Multi-agent trajectory prediction on 10-agent scenarios inferred goals benchmark leaderboard · SOTA2 Research

Multi-agent trajectory prediction on 10-agent scenarios inferred goals

0.2213ADE (m)PSN-Partial

Evaluation Results

Method	Links
PSN-Partial2025.04		0.2213	0.33	0.9909	3
PSN-Partial2025.04		0.2296	0.3295	0.991	2.62
PSN-Full2025.04		0.2314	0.3401	0.9936	3
Cost Evolution2025.04		0.2339	0.3426	0.9776	3
kNNs2025.04		0.2343	0.3485	0.9908	3
Hessian2025.04		0.236	0.3489	0.9928	3
CBF2025.04		0.2368	0.3519	0.9933	3
BF2025.04		0.2369	0.3508	0.9923	3
Gradient2025.04		0.2392	0.3517	0.993	3
Distance2025.04		0.2438	0.3638	0.9931	2.29
PSN-Full2025.04		0.2499	0.361	0.9936	2.06
Cost Evolution2025.04		0.4679	0.8002	0.9783	3
PSN-Full2025.04		0.4697	0.8054	0.9935	3
PSN-Full2025.04		0.4772	0.8094	0.9936	2.09
Distance2025.04		0.478	0.8083	0.9935	—
PSN-Partial2025.04		0.4782	0.8101	0.991	3
PSN-Partial2025.04		0.4794	0.8214	0.9909	2.65
kNNs2025.04		0.483	0.8272	0.9909	3
Gradient2025.04		0.4833	0.8323	0.9899	3
BF2025.04		0.4835	0.8274	0.9921	3
Hessian2025.04		0.4839	0.8289	0.9919	3
CBF2025.04		0.4843	0.8343	0.993	3