Motion Forecasting On Argoverse Cvpr 2020

평가 지표

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)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표

모델 이름	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)
모델 1	0.9831	0.5933	0.1674	2.0999	1.7176	0.8853	3.7966	1.4055
tenet-transformer-encoding-network-for	0.9863	0.5596	0.1272	1.7667	1.6565	0.8121	3.6081	1.2141
모델 3	0.9263	0.7498	0.7498	4.9209	2.1686	2.1686	4.9209	4.9209
모델 4	0.8857	0.8348	0.8348	7.8875	3.5333	3.5333	7.8875	7.8875
모델 5	0.9183	0.7352	0.5901	4.0999	2.3865	1.6055	5.488	3.4055
모델 6	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 7	0.9877	0.5791	0.1432	1.9162	1.6856	0.8186	3.7588	1.2754
모델 8	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7642	1.355
모델 9	0.9838	0.5701	0.1438	1.8752	1.6242	0.8331	3.58	1.2748
모델 10	0.9124	0.8774	0.5504	4.1457	3.467	1.7875	7.4546	3.4512
모델 11	0.9856	0.7161	0.1589	2.1082	2.2709	0.9583	5.0216	1.4138
모델 12	0.9738	0.6685	0.6685	4.0972	1.8312	1.8312	4.0972	4.0972
모델 13	0.9874	0.5598	0.1306	1.78	1.6244	0.81	3.5572	1.1996
모델 14	0.9887	0.6467	0.1676	2.3613	1.9108	0.99	4.2597	1.6668
모델 15	0.9671	0.8355	0.4889	4.1988	3.6495	1.888	8.1197	3.5044
모델 16	0.9826	0.669	0.1849	2.083	2.0363	0.9105	4.5807	1.4341
모델 17	0.9843	0.5665	0.1474	1.954	1.6217	0.8294	3.5508	1.2595
모델 18	0.9759	0.6795	0.6795	4.1704	1.8705	1.8705	4.1704	4.1704
모델 19	0.9871	0.5877	0.1434	1.9922	1.7368	0.8948	3.7874	1.2978
leveraging-future-relationship-reasoning-for	0.9878	0.5728	0.143	1.9365	1.7063	0.8165	3.7486	1.2671
모델 21	0.9912	0.5918	0.205	2.3544	1.8594	1.1463	3.8216	1.7597
home-heatmap-output-for-future-motion	0.983	0.5723	0.0846	1.8601	1.6986	0.8904	3.681	1.2919
모델 23	0.8441	0.8868	0.8868	8.7522	3.9771	3.9771	8.7522	8.7522
모델 24	0.9185	0.7668	0.7668	5.7168	2.1875	2.1875	5.0224	5.0224
모델 25	0.9691	0.6825	0.6825	4.2673	1.9123	1.9123	4.2673	4.2673
모델 26	0.7042	0.9967	0.9893	26.4057	30.7524	28.3813	31.1513	25.7113
모델 27	0.9882	0.5401	0.1223	1.8095	1.5097	0.7543	3.3621	1.1376
모델 28	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 29	0.9883	0.5555	0.1297	1.8738	1.6266	0.7863	3.5825	1.1974
모델 30	0.9835	0.6019	0.1586	2.044	1.7453	0.8642	3.8981	1.3496
모델 31	0.9922	0.5154	0.1032	1.6820	1.4412	0.7282	3.1777	1.0566
모델 32	0.9799	0.6036	0.1764	2.0408	1.73	0.8621	3.8782	1.4071
모델 33	0.9848	0.6354	0.1677	2.0884	1.89	0.8888	4.2265	1.401
모델 34	0.9829	0.5871	0.1471	1.9006	1.6864	0.8406	3.7477	1.2785
모델 35	0.9854	0.6122	0.1692	2.1486	1.7343	0.8963	3.8532	1.4542
