mlsanityData to 14 Oct 2026

Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction

arXiv 2609.25942 ↗First posted 22 Sep 2026cs.RO cs.AIcited by 0

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Abstract (arXiv)

Robots operating around pedestrians often reason over a finite set of predicted human futures. Repeated online updates can concentrate this limited prediction budget on dominant destinations and leave plausible alternatives underrepresented or absent, removing those alternatives from the finite representation available to downstream decision making. We introduce Destination Support Restoration (DSR), a causal post-selection operator that repairs destination support without retraining the host predictor or increasing the maintained set size. At a repair step, DSR evaluates a temporary destination-stratified candidate bank from the observed prefix, converts candidate evidence into integer target counts, protects representatives of active modes, and reallocates redundant surplus hypotheses to deficient modes. The maintained and returned sets retain exactly N hypotheses, and DSR replaces at most lceilρ Nrceil entries. Protected representatives preserve current categorical support; lineage-aware particle filters also preserve surviving resampling ancestors. Each replacement reduces the allocation mismatch to the evidence-driven target by one. On the complete 3,719-trajectory Edinburgh protocol over three seeds, DSR reduces MIF weighted ADE and FDE by 13.36% and 13.30% at N=64. Paired integrations with CLiFF, PPT, causal GDTS, Social Informer, and PECNet improve both metrics in every evaluated pair. These results show that finite-set support allocation is a useful prediction-side control point when a fixed hypothesis set serves as the interface to downstream systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 22 Sep 2026.

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References · 14

  1. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
  2. 2020It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Predictioncited by 236
  3. 2022Stochastic Trajectory Prediction via Motion Indeterminacy Diffusioncited by 185
  4. 2020From Goals, Waypoints & Paths To Long Term Human Trajectory Forecastingcited by 167
  5. 2023Leapfrog Diffusion Model for Stochastic Trajectory Predictioncited by 132
  6. 2021SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Predictioncited by 97
  7. 2021Differentiable Particle Filtering via Entropy-Regularized Optimal Transportcited by 59
  8. 2022Non-Probability Sampling Network for Stochastic Human Trajectory Predictioncited by 46
  9. 2017Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detectioncited by 42
  10. 2020Likelihood-Based Diverse Sampling for Trajectory Forecastingcited by 16
  11. 2024Progressive Pretext Task Learning for Human Trajectory Predictioncited by 16
  12. 2020Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTMcited by 10
2 more
  1. 2023CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Predictioncited by 5
  2. 2023GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Predictioncited by 3

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