2021 IEEE/CVF International Conference on Computer Vision (ICCV) 2021
DOI: 10.1109/iccv48922.2021.01502
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DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets

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Cited by 299 publications
(170 citation statements)
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“…More recently, a family of models are proposed to improve prediction accuracy and coverage by first predicting high-level intentions, such as goal targets [11,13,15,26,30,42], lanes to follow [20,33], and maneuver actions [8,9], before predicting low-level trajectories conditioning on each intention. Such models demonstrate great success in predicting accurate trajectories for single agents in popular trajectory prediction benchmarks, such as Argoverse [6] and Waymo Open Motion Dataset [10].…”
Section: Related Workmentioning
confidence: 99%
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“…More recently, a family of models are proposed to improve prediction accuracy and coverage by first predicting high-level intentions, such as goal targets [11,13,15,26,30,42], lanes to follow [20,33], and maneuver actions [8,9], before predicting low-level trajectories conditioning on each intention. Such models demonstrate great success in predicting accurate trajectories for single agents in popular trajectory prediction benchmarks, such as Argoverse [6] and Waymo Open Motion Dataset [10].…”
Section: Related Workmentioning
confidence: 99%
“…Such models demonstrate great success in predicting accurate trajectories for single agents in popular trajectory prediction benchmarks, such as Argoverse [6] and Waymo Open Motion Dataset [10]. While our proposed approach M2I can use an arbitrary prediction model, we choose to adopt an anchor-free goal-based predictor [15] because of its outstanding performance.…”
Section: Related Workmentioning
confidence: 99%
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