motion-forecasting-on-argoverse-cvpr-2020

Autonomous DrivingMotion Forecasting

Results over time

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Leaderboard

Paper	Code	brier-minFDE (K=6)	minFDE (K=6)	MR (K=6)	minADE (K=6)	DAC (K=6)	minFDE (K=1)	MR (K=1)	minADE (K=1)	ModelName	ReleaseDate
		1.6820	1.0566	0.1032	0.7282	0.9922	3.1777	0.5154	1.4412	SEPT
Query-Centric Trajectory Prediction	✓ Link	1.6934	1.0666	0.1056	0.7340	0.9887	3.3420	0.5257	1.5234	QCNet	2023-01-01
		1.6942	1.1337	0.1101	0.7623	0.9893	3.2628	0.5261	1.491	ProphNet
		1.7286	1.1213	0.1124	0.7606	0.9875	3.3611	0.5431	1.5327	FFINet
		1.7313	1.1057	0.1065	0.7709	0.9897	3.2849	0.5275	1.5181	VI LaneIter
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks	✓ Link	1.7408	1.1616	0.1186	0.7676	0.9893	3.6559	0.5716	1.636	Wayformer	2022-07-12
		1.7483	1.1554	0.1177	0.8046	0.9894	3.4467	0.5475	1.5737	ProIn_av1
		1.7512	1.1602	0.1168	0.789	0.9886	3.4067	0.5449	1.5689	Gnet
Bootstrap Motion Forecasting With Self-Consistent Constraints		1.7564	1.135	0.1094	0.7659	0.9902	3.2515	0.5322	1.4768	DCMS	2022-04-12
		1.7568	1.2139	0.1143	0.8014	0.9883	3.3814	0.5395	1.5599	PAGA
		1.7654	1.1385	0.1155	0.8053	0.9878	3.3216	0.5378	1.5349	DuanZX
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction		1.7667	1.2141	0.1272	0.8121	0.9863	3.6081	0.5596	1.6565	MacFormer	2022-06-30
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement		1.7765	1.1236	0.1165	0.7629	0.992	3.4718	0.5344	1.5843	R-Pred	2022-11-16
		1.78	1.1996	0.1306	0.81	0.9874	3.5572	0.5598	1.6244	be_s2lossf_3846
GANet: Goal Area Network for Motion Forecasting	✓ Link	1.7899	1.1605	0.1179	0.806	0.9899	3.4548	0.5499	1.5921	GANet	2022-09-20
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction	✓ Link	1.7932	1.2144	0.1324	0.7897	0.9876	3.6141	0.5645	1.6235	multipath++	2021-11-29
		1.794	1.1127	0.1075	0.7819	0.9912	3.5116	0.5528	1.6124	TO
		1.7963	1.1675	0.1163	0.7797	0.9893	3.3297	0.5435	1.5099	TPCN++
		1.7992	1.1568	0.1207	0.8344	0.9913	3.5505	0.5573	1.632	ATTTHOM
		1.8006	1.1693	0.1207	0.8057	0.9898	3.4648	0.5514	1.5959	Ameame
		1.8067	1.1744	0.1209	0.8091	0.9896	3.4945	0.5517	1.6072	Met
		1.8092	1.137	0.1223	0.7537	0.9881	3.3645	0.54	1.5111	TSGN
		1.8095	1.1376	0.1223	0.7543	0.9882	3.3621	0.5401	1.5097	shernan_wu
HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction	✓ Link	1.8171	1.146	0.1221	0.7673	0.9891	3.4449	0.5431	1.5619	HiVT++	2022-01-01
		1.8188	1.1888	0.1209	0.818	0.9909	3.5263	0.553	1.6287	HIKVISION-ADLab-hz
		1.8347	1.1626	0.1245	0.772	0.9897	3.4533	0.5467	1.5531	LAformer
		1.8392	1.2425	0.1374	0.8157	0.9888	3.5573	0.5606	1.6186	m2a2_34
HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction	✓ Link	1.8422	1.1693	0.1267	0.7735	0.9888	3.5328	0.5473	1.5984	HiVT-128	2022-01-01