lanercnn-distributed-representations-for	0.9903	0.5685	0.1232	2.147	1.6852	0.9038	3.6916	1.4526
모델 37	0.9931	0.5348	0.5348	3.2923	1.5407	1.5407	3.2923	3.2923
모델 38	0.9835	0.5756	0.5756	3.5154	1.5969	1.5969	3.5154	3.5154
모델 39	0.9875	0.6749	0.2585	2.8266	2.0414	1.1518	4.6325	2.1322
모델 40	0.9834	0.6241	0.6241	3.9973	1.809	1.809	3.9973	3.9973
모델 41	0.755	0.9851	0.9817	23.394	12.3146	11.8296	23.7276	22.6995
r-pred-two-stage-motion-prediction-via-tube	0.992	0.5344	0.1165	1.7765	1.5843	0.7629	3.4718	1.1236
모델 43	0.9898	0.5867	0.115	2.0978	1.9105	1.2187	3.8217	1.5582
모델 44	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 45	0.9892	0.5754	0.1303	1.8584	1.6788	0.8194	3.7087	1.2186
모델 46	0.9514	0.7892	0.4141	3.5654	2.9029	1.4811	6.8301	2.871
모델 47	0.1475	1.0	0.8261	6.3362	38.9455	3.541	57.7046	5.6417
모델 48	0.9118	0.8087	0.8087	5.8239	2.5344	2.5344	5.8239	5.8239
모델 49	0.9817	0.5827	0.1651	2.0651	1.6666	0.866	3.6886	1.3707
multi-modal-motion-prediction-with	0.9852	0.6023	0.1429	1.9393	1.735	0.8372	3.9007	1.2905
모델 51	0.9812	0.6321	0.1791	2.0798	1.8436	0.9013	4.0875	1.4384
모델 52	0.8934	0.7861	0.3389	2.7277	2.5904	1.1394	5.7132	2.0333
모델 53	0.9849	0.6272	0.1591	2.006	1.8103	0.8703	3.9733	1.3505
모델 54	0.9893	0.5435	0.1163	1.7963	1.5099	0.7797	3.3297	1.1675
모델 55	0.9824	0.5966	0.1666	2.0714	1.7168	0.8702	3.8171	1.377
모델 56	0.8697	0.8449	0.8449	8.3792	3.3726	3.3726	7.7392	7.7392
wayformer-motion-forecasting-via-simple	0.9893	0.5716	0.1186	1.7408	1.636	0.7676	3.6559	1.1616
모델 58	0.9864	0.5634	0.1408	1.9737	1.6262	0.8308	3.5851	1.2793
모델 59	0.9485	0.7601	0.678	4.9666	3.0107	2.0839	6.688	4.2735
모델 60	0.964	0.8355	0.5821	4.7203	3.6495	2.0775	8.1197	4.0258
모델 61	0.9812	0.5926	0.1647	1.9981	1.7163	0.8809	3.801	1.3845
모델 62	0.9866	0.5565	0.1427	1.9744	1.5851	0.8444	3.4838	1.28
모델 63	0.9851	0.6277	0.195	2.5316	1.9152	1.0783	4.194	1.8371
prank-motion-prediction-based-on-ranking	0.9891	0.5955	0.5955	3.8239	1.7284	1.7284	3.8239	3.8239
모델 65	0.9847	0.5764	0.1395	1.8988	1.645	0.8277	3.6029	1.2532
모델 66	0.9437	0.7964	0.4199	3.8051	2.5911	1.8703	5.6038	3.1107
thomas-trajectory-heatmap-output-with-learned-1	0.9781	0.5613	0.1038	1.9736	1.6686	0.9423	3.593	1.4388
모델 68	0.9568	0.7171	0.5363	4.2643	2.4032	1.6939	5.4621	3.5698
모델 69	0.9909	0.553	0.1209	1.8188	1.6287	0.818	3.5263	1.1888
모델 70	0.9825	0.6333	0.1732	2.2004	1.8785	0.9172	4.2259	1.506
모델 71	0.9842	0.6178	0.154	2.0328	1.7737	0.8436	4.0033	1.3383
