Trajectory Forecasting on ETH
0.59FDENPSN-SGCN
Evaluation results
| Method | Links | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| NPSN-SGCNsampling_strategy=NPSN2022.03 | 0.59 | 0.36 | — | — | — | — | — | — | — | |
| NPSN-NCE-Trajectron++sampling_strategy=NPSN2022.03 | 0.62 | 0.4 | — | — | — | — | — | — | — | |
| Expert Traj2022.03 | 0.62 | 0.3 | 61.77 | 0.034 | — | — | — | — | — | |
| NPSN-STGCNNsampling_strategy=NPSN2022.03 | 0.65 | 0.44 | — | — | — | — | — | — | — | |
| ScePTsampling=Best-of-202022.06 | 0.65 | 0.1 | — | — | — | — | — | — | — | |
| NPSN-Trajectron++sampling_strategy=NPSN2022.03 | 0.78 | 0.52 | — | — | — | — | — | — | — | |
| Trajectron++Training Strategy=Social-NCE2020.12 | 0.791 | — | — | — | — | — | — | 0 | 100 | |
| Trajectron++Training Strategy=Vanilla2020.12 | 0.81 | — | — | — | — | — | — | 1.16 | — | |
| Trajectron++sampling=Best-of-202022.06 | 0.83 | 0.39 | — | — | — | — | — | — | — | |
| Trajectron++2022.03 | 0.83 | 0.39 | 3.04 | 0.286 | 1.34 | — | — | — | — | |
| NPSN-PECNetsampling_strategy=NPSN2022.03 | 0.88 | 0.55 | — | — | — | — | — | — | — | |
| NPSN-Causal-STGATsampling_strategy=NPSN2022.03 | 0.9 | 0.56 | — | — | — | — | — | — | — | |
| Causal-STGAT2022.03 | 0.98 | 0.6 | — | — | — | — | — | — | — | |
| SGCN2022.03 | 1 | 0.57 | — | — | — | — | — | — | — | |
| NCE-Trajectron++2022.03 | 1.02 | 0.56 | — | — | — | — | — | — | — | |
| NPSN-STGATsampling_strategy=NPSN2022.03 | 1.02 | 0.61 | — | — | — | — | — | — | — | |
| Trajectron++2022.03 | 1.03 | 0.61 | — | — | — | — | — | — | — | |
| PECNet2022.03 | 1.07 | 0.61 | — | — | — | — | — | — | — | |
| STT (8 obs)Observations=82022.03 | 1.1 | 0.54 | — | — | — | — | — | — | — | |
| STARObservations=82022.03 | 1.11 | 0.56 | — | — | — | — | — | — | — | |
| Social-STGCNN2022.03 | 1.11 | 0.64 | — | — | — | — | — | — | — | |
| S-STGCNN2022.03 | 1.11 | 0.64 | 3.73 | 0.094 | 6.83 | — | — | — | — | |
| STGAT2022.03 | 1.12 | 0.65 | — | — | — | — | — | — | — | |
| STT + DTO (2 obs)Observations=2, Method=Distilling the Observations (DTO)2022.03 | 1.22 | 0.62 | — | — | — | — | — | — | — | |
| Social-STGCNNTraining Strategy=Vanilla2020.12 | 1.223 | — | — | — | — | — | — | 1.33 | — | |
| Social-STGCNNTraining Strategy=Social-NCE2020.12 | 1.224 | — | — | — | — | — | — | 0.61 | 54.1 | |
| Ind-TFObservations=82022.03 | 1.25 | 0.6 | — | — | — | — | — | — | — | |
| SR-LSTMObservations=82022.03 | 1.25 | 0.63 | — | — | — | — | — | — | — | |
| NPSN-SGANsampling_strategy=NPSN2022.03 | 1.26 | 0.72 | — | — | — | — | — | — | — | |
| ScePTmode=most likely2022.06 | 1.33 | 0.19 | — | — | — | — | — | — | — | |
| ST-GAT 1V-1Observations=82022.03 | 1.36 | 0.69 | — | — | — | — | — | — | — | |
| SoPhiesampling=Best-of-202022.06 | 1.43 | 0.7 | — | — | — | — | — | — | — | |
| Social-Implicit2022.03 | 1.44 | 0.66 | 3.05 | 0.127 | 5.08 | — | — | — | — | |
| STT (2 obs)Observations=22022.03 | 1.45 | 0.72 | — | — | — | — | — | — | — | |
| S-GANsampling=Best-of-202022.06 | 1.52 | 0.81 | — | — | — | — | — | — | — | |
| S-GAN2022.03 | 1.52 | 0.81 | 3.94 | 0.373 | 5.02 | — | — | — | — | |
| Social-GAN2022.03 | 1.62 | 0.87 | — | — | — | — | — | — | — | |
| Trajectron++mode=most likely2022.06 | 1.66 | 0.71 | — | — | — | — | — | — | — | |
| MATFsampling=Best-of-202022.06 | 1.75 | 1.01 | — | — | — | — | — | — | — | |
| CVMObservations=82022.03 | 2.28 | 1.07 | — | — | — | — | — | — | — | |
| S-LSTMmode=most likely2022.06 | 2.35 | 1.09 | — | — | — | — | — | — | — | |
| S-ATTNmode=most likely2022.06 | 3.74 | 0.39 | — | — | — | — | — | — | — | |
| DD-MDNEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | 0.32 | 0.51 | — | — | |
| EigenTrajEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | 0.46 | 0.76 | — | — | |
| MIDEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | 0.63 | 1.05 | — | — | |
| SingTrajEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | 0.45 | 0.67 | — | — |