		1.8566	1.2955	0.1472	0.8335	0.9884	3.5485	0.5625	1.6214	LTP
		1.8567	1.2403	0.1266	0.8285	0.99	3.7582	0.575	1.715	Jack-M
		1.8584	1.2186	0.1303	0.8194	0.9892	3.7087	0.5754	1.6788	DSP
HOME: Heatmap Output for future Motion Estimation	✓ Link	1.8601	1.2919	0.0846	0.8904	0.983	3.681	0.5723	1.6986	HOME + GOHOME	2021-05-23
		1.8678	1.2339	0.1352	0.8026	0.9862	3.7724	0.5998	1.6702	mm_kk_parallel_distemb_91
		1.8691	1.1872	0.129	0.7854	0.9895	4.1163	0.6286	1.8521	FGNet
		1.8738	1.1974	0.1297	0.7863	0.9883	3.5825	0.5555	1.6266	matrix_mugen
		1.8752	1.2748	0.1438	0.8331	0.9838	3.58	0.5701	1.6242	whr_test
		1.8817	1.1873	0.121	0.813	0.9903	3.5087	0.5579	1.6117	chl(yiqi)
		1.883	1.2694	0.1383	0.8332	0.9887	3.6604	0.5766	1.6572	ATDSNet-v2
		1.8868	1.2321	0.1255	0.8026	0.9899	4.0551	0.5921	1.8108	SceneTransformer
		1.8872	1.2124	0.1324	0.7917	0.9892	3.6226	0.5563	1.6405	cls_weight1.2-decoder5-use-last
		1.8915	1.2212	0.136	0.7943	0.9883	3.6568	0.5586	1.6588	cls-weight1-5
		1.8932	1.2193	0.1331	0.7933	0.9885	3.614	0.5573	1.6377	decoder-3-last
		1.8981	1.2283	0.1354	0.7973	0.9884	3.662	0.563	1.6602	Hi_base
		1.8988	1.2532	0.1395	0.8277	0.9847	3.6029	0.5764	1.645	lstm_v1
		1.9006	1.2785	0.1471	0.8406	0.9829	3.7477	0.5871	1.6864	prediction-liangdian
		1.9162	1.2754	0.1432	0.8186	0.9877	3.7588	0.5791	1.6856	MFT
Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles		1.9172	1.2227	0.1303	0.8123	0.9865	3.4284	0.5496	1.5692	Holistic Transformer	2022-06-17
		1.9175	1.2235	0.1351	0.7949	0.9886	3.6269	0.5577	1.6453	xia
		1.918	1.2235	0.1341	0.8012	0.9898	3.6496	0.5814	1.6561	FGNet_sub
		1.9211	1.2494	0.1282	0.8468	0.9886	3.5752	0.5696	1.6338	DTNet(FDE)
Trajectory Forecasting on Temporal Graphs	✓ Link	1.9285	1.3055	0.1528	0.8607	0.9837	3.9031	0.5984	1.7716	FTGN	2022-07-01
TPCN: Temporal Point Cloud Networks for Motion Forecasting		1.9286	1.2442	0.1333	0.8153	0.9884	3.4872	0.5601	1.5752	TPCN	2021-03-04
		1.929	1.2395	0.1376	0.827	0.9846	3.7811	0.5831	1.7012	yiqi-fudan
		1.9309	1.2651	0.1437	0.8142	0.9873	3.7626	0.5791	1.7061	vill
		1.932	1.2225	0.1349	0.7963	0.987	6.6845	0.8556	2.7484	parallel_tl_123_0224
Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction		1.9365	1.2671	0.143	0.8165	0.9878	3.7486	0.5728	1.7063	FRM	2023-05-24
Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving		1.9393	1.2905	0.1429	0.8372	0.9852	3.9007	0.6023	1.735	Waypoint	2021-09-14
		1.9423	1.2703	0.1327	0.8674	0.9888	3.5879	0.5742	1.6492	DIDI
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving	✓ Link	1.9433	1.2493	0.1326	0.8401	0.9844	3.5643	0.5671	1.6342	SSL-Lanes	2022-06-28
		1.946	1.3399	0.146	0.8636	0.9891	3.8476	0.5972	1.7338	Shangguan_IFS
		1.9461	1.2517	0.1362	0.8181	0.9874	3.47	0.5503	1.5852	EuclidNet-L