모델 72	0.4323	0.9936	0.9312	24.3515	17.0122	12.6584	34.3376	23.657
모델 73	0.9192	0.7773	0.7773	5.1247	2.2181	2.2181	5.1247	5.1247
모델 74	0.9179	0.6576	0.5428	3.7921	1.9103	1.5031	4.2923	3.0976
모델 75	0.9521	0.943	0.3539	2.8102	4.9745	1.2216	10.0298	2.1157
trajectory-forecasting-on-temporal-graphs	0.9837	0.5984	0.1528	1.9285	1.7716	0.8607	3.9031	1.3055
모델 77	0.4222	0.6517	0.3579	2.994	2.3896	1.8694	4.1008	2.2995
모델 78	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
모델 79	0.3525	0.737	0.6583	5.0426	22.3728	2.1476	25.4924	4.7166
모델 80	0.9742	0.9083	0.5156	4.8253	5.5699	2.2968	11.0515	4.1309
모델 81	0.9865	0.7452	0.1589	2.048	2.1902	0.848	4.915	1.3536
모델 82	0.9776	0.7285	0.1951	2.1576	2.2283	0.9163	5.0944	1.4631
모델 83	0.9815	0.6375	0.1865	2.0434	1.8637	0.8832	4.1511	1.4262
모델 84	0.9876	0.718	0.3276	3.2327	2.4228	1.549	5.2943	2.5765
모델 85	0.99	0.575	0.1266	1.8567	1.715	0.8285	3.7582	1.2403
모델 86	0.9825	0.5981	0.1603	1.9863	1.7978	0.861	4.0415	1.3527
모델 87	0.9891	0.5972	0.146	1.946	1.7338	0.8636	3.8476	1.3399
모델 88	0.9816	0.5933	0.1217	2.1386	1.7995	0.9892	3.8357	1.5159
모델 89	0.9875	0.5843	0.1258	1.9759	1.6791	0.8817	3.6321	1.2815
모델 90	0.947	0.6688	0.3857	3.2277	1.9276	1.3568	4.2738	2.4881
모델 91	0.9889	0.5862	0.148	1.9635	1.698	0.836	3.7395	1.2863
모델 92	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 93	0.9877	0.6322	0.1707	2.1092	1.9536	0.8841	4.2854	1.4204
모델 94	0.9848	0.6312	0.1635	2.0574	1.8397	0.8758	4.1061	1.3715
모델 95	0.984	0.6151	0.1059	2.1898	1.8014	0.9779	3.9418	1.4953
모델 96	0.9776	0.5968	0.1738	2.0975	1.731	0.8775	3.8592	1.4031
모델 97	0.9888	0.5742	0.1327	1.9423	1.6492	0.8674	3.5879	1.2703
모델 98	0.9805	0.6158	0.1666	2.0857	1.7762	0.8986	3.9079	1.3913
모델 99	0.9839	0.6201	0.6201	3.9861	1.8062	1.8062	3.9861	3.9861
모델 100	0.9044	0.8197	0.8197	6.1954	2.7291	2.7291	6.1954	6.1954
모델 101	0.9408	0.8659	0.5203	3.8899	3.3858	1.684	7.6186	3.1954
모델 102	0.9816	0.6329	0.1804	2.1001	1.9016	0.916	4.2119	1.4591
모델 103	0.8878	0.8709	0.655	5.6308	3.4152	2.5705	7.2377	4.9364
ganet-goal-area-network-for-motion	0.9899	0.5499	0.1179	1.7899	1.5921	0.806	3.4548	1.1605
모델 105	0.89	0.8602	0.8602	6.0771	2.7271	2.7271	6.0771	6.0771
모델 106	0.9814	0.6353	0.1795	2.1037	1.8988	0.8943	4.2353	1.4645
모델 107	0.9898	0.5814	0.1341	1.918	1.6561	0.8012	3.6496	1.2235
모델 108	0.8654	0.9068	0.9068	6.992	3.2553	3.2553	6.992	6.992
모델 109	0.2939	0.9999	0.8237	6.2931	32.9034	3.2331	46.7268	5.5987
모델 110	0.9495	0.7756	0.3983	2.9967	2.7949	1.3502	6.0524	2.3501
