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	null
[]()		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