		1.954	1.2595	0.1474	0.8294	0.9843	3.5508	0.5665	1.6217	CU-aware LaneGCN
		1.9618	1.3432	0.1576	0.8579	0.984	3.6581	0.5795	1.6585	brier36
		1.9623	1.3393	0.1618	0.8341	0.9838	3.9297	0.6001	1.7396	Model0
		1.9635	1.2863	0.148	0.836	0.9889	3.7395	0.5862	1.698	vilab
		1.966	1.2715	0.1416	0.8351	0.9859	3.5641	0.5601	1.6225	Dcupqiu
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling		1.9736	1.4388	0.1038	0.9423	0.9781	3.593	0.5613	1.6686	THOMAS	2021-10-13
		1.9737	1.3222	0.1539	0.8367	0.9876	4.0418	0.6085	1.7874	MR
		1.9737	1.2793	0.1408	0.8308	0.9864	3.5851	0.5634	1.6262	vectorgcn
		1.9744	1.28	0.1427	0.8444	0.9866	3.4838	0.5565	1.5851	lt_tv_cont
		1.9759	1.2815	0.1258	0.8817	0.9875	3.6321	0.5843	1.6791	minFDE
		1.9773	1.2828	0.1418	0.8393	0.9877	3.6518	0.5764	1.6724	ToNet
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation		1.9834	1.4503	0.1048	0.9425	0.9811	3.6468	0.5724	1.6887	GOHOME	2021-09-04
		1.9863	1.3527	0.1603	0.861	0.9825	4.0415	0.5981	1.7978	JAL-MTP
		1.9902	1.3072	0.1425	0.866	0.9872	3.6103	0.5715	1.6427	ECARX-V1
		1.9922	1.2978	0.1434	0.8948	0.9871	3.7874	0.5877	1.7368	Lotus
		1.9981	1.3845	0.1647	0.8809	0.9812	3.801	0.5926	1.7163	lengyue
		2.0058	1.3114	0.1501	11.2834	0.9092	3.7832	0.5887	11.3861	Lane_vae
		2.006	1.3505	0.1591	0.8703	0.9849	3.9733	0.6272	1.8103	wo_longterm
		2.0115	1.3171	0.1522	0.8406	0.9842	4.8659	0.7081	2.0558	Parallel x-transformers version1.1
		2.0179	1.384	0.1735	0.8536	0.9797	3.8623	0.6001	1.7239	SCM+Distill(best model)
		2.0214	1.327	0.151	0.8605	0.9846	3.6951	0.5831	1.6768	88
		2.0305	1.3361	0.154	0.8525	0.9833	3.6489	0.575	1.6547	MSPre
		2.0328	1.3383	0.154	0.8436	0.9842	4.0033	0.6178	1.7737	mmTransformer
		2.0374	1.3429	0.1606	0.856	0.9834	3.775	0.5935	1.7001	scale4
		2.04	1.3456	0.1591	0.862	0.9827	3.7274	0.5875	1.6883	vi_avp
		2.0408	1.4071	0.1764	0.8621	0.9799	3.8782	0.6036	1.73	sample_replace_gt_smalln_82
		2.0408	1.3464	0.158	0.8615	0.9836	3.7242	0.5828	1.6837	daiyongjie
		2.0414	1.3718	0.1622	0.8879	0.985	3.7642	0.5876	1.7438	leon
		2.0434	1.4262	0.1865	0.8832	0.9815	4.1511	0.6375	1.8637	idlaber-Hans
		2.044	1.3496	0.1586	0.8642	0.9835	3.8981	0.6019	1.7453	l_DGA_lg
		2.048	1.3536	0.1589	0.848	0.9865	4.915	0.7452	2.1902	JMT
		2.0482	1.3537	0.1611	0.8626	0.9807	3.7418	0.5871	1.6906	(VI)
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	YuNi
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	watson
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	CY_ng
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	cbc
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	julie-1
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	ywyeh
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	hitchhiker
		2.0495	1.355	0.1597	0.8679	0.9836	3.7643	0.5873	1.7024	alfred