모델 111	0.9903	0.5579	0.121	1.8817	1.6117	0.813	3.5087	1.1873
모델 112	0.9725	0.6344	0.2179	2.243	1.9044	0.9436	4.1917	1.5486
모델 113	0.9769	0.6702	0.2129	2.1611	2.0401	0.9687	4.5299	1.5716
모델 114	0.8697	0.889	0.889	6.7247	3.1088	3.1088	6.7247	6.7247
모델 115	0.9826	0.6074	0.1619	2.0682	1.7365	0.8715	3.8482	1.3737
모델 116	0.9699	0.7837	0.2189	2.3277	2.6392	0.9693	6.029	1.6332
모델 117	0.9807	0.5852	0.1696	2.0993	1.6737	0.8858	3.6837	1.4049
모델 118	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
모델 119	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
holistic-transformer-a-joint-neural-network	0.9865	0.5496	0.1303	1.9172	1.5692	0.8123	3.4284	1.2227
multipath-efficient-information-fusion-and	0.9876	0.5645	0.1324	1.7932	1.6235	0.7897	3.6141	1.2144
모델 122	0.9143	0.806	0.806	5.8185	2.5329	2.5329	5.8185	5.8185
모델 123	0.9743	0.6879	0.3191	2.7237	2.0987	1.1624	4.6642	2.1036
모델 124	0.832	0.9064	0.709	5.6071	5.8841	2.823	11.3051	4.9126
모델 125	0.981	0.5962	0.1725	2.1136	1.7191	0.8883	3.8039	1.4192
모델 126	0.7756	0.9238	0.9238	8.1441	3.9521	3.9521	8.1441	8.1441
모델 127	0.9827	0.5875	0.1591	2.04	1.6883	0.862	3.7274	1.3456
모델 128	0.9602	0.6278	0.214	2.2688	1.8335	0.9303	4.1274	1.5984
모델 129	0.8983	0.8379	0.7805	6.0565	2.6883	2.4943	5.8451	5.362
모델 130	0.991	0.7828	0.2904	3.2196	2.9576	1.4537	6.3673	2.5476
모델 131	0.9718	0.8457	0.4903	3.7943	3.3035	1.6056	7.4299	3.0999
모델 132	0.8857	0.8348	0.8348	7.8875	3.5333	3.5333	7.8875	7.8875
모델 133	0.9865	0.7656	0.3969	3.1788	2.5888	1.4161	5.7465	2.4845
모델 134	0.9808	0.6127	0.1779	2.4781	1.8312	1.0416	4.01	1.7936
모델 135	0.9397	0.7411	0.3553	2.8147	2.2558	1.1807	5.1581	2.2379
모델 136	0.958	0.802	0.3434	3.0901	3.0338	1.4024	7.0702	2.3957
모델 137	0.9092	0.5887	0.1501	2.0058	11.3861	11.2834	3.7832	1.3114
모델 138	0.9818	0.5889	0.1673	2.0717	1.7041	0.8663	3.7827	1.3772
모델 139	0.951	0.6791	0.6791	4.5196	2.0031	2.0031	4.5196	4.5196
crat-pred-vehicle-trajectory-prediction-with	0.9558	0.6323	0.2624	2.5926	1.8162	1.0626	4.0576	1.8981
모델 141	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 142	0.7734	0.8151	0.6899	5.3363	2.8257	2.1564	6.3946	4.6394
모델 143	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
모델 144	0.9886	0.5577	0.1351	1.9175	1.6453	0.7949	3.6269	1.2235
모델 145	0.9584	0.7471	0.3393	3.2021	2.4611	1.2522	5.5965	2.5305
모델 146	0.9494	0.7301	0.2845	2.8131	2.1057	1.1343	4.8259	2.1187
모델 147	0.8857	0.8348	0.8348	7.8875	3.5333	3.5333	7.8875	7.8875
