Trajectory Prediction on Hotel ETH-UCY (test)
0.11ADELED
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| LEDNumber of samples (S)=202024.03 | 0.11 | 0.17 | — | |
| Trajectron++sampling=Best-of-202020.07 | 0.12 | 0.19 | — | |
| BiTraP-NPdistribution=Non-Parametric, sampling=Best-of-202020.07 | 0.12 | 0.21 | — | |
| EqMotionNumber of samples (S)=202024.03 | 0.12 | 0.18 | — | |
| EigenTrajectoryNumber of samples (S)=202024.03 | 0.12 | 0.19 | — | |
| EqMotionM=202024.02 | 0.12 | 0.18 | — | |
| Trajectron++Evaluation Setting=Best-of-202026.04 | 0.12 | 0.19 | — | |
| BiTraP-GMMdistribution=Gaussian Mixture Model, sampling=Best-of-202020.07 | 0.13 | 0.22 | — | |
| LB-EBMProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.13 | 0.2 | — | |
| SocialVAENumber of samples (S)=202024.03 | 0.13 | 0.19 | — | |
| SingularTrajectoryNumber of samples (S)=202024.03 | 0.13 | 0.19 | — | |
| MGFM=202024.02 | 0.13 | 0.2 | — | |
| AgentFormerNumber of samples (S)=202024.03 | 0.14 | 0.22 | — | |
| AgentFormerM=202024.02 | 0.14 | 0.22 | — | |
| GroupNetNumber of samples (S)=202024.03 | 0.15 | 0.25 | — | |
| GroupNetM=202024.02 | 0.15 | 0.25 | — | |
| NPSNNumber of samples (S)=202024.03 | 0.16 | 0.25 | — | |
| MyriadEvaluation Setting=Best-of-20, zero-shot=true2026.04 | 0.17 | 0.3 | — | |
| PECNetsampling=Best-of-202020.07 | 0.18 | 0.24 | — | |
| PECNetProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.18 | 0.24 | — | |
| GP-GraphNumber of samples (S)=202024.03 | 0.18 | 0.3 | — | |
| PECNetM=202024.02 | 0.18 | 0.24 | — | |
| STARNumber of samples (S)=202024.03 | 0.19 | 0.37 | — | |
| Trajectron++Number of samples (S)=202024.03 | 0.2 | 0.28 | — | |
| Trajectron++M=202024.02 | 0.2 | 0.28 | — | |
| MIDM=202024.02 | 0.2 | 0.35 | — | |
| FlowChainM=202024.02 | 0.2 | 0.35 | — | |
| MIDNumber of samples (S)=202024.03 | 0.21 | 0.33 | — | |
| LBEBMNumber of samples (S)=202024.03 | 0.21 | 0.38 | — | |
| PECNetNumber of samples (S)=202024.03 | 0.22 | 0.39 | — | |
| Trajectron++Evaluation Setting=Deterministic2026.04 | 0.22 | 0.46 | — | |
| PIFProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.3 | 0.59 | — | |
| MyriadEvaluation Setting=Deterministic, zero-shot=true2026.04 | 0.3 | 0.54 | — | |
| STGATM=202024.02 | 0.35 | 0.66 | — | |
| TrajectronEvaluation Setting=Best-of-202026.04 | 0.35 | 0.66 | — | |
| SR-LSTM-2Probabilistic=false, History frames=8, Prediction frames=122021.04 | 0.37 | 0.74 | — | |
| LinearProbabilistic=false, History frames=8, Prediction frames=122021.04 | 0.39 | 0.72 | — | |
| S-BiGATsampling=Best-of-202020.07 | 0.49 | 1.01 | — | |
| Social-BiGATProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.49 | 1.01 | — | |
| Social-STGCNNProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.49 | 0.85 | — | |
| STGCNNNumber of samples (S)=202024.03 | 0.49 | 0.85 | — | |
| Social-STGCNNM=202024.02 | 0.49 | 0.85 | — | |
| STSGNProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.63 | 1.01 | — | |
| S-GAN-PProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.67 | 1.37 | — | |
| MATFProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.67 | 1.37 | — | |
| Social-GANNumber of samples (S)=202024.03 | 0.67 | 1.37 | — | |
| Social-GANM=202024.02 | 0.67 | 1.37 | — | |
| GATProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.68 | 1.4 | — | |
| CGNSProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.7 | 0.93 | — | |
| S-GANsampling=Best-of-202020.07 | 0.72 | 1.61 | — | |
| SocialGANEvaluation Setting=Best-of-202026.04 | 0.72 | 1.61 | — | |
| SoPhiesampling=Best-of-202020.07 | 0.76 | 1.67 | — | |
| SoPhieProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.76 | 1.67 | — | |
| S-LSTMProbabilistic=true, History frames=8, Prediction frames=122021.04 | 0.79 | 1.76 | — | |
| SocialLSTMEvaluation Setting=Deterministic2026.04 | 0.79 | 1.76 | — | |
| Tunable Soft EquivarianceArchitecture=Autoregressive Transformer2026.03 | 5.69 | 6.26 | 0.41 | |
| BaseArchitecture=Autoregressive Transformer2026.03 | 5.84 | 6.67 | 2.5 | |
| EqAutoArchitecture=Equivariant Autoregressive Transformer2026.03 | 6.16 | 6.78 | 0 |