Trajectory Forecasting on Zara-2
0.11ADETrajectron++
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Trajectron++sampling=Best-of-202022.06 | 0.11 | 0.25 | |
| NPSN-NCE-Trajectron++sampling_strategy=NPSN2022.03 | 0.14 | 0.25 | |
| NPSN-SGCNsampling_strategy=NPSN2022.03 | 0.14 | 0.25 | |
| ScePTsampling=Best-of-202022.06 | 0.14 | 0.81 | |
| NCE-Trajectron++2022.03 | 0.16 | 0.31 | |
| NPSN-PECNetsampling_strategy=NPSN2022.03 | 0.16 | 0.25 | |
| NPSN-Trajectron++sampling_strategy=NPSN2022.03 | 0.16 | 0.28 | |
| Trajectron++2022.03 | 0.18 | 0.32 | |
| PECNet2022.03 | 0.19 | 0.33 | |
| ScePTmode=most likely2022.06 | 0.19 | 1.2 | |
| SGCN2022.03 | 0.22 | 0.42 | |
| NPSN-STGCNNsampling_strategy=NPSN2022.03 | 0.22 | 0.38 | |
| Trajectron++mode=most likely2022.06 | 0.23 | 0.59 | |
| MATFsampling=Best-of-202022.06 | 0.26 | 0.57 | |
| NPSN-Causal-STGATsampling_strategy=NPSN2022.03 | 0.27 | 0.56 | |
| Causal-STGAT2022.03 | 0.28 | 0.58 | |
| STGAT2022.03 | 0.29 | 0.6 | |
| Social-STGCNN2022.03 | 0.3 | 0.48 | |
| NPSN-STGATsampling_strategy=NPSN2022.03 | 0.3 | 0.62 | |
| S-ATTNmode=most likely2022.06 | 0.3 | 2.13 | |
| STARObservations=82022.03 | 0.31 | 0.71 | |
| CVMObservations=82022.03 | 0.32 | 0.72 | |
| SR-LSTMObservations=82022.03 | 0.32 | 0.7 | |
| STT + DTO (2 obs)Observations=2, Method=Distilling the Observations (DTO)2022.03 | 0.34 | 0.74 | |
| NPSN-SGANsampling_strategy=NPSN2022.03 | 0.34 | 0.7 | |
| STT (8 obs)Observations=82022.03 | 0.36 | 0.77 | |
| SoPhiesampling=Best-of-202022.06 | 0.38 | 0.78 | |
| ST-GAT 1V-1Observations=82022.03 | 0.4 | 0.86 | |
| Ind-TFObservations=82022.03 | 0.42 | 0.81 | |
| Social-GAN2022.03 | 0.42 | 0.84 | |
| S-GANsampling=Best-of-202022.06 | 0.42 | 0.84 | |
| STT (2 obs)Observations=22022.03 | 0.44 | 0.88 | |
| S-LSTMmode=most likely2022.06 | 0.56 | 1.17 |