Browse State-of-the-Art › Multi Future Trajectory Prediction
Multi Future Trajectory Prediction
6 papers with code · 0 benchmarks · 3 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
3 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
6 shown of 6 papers with code (8 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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4 Apr 2020 4 repositories listedIn this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
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14 Apr 2017 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.
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1 Feb 2022 1 repository listedSimultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations.
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5 Jun 2020 1 repository listedAutonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents.
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17 May 2020 1 repository listedAnticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications.
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13 Dec 2019 1 repository listed Syntology ran 2 of 3 samples · 1 unverifiedThe first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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