Multi-agent motion simulation on INTERACTION dataset (test)
0RMSE (m)Ground Truth
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Ground TruthObservation Type=N/A, Training Dataset=None (recorded data)2025.12 | 0 | 47 | 12 | |
| OursObservation Type=instance-centric, Number of Parameters=429K, Training Dataset=DeepScenario2025.12 | 16.65 | 249 | 63 | |
| Ours (small)Observation Type=instance-centric, Number of Parameters=59K, Training Dataset=DeepScenario2025.12 | 17.84 | 249 | 115 | |
| GraphAIRLObservation Type=agent-centric, Number of Parameters=145K, Training Dataset=DeepScenario, Target Reward Training=true2025.12 | 19.71 | 1,026 | 209 | |
| BCObservation Type=instance-centric, Number of Parameters=429K, Training Dataset=DeepScenario2025.12 | 20.19 | 1,351 | 1,968 | |
| LateFusionMLPObservation Type=agent-centric, Number of Parameters=104K, Training Dataset=DeepScenario, Target Reward Training=true2025.12 | 20.25 | 1,348 | 899 | |
| GraphAIRLObservation Type=agent-centric, Number of Parameters=145K, Training Dataset=DeepScenario, Target Reward Training=false2025.12 | 20.47 | 1,110 | 537 | |
| CVObservation Type=N/A, Training Dataset=N/A (Learning-free)2025.12 | 23.17 | 2,823 | 2,774 |