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Trajectory Prediction With Latent Belief Energy-Based Model

CVPR 2021 · Probabilistic and scientific modeling › Molecules and proteins · approach: Multimodal trajectory prediction · 90 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use LB-EBM instead of PECNettrajectory prediction · Stanford Drone DatasetADE: 9.96 -> 8.87; 10.9%Helped
use LB-EBM instead of PECNettrajectory prediction · Stanford Drone DatasetFDE: 15.88 -> 15.61Helped
use LB-EBM instead of PECNettrajectory prediction · ETH-UCY AVGADE: 0.29 -> 0.21; 27.6%Helped
use LB-EBM instead of PECNettrajectory prediction · ETH-UCY AVGFDE: 0.48 -> 0.38Helped
replace the EBM prior with a conditional Gaussian (ablation)LB-EBM with a plan · Stanford Drone DatasetADE: 9.53 -> 8.87Helped
replace the EBM prior with a conditional Gaussian (ablation)LB-EBM with a plan · Stanford Drone DatasetFDE: 16.32 -> 15.61Helped
add the plan stage (ablation)EBM trajectory prediction · Stanford Drone DatasetADE: 10.28 -> 8.87Helped
add the plan stage (ablation)EBM trajectory prediction · Stanford Drone DatasetFDE: 18.60 -> 15.61Helped
add the EBM prior (ablation)Gaussian model without a plan · Stanford Drone DatasetADE: 18.61 -> 10.28Helped
add the EBM prior (ablation)Gaussian model without a plan · Stanford Drone DatasetFDE: 27.55 -> 18.60Helped
add social pooling (ablation)EBM with a plan · Stanford Drone DatasetADE: 9.23 -> 8.87Helped
add social pooling (ablation)EBM with a plan · Stanford Drone DatasetFDE: 16.57 -> 15.61Helped

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