arXiv:2207.00170
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
원문 초록
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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GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
TrajFlow: Multi-modal Motion Prediction via Flow Matching
GRIP: Graph-based Interaction-aware Trajectory Prediction
DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory Prediction
Where, What, Why: Towards Explainable Driver Attention Prediction
Out-of-Distribution Semantic Occupancy Prediction
토론
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