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

Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction

ICLR 2022 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Interactive multi-agent trajectory prediction · 38 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add joint social attention during future trajectory generation (ablation)AutoBot · TrajNet++ validation setNumber of collisions: 1144 -> 139Helped
Add joint social attention during future trajectory generation (ablation)AutoBot · TrajNet++ validation setScene-level Min ADE (6): 0.316 -> 0.128Helped
Add joint social attention during future trajectory generation (ablation)AutoBot · TrajNet++ validation setScene-level Min FDE (6): 0.431 -> 0.234Helped
Add joint social attention during future trajectory generation (ablation)AutoBot · TrajNet++ validation setEgo Agent's Min ADE (6): 0.098 -> 0.095Helped
Use an ensemble of three AutoBot-Ego modelsAutoBot-Ego · nuScenesMin ADE (10): 1.05 -> 1.03Helped
Remove map information (ablation)AutoBot-Ego · nuScenestrajectory prediction metricsHurt
Remove discrete latent variables and decoder seed parameters (ablation)AutoBot-Ego · nuScenesADEHurt
Replace the seed-parameter decoder with an autoregressive decoder (ablation)AutoBot · inference speedinference rateHurt

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