keywords methodencodingmotionpredictioneffectiveensembleflownetwork
verification ladder T0 review T1 audit T2 compute T3 formal
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This technical report presents an effective method for motion prediction in autonomous driving. We develop a Transformer-based method for input encoding and trajectory prediction. Besides, we propose the Temporal Flow Header to enhance the trajectory encoding. In the end, an efficient K-means ensemble method is used. Using our Transformer network and ensemble method, we win the first place of Argoverse 2 Motion Forecasting Challenge with the state-of-the-art brier-minFDE score of 1.90.

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