		2.0495	1.355	0.1597	0.8679	0.9836	3.7642	0.5873	1.7024	hitljx_test_sub
		2.0495	1.355	0.1597	0.8679	0.9836	3.7642	0.5873	1.7024	chailiang
		2.0495	1.355	0.1597	0.8679	0.9836	3.7642	0.5873	1.7024	zhousihong
		2.0539	1.3622	0.162	0.8703	0.9812	3.7624	0.5877	1.7019	LaneGCN
		2.0574	1.3715	0.1635	0.8758	0.9848	4.1061	0.6312	1.8397	AutoBot-Ego
		2.0584	1.3639	0.1203	0.8688	0.9857	3.7573	0.583	1.6973	SenseTime_AP
		2.0585	1.364	0.1634	0.8679	0.98	3.7786	0.5905	1.706	LGN
		2.0592	1.4475	0.1858	0.9191	0.9756	4.0927	0.639	1.8599	UAR
		2.0651	1.3707	0.1657	0.8683	0.9793	3.7985	0.5928	1.712	William
		2.0651	1.3707	0.1651	0.866	0.9817	3.6886	0.5827	1.6666	just_a_test
		2.0668	1.3723	0.1638	0.8706	0.9814	3.8222	0.5945	1.7252	LaneGCN-trainset-36
		2.0682	1.3737	0.1619	0.8715	0.9826	3.8482	0.6074	1.7365	sihong
		2.0699	1.3755	0.1623	0.8667	0.9834	3.7496	0.5967	1.6968	mymodel7_actornet_2_mapnet_a2m_a2a_222
		2.0712	1.498	0.1146	0.9518	0.9831	3.8435	0.6008	1.7491	numberEight
		2.0714	1.377	0.1666	0.8702	0.9824	3.8171	0.5966	1.7168	lanegcn_12_epoch
		2.0717	1.3772	0.1673	0.8663	0.9818	3.7827	0.5889	1.7041	15 train 40e
		2.0725	1.3781	0.1652	0.8803	0.982	3.7918	0.591	1.7189	Transformer1
		2.0759	1.3814	0.1032	0.9106	0.985	3.6961	0.5988	1.7029	Miss Rate
		2.0776	1.3832	0.1689	0.8716	0.9805	3.8359	0.5927	1.7288	huyuening
		2.0783	1.409	0.1884	0.9414	0.979	5.053	0.6742	2.3148	mt_navi
		2.0789	1.3844	0.1674	0.8741	0.9814	3.7705	0.5891	1.7055	Europa
		2.0798	1.4384	0.1791	0.9013	0.9812	4.0875	0.6321	1.8436	chant
		2.083	1.4341	0.1849	0.9105	0.9826	4.5807	0.669	2.0363	CMAN(av1_demo)
		2.0857	1.3913	0.1666	0.8986	0.9805	3.9079	0.6158	1.7762	ISY@TK
		2.0884	1.401	0.1677	0.8888	0.9848	4.2265	0.6354	1.89	somemethod
		2.0891	1.4413	0.1615	0.908	0.987	4.3037	0.6465	1.9673	Alibaba-ADLab
		2.0926	1.3981	0.1699	0.8935	0.9833	4.1783	0.6453	1.8763	HGO (K=6)
		2.0975	1.4031	0.1738	0.8775	0.9776	3.8592	0.5968	1.731	LaneGCN-s
		2.0978	1.5582	0.115	1.2187	0.9898	3.8217	0.5867	1.9105	PRIME
		2.0993	1.4049	0.1696	0.8858	0.9807	3.6837	0.5852	1.6737	TPA+Laneloss
		2.0999	1.4055	0.1674	0.8853	0.9831	3.7966	0.5933	1.7176	fyyyy
		2.1001	1.4591	0.1804	0.916	0.9816	4.2119	0.6329	1.9016	SCP
		2.1037	1.4645	0.1795	0.8943	0.9814	4.2353	0.6353	1.8988	player
		2.1039	1.4095	0.1592	0.8779	0.9815	4.0235	0.6172	1.803	MTN
		2.1082	1.4138	0.1589	0.9583	0.9856	5.0216	0.7161	2.2709	DF-RNN
		2.1092	1.4204	0.1707	0.8841	0.9877	4.2854	0.6322	1.9536	lagat_mm
		2.1093	1.4396	0.1397	1.3818	0.9808	4.227	0.637	2.1461	TestForecast
		2.1114	1.4503	0.142	0.9116	0.9922	4.0309	0.6133	1.8386	HTTP
		2.1136	1.4192	0.1725	0.8883	0.981	3.8039	0.5962	1.7191	s1
		2.1154	1.4209	0.1308	0.9973	0.9868	4.2372	0.6856	1.7414	Jean
		2.1164	1.422	0.1669	0.8995	0.9815	4.0307	0.6288	1.8233	wimp