모델 148	0.8857	0.8348	0.8348	7.8875	3.5333	3.5333	7.8875	7.8875
tpcn-temporal-point-cloud-networks-for-motion	0.9884	0.5601	0.1333	1.9286	1.5752	0.8153	3.4872	1.2442
dcms-motion-forecasting-with-dual-consistency	0.9902	0.5322	0.1094	1.7564	1.4768	0.7659	3.2515	1.135
모델 151	0.8704	0.85	0.85	6.457	2.9719	2.9719	6.457	6.457
모델 152	0.9846	0.5831	0.1376	1.929	1.7012	0.827	3.7811	1.2395
모델 153	0.9139	0.8388	0.4931	3.7583	2.9838	1.6327	6.8853	3.0638
모델 154	0.9833	0.6453	0.1699	2.0926	1.8763	0.8935	4.1783	1.3981
모델 155	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
모델 156	0.9898	0.5514	0.1207	1.8006	1.5959	0.8057	3.4648	1.1693
모델 157	0.9878	0.5378	0.1155	1.7654	1.5349	0.8053	3.3216	1.1385
모델 158	0.9895	0.6286	0.129	1.8691	1.8521	0.7854	4.1163	1.1872
모델 159	0.9887	0.5766	0.1383	1.883	1.6572	0.8332	3.6604	1.2694
모델 160	0.9883	0.5395	0.1143	1.7568	1.5599	0.8014	3.3814	1.2139
모델 161	0.9793	0.5928	0.1657	2.0651	1.712	0.8683	3.7985	1.3707
모델 162	0.979	0.6742	0.1884	2.0783	2.3148	0.9414	5.053	1.409
모델 163	0.9799	0.6142	0.6142	4.5214	1.7303	1.7303	3.827	3.827
모델 164	0.9859	0.5601	0.1416	1.966	1.6225	0.8351	3.5641	1.2715
모델 165	0.985	0.5876	0.1622	2.0414	1.7438	0.8879	3.7642	1.3718
모델 166	0.9812	0.6542	0.1937	2.1405	1.9953	0.9776	4.3111	1.5183
gohome-graph-oriented-heatmap-output	0.9811	0.5724	0.1048	1.9834	1.6887	0.9425	3.6468	1.4503
모델 168	0.987	0.6465	0.1615	2.0891	1.9673	0.908	4.3037	1.4413
모델 169	0.8366	0.9572	0.9572	12.8813	7.1746	7.1746	12.8813	12.8813
모델 170	0.9833	0.6306	0.6306	4.0092	1.8179	1.8179	4.0092	4.0092
모델 171	0.8722	0.8348	0.8168	8.2618	3.5333	3.3861	7.8875	7.5673
모델 172	0.9876	0.6085	0.1539	1.9737	1.7874	0.8367	4.0418	1.3222
모델 173	0.9105	0.9563	0.4048	3.0322	4.7665	1.2684	10.8683	2.3377
모델 174	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
모델 175	0.983	0.8634	0.1864	2.1343	3.0009	0.8892	7.0697	1.4399
모델 176	0.9815	0.6321	0.1891	2.2003	1.8768	0.9347	4.1366	1.5058
모델 177	0.98	0.5905	0.1634	2.0585	1.706	0.8679	3.7786	1.364
모델 178	0.8592	0.8677	0.8677	6.8531	2.7385	2.7385	6.1586	6.1586
모델 179	0.9818	0.6237	0.1699	2.1368	1.7988	0.9096	3.9852	1.4424
모델 180	0.9868	0.6305	0.2317	2.1485	1.8426	0.9193	4.1231	1.5334
모델 181	0.9897	0.5275	0.1065	1.7313	1.5181	0.7709	3.2849	1.1057
모델 182	0.9758	0.7508	0.2887	2.6675	2.771	1.2052	5.7482	1.973
모델 183	0.864	0.9386	0.9386	9.5077	4.6564	4.6564	9.5077	9.5077
ssl-lanes-self-supervised-learning-for-motion	0.9844	0.5671	0.1326	1.9433	1.6342	0.8401	3.5643	1.2493
