Motion Forecasting On Argoverse Cvpr 2020
المقاييس
DAC (K=6)
MR (K=1)
MR (K=6)
brier-minFDE (K=6)
minADE (K=1)
minADE (K=6)
minFDE (K=1)
minFDE (K=6)
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
اسم النموذج | DAC (K=6) | MR (K=1) | MR (K=6) | brier-minFDE (K=6) | minADE (K=1) | minADE (K=6) | minFDE (K=1) | minFDE (K=6) | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|---|---|
| fyyyy | 0.9831 | 0.5933 | 0.1674 | 2.0999 | 1.7176 | 0.8853 | 3.7966 | 1.4055 | - | - |
| MacFormer | 0.9863 | 0.5596 | 0.1272 | 1.7667 | 1.6565 | 0.8121 | 3.6081 | 1.2141 | TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction | - |
| Challenge | 0.9263 | 0.7498 | 0.7498 | 4.9209 | 2.1686 | 2.1686 | 4.9209 | 4.9209 | - | - |
| NCTU_309512033 | 0.8857 | 0.8348 | 0.8348 | 7.8875 | 3.5333 | 3.5333 | 7.8875 | 7.8875 | - | - |
| Multiple Trajectories | 0.9183 | 0.7352 | 0.5901 | 4.0999 | 2.3865 | 1.6055 | 5.488 | 3.4055 | - | - |
| Constant-V-Forecating(very very naive) | 0.8977 | 0.8122 | 0.6916 | 6.1315 | 2.9631 | 2.3432 | 6.8116 | 5.437 | - | - |
| MFT | 0.9877 | 0.5791 | 0.1432 | 1.9162 | 1.6856 | 0.8186 | 3.7588 | 1.2754 | - | - |
| hitljx_test_sub | 0.9836 | 0.5873 | 0.1597 | 2.0495 | 1.7024 | 0.8679 | 3.7642 | 1.355 | - | - |
| whr_test | 0.9838 | 0.5701 | 0.1438 | 1.8752 | 1.6242 | 0.8331 | 3.58 | 1.2748 | - | - |
| HYU_ACE | 0.9124 | 0.8774 | 0.5504 | 4.1457 | 3.467 | 1.7875 | 7.4546 | 3.4512 | - | - |
| DF-RNN | 0.9856 | 0.7161 | 0.1589 | 2.1082 | 2.2709 | 0.9583 | 5.0216 | 1.4138 | - | - |
| phuang | 0.9738 | 0.6685 | 0.6685 | 4.0972 | 1.8312 | 1.8312 | 4.0972 | 4.0972 | - | - |
| be_s2lossf_3846 | 0.9874 | 0.5598 | 0.1306 | 1.78 | 1.6244 | 0.81 | 3.5572 | 1.1996 | - | - |
| lstm | 0.9887 | 0.6467 | 0.1676 | 2.3613 | 1.9108 | 0.99 | 4.2597 | 1.6668 | - | - |
| Gilgamesh | 0.9671 | 0.8355 | 0.4889 | 4.1988 | 3.6495 | 1.888 | 8.1197 | 3.5044 | - | - |
| CMAN(av1_demo) | 0.9826 | 0.669 | 0.1849 | 2.083 | 2.0363 | 0.9105 | 4.5807 | 1.4341 | - | - |
| CU-aware LaneGCN | 0.9843 | 0.5665 | 0.1474 | 1.954 | 1.6217 | 0.8294 | 3.5508 | 1.2595 | - | - |
| tjxu | 0.9759 | 0.6795 | 0.6795 | 4.1704 | 1.8705 | 1.8705 | 4.1704 | 4.1704 | - | - |
| Lotus | 0.9871 | 0.5877 | 0.1434 | 1.9922 | 1.7368 | 0.8948 | 3.7874 | 1.2978 | - | - |
| FRM | 0.9878 | 0.5728 | 0.143 | 1.9365 | 1.7063 | 0.8165 | 3.7486 | 1.2671 | Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction | - |
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