		2.1286	1.4341	0.1779	0.8832	0.9756	4.0114	0.6097	1.7902	ulimit
		2.1343	1.4399	0.1864	0.8892	0.983	7.0697	0.8634	3.0009	rush
		2.1368	1.4424	0.1699	0.9096	0.9818	3.9852	0.6237	1.7988	ORIGINAL
		2.1386	1.5159	0.1217	0.9892	0.9816	3.8357	0.5933	1.7995	pred
		2.1401	1.4457	0.1656	0.9097	0.9889	4.9593	0.7097	2.174	Habitat-Web
TNT: Target-driveN Trajectory Prediction	✓ Link	2.1401	1.4457	0.1656	0.9097	0.9889	4.9593	0.7097	2.174	TNT - CoRL20	2020-08-19
		2.1405	1.5183	0.1937	0.9776	0.9812	4.3111	0.6542	1.9953	SCP
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting		2.147	1.4526	0.1232	0.9038	0.9903	3.6916	0.5685	1.6852	LaneRCNN (IROS 2021)	2021-01-17
		2.1485	1.5334	0.2317	0.9193	0.9868	4.1231	0.6305	1.8426	tp
		2.1486	1.4542	0.1692	0.8963	0.9854	3.8532	0.6122	1.7343	cxx
		2.1576	1.4631	0.1951	0.9163	0.9776	5.0944	0.7285	2.2283	NCTU-GPL-GPAL
		2.1611	1.5716	0.2129	0.9687	0.9769	4.5299	0.6702	2.0401	huangmozhi9527
		2.1863	1.4919	0.1048	0.9574	0.9818	3.9003	0.6118	1.7812	Tang Luqi
		2.1898	1.4953	0.1059	0.9779	0.984	3.9418	0.6151	1.8014	Tang Luqi
		2.2003	1.5058	0.1891	0.9347	0.9815	4.1366	0.6321	1.8768	tnt-mtp_100s_targetloss_1-1_6
		2.2004	1.506	0.1732	0.9172	0.9825	4.2259	0.6333	1.8785	leige
		2.225	1.5956	0.2096	1.2904	0.9563	4.3966	0.6589	2.2842	arky
		2.243	1.5486	0.2179	0.9436	0.9725	4.1917	0.6344	1.9044	uulm-mrm
		2.2641	1.5696	0.2208	0.9602	0.9762	3.9013	0.6133	1.7721	fyyclass
		2.2688	1.5984	0.214	0.9303	0.9602	4.1274	0.6278	1.8335	xjh
		2.2793	1.5849	0.1035	14.9112	0.1816	4.1642	0.6574	14.9928	tusiji12138
		2.3104	1.6453	0.1823	1.1322	0.9836	4.345	0.6422	2.0029	TrajectoryOracle
		2.3223	1.6362	0.2333	0.9296	0.98	3.7541	0.5909	1.6737	damplyv1
		2.3277	1.6332	0.2189	0.9693	0.9699	6.029	0.7837	2.6392	Map Static+Specific
		2.3544	1.7597	0.205	1.1463	0.9912	3.8216	0.5918	1.8594	argo_test_18_5m_add_rotation_change_data_change_model_3_01
		2.3584	1.6639	0.2186	0.9877	0.9762	6.6511	0.8405	2.797	6 mode model
		2.3613	1.6668	0.1676	0.99	0.9887	4.2597	0.6467	1.9108	lstm
		2.3691	1.7883	0.1823	1.0267	0.9842	4.4898	0.6779	1.986	zhanzhenxueyuan
		2.4713	1.7768	0.2471	1.1662	0.973	5.1875	0.7074	2.4627	DGA_lg
		2.4781	1.7936	0.1779	1.0416	0.9808	4.01	0.6127	1.8312	MultiLine
		2.5316	1.8371	0.195	1.0783	0.9851	4.194	0.6277	1.9152	Array
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention	✓ Link	2.5926	1.8981	0.2624	1.0626	0.9558	4.0576	0.6323	1.8162	CRAT-Pred	2022-02-09
		2.6675	1.973	0.2887	1.2052	0.9758	5.7482	0.7508	2.771	huhu
		2.6708	1.9764	0.2508	1.1287	0.9588	4.8418	0.6971	2.1728	El Camino
		2.7237	2.1036	0.3191	1.1624	0.9743	4.6642	0.6879	2.0987	huangmozhi
		2.7277	2.0333	0.3389	1.1394	0.8934	5.7132	0.7861	2.5904	multimodal
		2.7502	2.0558	0.3135	1.2128	0.9623	17.191	0.9878	7.4994	mtz