모델 185	0.9881	0.54	0.1223	1.8092	1.5111	0.7537	3.3645	1.137
모델 186	0.8688	0.8715	0.5718	4.276	3.4549	1.8114	7.8828	3.5816
모델 187	0.9298	0.7648	0.7648	5.8479	2.2831	2.2831	5.1534	5.1534
모델 188	0.9893	0.5261	0.1101	1.6942	1.491	0.7623	3.2628	1.1337
모델 189	0.8857	0.8348	0.8348	7.8875	3.5333	3.5333	7.8875	7.8875
모델 190	0.1816	0.6574	0.1035	2.2793	14.9928	14.9112	4.1642	1.5849
모델 191	0.9874	0.5503	0.1362	1.9461	1.5852	0.8181	3.47	1.2517
모델 192	0.9833	0.575	0.154	2.0305	1.6547	0.8525	3.6489	1.3361
모델 193	0.9762	0.8405	0.2186	2.3584	2.797	0.9877	6.6511	1.6639
모델 194	0.9873	0.5791	0.1437	1.9309	1.7061	0.8142	3.7626	1.2651
모델 195	0.9831	0.6008	0.1146	2.0712	1.7491	0.9518	3.8435	1.498
모델 196	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7642	1.355
모델 197	0.9197	0.7789	0.3983	3.0871	2.5448	1.4307	5.6608	2.3926
모델 198	0.9563	0.6589	0.2096	2.225	2.2842	1.2904	4.3966	1.5956
모델 199	0.9888	0.5606	0.1374	1.8392	1.6186	0.8157	3.5573	1.2425
모델 200	0.9384	0.7347	0.7205	5.4699	2.1416	2.1	4.8748	4.7755
모델 201	0.9846	0.5831	0.151	2.0214	1.6768	0.8605	3.6951	1.327
모델 202	0.7834	0.9056	0.6785	5.1399	4.4349	2.2765	9.5225	4.4455
모델 203	0.9268	0.7574	0.7574	5.054	2.1962	2.1962	5.054	5.054
모델 204	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 205	0.9892	0.5563	0.1324	1.8872	1.6405	0.7917	3.6226	1.2124
모델 206	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 207	0.9884	0.563	0.1354	1.8981	1.6602	0.7973	3.662	1.2283
모델 208	0.973	0.7074	0.2471	2.4713	2.4627	1.1662	5.1875	1.7768
모델 209	0.9808	0.637	0.1397	2.1093	2.1461	1.3818	4.227	1.4396
모델 210	0.8676	0.8715	0.5369	3.9814	3.4549	1.7129	7.8828	3.287
모델 211	0.9578	0.7294	0.5128	3.9753	2.232	1.6091	4.7064	3.2809
모델 212	0.9818	0.6118	0.1048	2.1863	1.7812	0.9574	3.9003	1.4919
모델 213	0.8879	0.912	0.912	8.8578	3.5646	3.5646	8.1634	8.1634
모델 214	0.9857	0.583	0.1203	2.0584	1.6973	0.8688	3.7573	1.3639
모델 215	0.8893	0.8902	0.6457	5.4818	4.4824	2.1201	9.4305	4.7874
모델 216	0.98	0.5909	0.2333	2.3223	1.6737	0.9296	3.7541	1.6362
모델 217	0.9815	0.6172	0.1592	2.1039	1.803	0.8779	4.0235	1.4095
모델 218	0.9042	0.8117	0.8117	6.1454	2.3805	2.3805	5.451	5.451
모델 219	0.9815	0.6288	0.1669	2.1164	1.8233	0.8995	4.0307	1.422
모델 220	0.9836	0.6422	0.1823	2.3104	2.0029	1.1322	4.345	1.6453
모델 221	0.9187	0.779	0.7392	5.6948	2.4611	2.3434	5.3231	5.0003
모델 222	0.982	0.591	0.1652	2.0725	1.7189	0.8803	3.7918	1.3781
query-centric-trajectory-prediction	0.9887	0.5257	0.1056	1.6934	1.5234	0.7340	3.3420	1.0666
