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Research map / Multi-agent systems and games / Human-AI and goal-conditioned

HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction

CVPR 2022 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Interactive multi-agent trajectory prediction · 457 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add agent-agent interaction (ablation)HiVT-64 · Argoverse validationminADE: 1.00 -> 0.69Helped
add temporal learning module (ablation)HiVT-64 · Argoverse validationminADE: 0.77 -> 0.69Helped
add agent-lane interaction (ablation)HiVT-64 · Argoverse validationminADE: 0.73 -> 0.69Helped
add global interaction module (ablation)HiVT-64 · Argoverse validationminADE: 0.71 -> 0.69Helped
use a gated update (ablation)HiVT-64 spatial attention · Argoverse validationminADE: 0.70 -> 0.69Helped
use a temporal mask (ablation)HiVT-64 temporal attention · Argoverse validationminADE: 0.70 -> 0.69Helped
use vector-set translation-invariant representation instead of point-set representation (ablation)HiVT-64 · Argoverse validationminADE: 1.09 -> 0.73Helped
add rotation-invariant spatial learning (ablation)HiVT-64 · Argoverse validationminADE: 0.73 -> 0.69Helped
add rotation-invariant spatial learning (ablation)HiVT-128 · Argoverse validationminADE: 0.69 -> 0.66Helped
add global interaction to the local-only model with r=50 (ablation)HiVT-64 · Argoverse validationminADE: 0.73 -> 0.69Helped
increase local-region radius from r=50 to r=80 (ablation)HiVT-64 local+global · Argoverse validationminADE: 0.69 -> 0.69No clear effect

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