		2.8102	2.1157	0.3539	1.2216	0.9521	10.0298	0.943	4.9745	CoderTeam
		2.8131	2.1187	0.2845	1.1343	0.9494	4.8259	0.7301	2.1057	timtim
		2.8147	2.2379	0.3553	1.1807	0.9397	5.1581	0.7411	2.2558	msms
		2.8266	2.1322	0.2585	1.1518	0.9875	4.6325	0.6749	2.0414	TNT_20220819
		2.8381	2.1774	0.2611	1.2774	0.9656	5.0578	0.7032	2.3379	Anchor
		2.882	2.1875	0.3264	1.1741	0.9829	3.8707	0.6087	1.7732	mmppgg
		2.9845	2.29	0.3591	1.3988	0.9511	5.9819	0.7824	2.7262	xmy
		2.994	2.2995	0.3579	1.8694	0.4222	4.1008	0.6517	2.3896	lanegcn_pimp_curr_sgd
		2.9967	2.3501	0.3983	1.3502	0.9495	6.0524	0.7756	2.7949	Vi yulia 51 prob
		3.0322	2.3377	0.4048	1.2684	0.9105	10.8683	0.9563	4.7665	AttnLstmMultiMod_fix
		3.0871	2.3926	0.3983	1.4307	0.9197	5.6608	0.7789	2.5448	deleted_accout_because_of_submission_error
		3.0901	2.3957	0.3434	1.4024	0.958	7.0702	0.802	3.0338	hale
		3.1788	2.4845	0.3969	1.4161	0.9865	5.7465	0.7656	2.5888	fs_test
		3.191	2.4966	0.4083	1.2687	0.8726	8.0179	0.904	3.3098	randommm
		3.2021	2.5305	0.3393	1.2522	0.9584	5.5965	0.7471	2.4611	adl
		3.2196	2.5476	0.2904	1.4537	0.991	6.3673	0.7828	2.9576	Anonymous1234
		3.2277	2.4881	0.3857	1.3568	0.947	4.2738	0.6688	1.9276	Social-CVAE
		3.2327	2.5765	0.3276	1.549	0.9876	5.2943	0.718	2.4228	team_moe
		3.2923	3.2923	0.5348	1.5407	0.9931	3.2923	0.5348	1.5407	lt_cont_k6
		3.3584	2.6639	0.4181	1.3836	0.9927	6.5416	0.82	2.9076	Holmes
		3.4092	2.7148	0.5201	1.4519	0.8892	5.999	0.8045	2.6784	bartosz
		3.4799	2.7821	0.4177	1.3998	0.9671	6.5063	0.8097	2.8104	TangBug
		3.5141	2.8196	0.4051	1.4608	0.9567	6.2682	0.7802	2.6885	lixin
		3.5154	3.5154	0.5756	1.5969	0.9835	3.5154	0.5756	1.5969	ussm v2-SAIPS
		3.5654	2.871	0.4141	1.4811	0.9514	6.8301	0.7892	2.9029	llh
		3.7583	3.0638	0.4931	1.6327	0.9139	6.8853	0.8388	2.9838	Aaron Huang
		3.7921	3.0976	0.5428	1.5031	0.9179	4.2923	0.6576	1.9103	ln
		3.7943	3.0999	0.4903	1.6056	0.9718	7.4299	0.8457	3.3035	Tim
		3.8051	3.1107	0.4199	1.8703	0.9437	5.6038	0.7964	2.5911	0626
PRANK: motion Prediction based on RANKing		3.8239	3.8239	0.5955	1.7284	0.9891	3.8239	0.5955	1.7284	PRANK	2020-10-22
		3.8239	3.8239	0.5955	1.7284	0.9891	3.8239	0.5955	1.7284	PRANK
		3.8899	3.1954	0.5203	1.684	0.9408	7.6186	0.8659	3.3858	Speculators
		3.9753	3.2809	0.5128	1.6091	0.9578	4.7064	0.7294	2.232	Fong
		3.9814	3.287	0.5369	1.7129	0.8676	7.8828	0.8715	3.4549	NCTU-heheEECS
		3.9814	3.287	0.5369	1.7129	0.8676	7.8828	0.8715	3.4549	NN(map)
		3.9814	3.287	0.5369	1.7129	0.8676	7.8828	0.8715	3.4549	NN
		3.9861	3.9861	0.6201	1.8062	0.9839	3.9861	0.6201	1.8062	1mode-groupEmbed-106000
		3.9973	3.9973	0.6241	1.809	0.9834	3.9973	0.6241	1.809	1mode-groupEmbed-160000
		4.0092	4.0092	0.6306	1.8179	0.9833	4.0092	0.6306	1.8179	dis102500
		4.0972	4.0972	0.6685	1.8312	0.9738	4.0972	0.6685	1.8312	phuang