모델 224	0.9877	0.5764	0.1418	1.9773	1.6724	0.8393	3.6518	1.2828
모델 225	0.9671	0.8097	0.4177	3.4799	2.8104	1.3998	6.5063	2.7821
모델 226	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
모델 227	0.9567	0.7802	0.4051	3.5141	2.6885	1.4608	6.2682	2.8196
모델 228	0.9637	0.8705	0.5423	4.255	3.7408	1.7691	8.3529	3.5606
모델 229	0.9912	0.5528	0.1075	1.794	1.6124	0.7819	3.5116	1.1127
모델 230	0.9922	0.6133	0.142	2.1114	1.8386	0.9116	4.0309	1.4503
모델 231	0.9883	0.5586	0.136	1.8915	1.6588	0.7943	3.6568	1.2212
모델 232	0.9807	0.5871	0.1611	2.0482	1.6906	0.8626	3.7418	1.3537
모델 233	0.9518	0.6808	0.6808	5.0872	1.9424	1.9424	4.3927	4.3927
모델 234	0.9506	0.6982	0.6982	4.6211	2.0489	2.0489	4.6211	4.6211
모델 235	0.7064	0.9476	0.9476	11.4556	5.7132	5.7132	11.4556	11.4556
모델 236	0.9814	0.5891	0.1674	2.0789	1.7055	0.8741	3.7705	1.3844
모델 237	0.9656	0.7032	0.2611	2.8381	2.3379	1.2774	5.0578	2.1774
모델 238	0.9834	0.5967	0.1623	2.0699	1.6968	0.8667	3.7496	1.3755
모델 239	0.9862	0.5998	0.1352	1.8678	1.6702	0.8026	3.7724	1.2339
hivt-hierarchical-vector-transformer-for	0.9888	0.5473	0.1267	1.8422	1.5984	0.7735	3.5328	1.1693
모델 241	0.9623	0.9878	0.3135	2.7502	7.4994	1.2128	17.191	2.0558
모델 242	0.9834	0.5935	0.1606	2.0374	1.7001	0.856	3.775	1.3429
모델 243	0.9588	0.6971	0.2508	2.6708	2.1728	1.1287	4.8418	1.9764
모델 244	0.9894	0.5475	0.1177	1.7483	1.5737	0.8046	3.4467	1.1554
모델 245	0.9896	0.5517	0.1209	1.8067	1.6072	0.8091	3.4945	1.1744
모델 246	0.984	0.5795	0.1576	1.9618	1.6585	0.8579	3.6581	1.3432
모델 247	0.8676	0.8715	0.5369	3.9814	3.4549	1.7129	7.8828	3.287
모델 248	0.9756	0.639	0.1858	2.0592	1.8599	0.9191	4.0927	1.4475
hivt-hierarchical-vector-transformer-for	0.9891	0.5431	0.1221	1.8171	1.5619	0.7673	3.4449	1.146
모델 250	0.9927	0.82	0.4181	3.3584	2.9076	1.3836	6.5416	2.6639
모델 251	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7643	1.355
모델 252	0.9868	0.6856	0.1308	2.1154	1.7414	0.9973	4.2372	1.4209
모델 253	0.9511	0.7824	0.3591	2.9845	2.7262	1.3988	5.9819	2.29
모델 254	0.9878	0.7159	0.7159	5.0682	2.2719	2.2719	5.0682	5.0682
모델 255	0.98	0.6016	0.6016	4.3633	1.6557	1.6557	3.6689	3.6689
모델 256	0.9886	0.5449	0.1168	1.7512	1.5689	0.789	3.4067	1.1602
모델 257	0.9897	0.5467	0.1245	1.8347	1.5531	0.772	3.4533	1.1626
모델 258	0.9875	0.5431	0.1124	1.7286	1.5327	0.7606	3.3611	1.1213
모델 259	0.8892	0.8045	0.5201	3.4092	2.6784	1.4519	5.999	2.7148
모델 260	0.8726	0.904	0.4083	3.191	3.3098	1.2687	8.0179	2.4966
모델 261	0.9756	0.6097	0.1779	2.1286	1.7902	0.8832	4.0114	1.4341