		4.0999	3.4055	0.5901	1.6055	0.9183	5.488	0.7352	2.3865	Multiple Trajectories
		4.1094	3.4149	0.5458	155985.7998	0.8332	8.6403	0.8917	10634487.5868	ewta
		4.1159	4.1159	0.6567	1.8525	0.9725	4.1159	0.6567	1.8525	vector-net-latest
		4.1457	3.4512	0.5504	1.7875	0.9124	7.4546	0.8774	3.467	HYU_ACE
		4.1704	4.1704	0.6795	1.8705	0.9759	4.1704	0.6795	1.8705	tjxu
		4.1982	4.1982	0.6868	1.8684	0.97	4.1982	0.6868	1.8684	tmtkinu
		4.1988	3.5044	0.4889	1.888	0.9671	8.1197	0.8355	3.6495	Gilgamesh
		4.255	3.5606	0.5423	1.7691	0.9637	8.3529	0.8705	3.7408	SHM
		4.2643	3.5698	0.5363	1.6939	0.9568	5.4621	0.7171	2.4032	BNet-2S
		4.2673	4.2673	0.6825	1.9123	0.9691	4.2673	0.6825	1.9123	raster_v2
		4.276	3.5816	0.5718	1.8114	0.8688	7.8828	0.8715	3.4549	hell
		4.3633	3.6689	0.6016	1.6557	0.98	3.6689	0.6016	1.6557	cul2
		4.435	3.7406	0.5984	1.6997	0.9845	3.7406	0.5984	1.6997	abac
		4.5196	4.5196	0.6791	2.0031	0.951	4.5196	0.6791	2.0031	cls-token-transfer208k
		4.5214	3.827	0.6142	1.7303	0.9799	3.827	0.6142	1.7303	gcu_v2
		4.5742	3.8798	0.5247	2.3101	0.937	9.7385	0.8574	4.7286	Whatever
		4.6211	4.6211	0.6982	2.0489	0.9506	4.6211	0.6982	2.0489	raster-to-svg-train(t70.5k-train120k)1e-4-2-bs5-39k
		4.6814	3.9867	0.5419	1.9767	0.879	6.7766	0.8388	3.0551	xjh199923
		4.7203	4.0258	0.5821	2.0775	0.964	8.1197	0.8355	3.6495	NCTU-BNN
		4.787	4.0938	0.6582	2.0282	0.946	6.4175	0.7494	2.9337	Alice_Bob
		4.8253	4.1309	0.5156	2.2968	0.9742	11.0515	0.9083	5.5699	wangwentong
		4.9209	4.9209	0.7498	2.1686	0.9263	4.9209	0.7498	2.1686	Challenge
		4.9666	4.2735	0.678	2.0839	0.9485	6.688	0.7601	3.0107	Model Agnostic Meta Learning
		5.0426	4.7166	0.6583	2.1476	0.3525	25.4924	0.737	22.3728	cxl
		5.054	5.054	0.7574	2.1962	0.9268	5.054	0.7574	2.1962	miaomiao
		5.0674	4.6173	0.7119	2.0458	0.916	5.2734	0.7907	2.3053	fs
		5.0682	5.0682	0.7159	2.2719	0.9878	5.0682	0.7159	2.2719	Tang Luqi
		5.0872	4.3927	0.6808	1.9424	0.9518	4.3927	0.6808	1.9424	Extended-Social-LSTM
		5.1247	5.1247	0.7773	2.2181	0.9192	5.1247	0.7773	2.2181	par
		5.1399	4.4455	0.6785	2.2765	0.7834	9.5225	0.9056	4.4349	GRU_CVAE
		5.3363	4.6394	0.6899	2.1564	0.7734	6.3946	0.8151	2.8257	posterior with best parameters
		5.4699	4.7755	0.7205	2.1	0.9384	4.8748	0.7347	2.1416	Xiaogang
		5.4732	5.4732	0.8141	2.418	0.8982	5.4732	0.8141	2.418	que_anr
		5.4818	4.7874	0.6457	2.1201	0.8893	9.4305	0.8902	4.4824	rishabh
		5.6071	4.9126	0.709	2.823	0.832	11.3051	0.9064	5.8841	D0.2
		5.6308	4.9364	0.655	2.5705	0.8878	7.2377	0.8709	3.4152	Ruochen
		5.6682	4.9738	0.7382	2.4191	0.8988	7.0155	0.8884	2.9871	zs
		5.6948	5.0003	0.7392	2.3434	0.9187	5.3231	0.779	2.4611	slf
		5.7168	5.0224	0.7668	2.1875	0.9185	5.0224	0.7668	2.1875	GoodMountain
		5.8185	5.8185	0.806	2.5329	0.9143	5.8185	0.806	2.5329	gg6666
		5.8239	5.8239	0.8087	2.5344	0.9118	5.8239	0.8087	2.5344	baguette
		5.8479	5.1534	0.7648	2.2831	0.9298	5.1534	0.7648	2.2831	LSTMs
		5.9661	5.2717	0.7701	2.2923	0.9176	5.2717	0.7701	2.2923	grip
		6.0565	5.362	0.7805	2.4943	0.8983	5.8451	0.8379	2.6883	lstm+cnn
		6.0771	6.0771	0.8602	2.7271	0.89	6.0771	0.8602	2.7271	zys
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	Funtastic_4casting
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	Constant-V-Forecating(very very naive)
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	DeepPrediction
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	lllsssjQuery33108037804872914465_1678097593292
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	MT-PNC
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	naive method test
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	default
		6.1315	5.437	0.6916	2.3432	0.8977	6.8116	0.8122	2.9631	efficent and adaptive
		6.1454	5.451	0.8117	2.3805	0.9042	5.451	0.8117	2.3805	haomokeji
		6.1954	6.1954	0.8197	2.7291	0.9044	6.1954	0.8197	2.7291	pph
		6.2931	5.5987	0.8237	3.2331	0.2939	46.7268	0.9999	32.9034	Johnny Hsieh
		6.3362	5.6417	0.8261	3.541	0.1475	57.7046	1.0	38.9455	jwlhs104
		6.4158	6.4158	0.8696	2.9498	0.8805	6.4158	0.8696	2.9498	gogogo_zhigang
		6.457	6.457	0.85	2.9719	0.8704	6.457	0.85	2.9719	HH
		6.5202	5.8257	0.8139	2.6003	0.8965	5.8257	0.8139	2.6003	Waldo
		6.7247	6.7247	0.889	3.1088	0.8697	6.7247	0.889	3.1088	vectornet
		6.8531	6.1586	0.8677	2.7385	0.8592	6.1586	0.8677	2.7385	haomo
		6.992	6.992	0.9068	3.2553	0.8654	6.992	0.9068	3.2553	Trial
		7.8875	7.8875	0.8348	3.5333	0.8857	7.8875	0.8348	3.5333	zlewe
		7.8875	7.8875	0.8348	3.5333	0.8857	7.8875	0.8348	3.5333	NCTU_309512033
		7.8875	7.8875	0.8348	3.5333	0.8857	7.8875	0.8348	3.5333
		7.8875	7.8875	0.8348	3.5333	0.8857	7.8875	0.8348	3.5333	baseline_argo_contant_vel_01
		7.8875	7.8875	0.8348	3.5333	0.8857	7.8875	0.8348	3.5333	test_1
		8.0293	8.0293	0.8915	3.9358	0.9033	8.0293	0.8915	3.9358	ffffx
		8.1441	8.1441	0.9238	3.9521	0.7756	8.1441	0.9238	3.9521	BD
		8.2618	7.5673	0.8168	3.3861	0.8722	7.8875	0.8348	3.5333	mmmddd
		8.3792	7.7392	0.8449	3.3726	0.8697	7.7392	0.8449	3.3726	RuSheng
		8.7522	8.7522	0.8868	3.9771	0.8441	8.7522	0.8868	3.9771	Online_CL
		8.8578	8.1634	0.912	3.5646	0.8879	8.1634	0.912	3.5646	yellowtaxi
		9.5077	9.5077	0.9386	4.6564	0.864	9.5077	0.9386	4.6564	suu
		11.4556	11.4556	0.9476	5.7132	0.7064	11.4556	0.9476	5.7132	xjtub09
		12.8813	12.8813	0.9572	7.1746	0.8366	12.8813	0.9572	7.1746	DQS
		23.394	22.6995	0.9817	11.8296	0.755	23.7276	0.9851	12.3146	Constant velocity map prior
		24.3515	23.657	0.9312	12.6584	0.4323	34.3376	0.9936	17.0122	BaseballBro
		26.4057	25.7113	0.9893	28.3813	0.7042	31.1513	0.9967	30.7524	WST

OpenCodePapers

motion-forecasting-on-argoverse-cvpr-2020