모델 262	0.9886	0.5696	0.1282	1.9211	1.6338	0.8468	3.5752	1.2494
모델 263	0.9885	0.5573	0.1331	1.8932	1.6377	0.7933	3.614	1.2193
모델 264	0.937	0.8574	0.5247	4.5742	4.7286	2.3101	9.7385	3.8798
모델 265	0.9899	0.5921	0.1255	1.8868	1.8108	0.8026	4.0551	1.2321
모델 266	0.9891	0.5955	0.5955	3.8239	1.7284	1.7284	3.8239	3.8239
모델 267	0.9812	0.5877	0.162	2.0539	1.7019	0.8703	3.7624	1.3622
모델 268	0.9762	0.6133	0.2208	2.2641	1.7721	0.9602	3.9013	1.5696
모델 269	0.9725	0.6567	0.6567	4.1159	1.8525	1.8525	4.1159	4.1159
모델 270	0.8332	0.8917	0.5458	4.1094	10634487.5868	155985.7998	8.6403	3.4149
모델 271	0.8676	0.8715	0.5369	3.9814	3.4549	1.7129	7.8828	3.287
모델 272	0.8988	0.8884	0.7382	5.6682	2.9871	2.4191	7.0155	4.9738
모델 273	0.9805	0.5927	0.1689	2.0776	1.7288	0.8716	3.8359	1.3832
모델 274	0.9838	0.6001	0.1618	1.9623	1.7396	0.8341	3.9297	1.3393
모델 275	0.9836	0.5873	0.1597	2.0495	1.7024	0.8679	3.7642	1.355
모델 276	0.8965	0.8139	0.8139	6.5202	2.6003	2.6003	5.8257	5.8257
모델 277	0.946	0.7494	0.6582	4.787	2.9337	2.0282	6.4175	4.0938
모델 278	0.9913	0.5573	0.1207	1.7992	1.632	0.8344	3.5505	1.1568
모델 279	0.8977	0.8122	0.6916	6.1315	2.9631	2.3432	6.8116	5.437
모델 280	0.9814	0.5945	0.1638	2.0668	1.7252	0.8706	3.8222	1.3723
모델 281	0.985	0.5988	0.1032	2.0759	1.7029	0.9106	3.6961	1.3814
모델 282	0.97	0.6868	0.6868	4.1982	1.8684	1.8684	4.1982	4.1982
모델 283	0.8982	0.8141	0.8141	5.4732	2.418	2.418	5.4732	5.4732
tnt-target-driven-trajectory-prediction	0.9889	0.7097	0.1656	2.1401	2.174	0.9097	4.9593	1.4457
모델 285	0.916	0.7907	0.7119	5.0674	2.3053	2.0458	5.2734	4.6173
모델 286	0.987	0.8556	0.1349	1.932	2.7484	0.7963	6.6845	1.2225
모델 287	0.9829	0.6087	0.3264	2.882	1.7732	1.1741	3.8707	2.1875
모델 288	0.8805	0.8696	0.8696	6.4158	2.9498	2.9498	6.4158	6.4158
모델 289	0.9872	0.5715	0.1425	1.9902	1.6427	0.866	3.6103	1.3072
모델 290	0.879	0.8388	0.5419	4.6814	3.0551	1.9767	6.7766	3.9867
모델 291	0.9797	0.6001	0.1735	2.0179	1.7239	0.8536	3.8623	1.384
모델 292	0.9033	0.8915	0.8915	8.0293	3.9358	3.9358	8.0293	8.0293
모델 293	0.9836	0.5828	0.158	2.0408	1.6837	0.8615	3.7242	1.3464
모델 294	0.9842	0.7081	0.1522	2.0115	2.0558	0.8406	4.8659	1.3171
모델 295	0.9884	0.5625	0.1472	1.8566	1.6214	0.8335	3.5485	1.2955
모델 296	0.9889	0.7097	0.1656	2.1401	2.174	0.9097	4.9593	1.4457
모델 297	0.9842	0.6779	0.1823	2.3691	1.986	1.0267	4.4898	1.7883
모델 298	0.9845	0.5984	0.5984	4.435	1.6997	1.6997	3.7406	3.7406
모델 299	0.9176	0.7701	0.7701	5.9661	2.2923	2.2923	5.2717	5.2717