Motion Forecasting on Argoverse CVPR 2020

Metric: DAC (K=6) (higher is better)

LeaderboardDataset

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Results

#	Model↕	DAC (K=6)▼	Extra Data	Paper	Date↕	Code
1	lt_cont_k6	0.9931	No	-	-	-
2	Holmes	0.9927	No	-	-	-
3	SEPT	0.9922	No	-	-	-
4	HTTP	0.9922	No	-	-	-
5	R-Pred	0.992	No	R-Pred: Two-Stage Motion Prediction Via Tube-Que...	2022-11-16	-
6	ATTTHOM	0.9913	No	-	-	-
7	TO	0.9912	No	-	-	-
8	argo_test_18_5m_add_rotation_change_data_change_model_3_01	0.9912	No	-	-	-
9	Anonymous1234	0.991	No	-	-	-
10	HIKVISION-ADLab-hz	0.9909	No	-	-	-
11	chl(yiqi)	0.9903	No	-	-	-
12	LaneRCNN (IROS 2021)	0.9903	No	LaneRCNN: Distributed Representations for Graph-...	2021-01-17	-
13	DCMS	0.9902	No	Bootstrap Motion Forecasting With Self-Consisten...	2022-04-12	-
14	Jack-M	0.99	No	-	-	-
15	GANet	0.9899	No	GANet: Goal Area Network for Motion Forecasting	2022-09-20	Code
16	SceneTransformer	0.9899	No	-	-	-
17	Ameame	0.9898	No	-	-	-
18	FGNet_sub	0.9898	No	-	-	-
19	PRIME	0.9898	No	-	-	-
20	VI LaneIter	0.9897	No	-	-	-
21	LAformer	0.9897	No	-	-	-
22	Met	0.9896	No	-	-	-
23	FGNet	0.9895	No	-	-	-
24	ProIn_av1	0.9894	No	-	-	-
25	ProphNet	0.9893	No	-	-	-
26	Wayformer	0.9893	No	Wayformer: Motion Forecasting via Simple & Effic...	2022-07-12	Code
27	TPCN++	0.9893	No	-	-	-
28	DSP	0.9892	No	-	-	-
29	cls_weight1.2-decoder5-use-last	0.9892	No	-	-	-
30	HiVT++	0.9891	No	-	-	Code
31	Shangguan_IFS	0.9891	No	-	-	-
32	PRANK	0.9891	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
33	PRANK	0.9891	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
34	vilab	0.9889	No	-	-	-
35	Habitat-Web	0.9889	No	-	-	-
36	TNT - CoRL20	0.9889	No	TNT: Target-driveN Trajectory Prediction	2020-08-19	Code
37	m2a2_34	0.9888	No	-	-	-
38	HiVT-128	0.9888	No	-	-	Code
39	DIDI	0.9888	No	-	-	-
40	QCNet	0.9887	No	-	-	Code
41	ATDSNet-v2	0.9887	No	-	-	-
42	lstm	0.9887	No	-	-	-
43	Gnet	0.9886	No	-	-	-
44	xia	0.9886	No	-	-	-
45	DTNet(FDE)	0.9886	No	-	-	-
46	decoder-3-last	0.9885	No	-	-	-
47	LTP	0.9884	No	-	-	-
48	Hi_base	0.9884	No	-	-	-
49	TPCN	0.9884	No	TPCN: Temporal Point Cloud Networks for Motion F...	2021-03-04	-
50	PAGA	0.9883	No	-	-	-
51	matrix_mugen	0.9883	No	-	-	-
52	cls-weight1-5	0.9883	No	-	-	-
53	shernan_wu	0.9882	No	-	-	-
54	TSGN	0.9881	No	-	-	-
55	DuanZX	0.9878	No	-	-	-
56	FRM	0.9878	No	Leveraging Future Relationship Reasoning for Veh...	2023-05-24	-
57	Tang Luqi	0.9878	No	-	-	-
58	MFT	0.9877	No	-	-	-
59	ToNet	0.9877	No	-	-	-
60	lagat_mm	0.9877	No	-	-	-
61	multipath++	0.9876	No	MultiPath++: Efficient Information Fusion and Tr...	2021-11-29	Code
62	MR	0.9876	No	-	-	-
63	team_moe	0.9876	No	-	-	-
64	FFINet	0.9875	No	-	-	-
65	minFDE	0.9875	No	-	-	-
66	TNT_20220819	0.9875	No	-	-	-
67	be_s2lossf_3846	0.9874	No	-	-	-
68	EuclidNet-L	0.9874	No	-	-	-
69	vill	0.9873	No	-	-	-
70	ECARX-V1	0.9872	No	-	-	-
71	Lotus	0.9871	No	-	-	-
72	parallel_tl_123_0224	0.987	No	-	-	-
73	Alibaba-ADLab	0.987	No	-	-	-
74	Jean	0.9868	No	-	-	-
75	tp	0.9868	No	-	-	-
76	lt_tv_cont	0.9866	No	-	-	-
77	Holistic Transformer	0.9865	No	Holistic Transformer: A Joint Neural Network for...	2022-06-17	-
78	JMT	0.9865	No	-	-	-
79	fs_test	0.9865	No	-	-	-
80	vectorgcn	0.9864	No	-	-	-
81	MacFormer	0.9863	No	TENET: Transformer Encoding Network for Effectiv...	2022-06-30	-
82	mm_kk_parallel_distemb_91	0.9862	No	-	-	-
83	Dcupqiu	0.9859	No	-	-	-
84	SenseTime_AP	0.9857	No	-	-	-
85	DF-RNN	0.9856	No	-	-	-
86	cxx	0.9854	No	-	-	-
87	Waypoint	0.9852	No	-	-	-
88	Array	0.9851	No	-	-	-
89	leon	0.985	No	-	-	-
90	Miss Rate	0.985	No	-	-	-
91	wo_longterm	0.9849	No	-	-	-
92	AutoBot-Ego	0.9848	No	-	-	-
93	somemethod	0.9848	No	-	-	-
94	lstm_v1	0.9847	No	-	-	-
95	yiqi-fudan	0.9846	No	-	-	-
96	88	0.9846	No	-	-	-
97	abac	0.9845	No	-	-	-
98	SSL-Lanes	0.9844	No	SSL-Lanes: Self-Supervised Learning for Motion F...	2022-06-28	Code
99	CU-aware LaneGCN	0.9843	No	-	-	-
100	Parallel x-transformers version1.1	0.9842	No	-	-	-
101	mmTransformer	0.9842	No	-	-	-
102	zhanzhenxueyuan	0.9842	No	-	-	-
103	brier36	0.984	No	-	-	-
104	Tang Luqi	0.984	No	-	-	-
105	1mode-groupEmbed-106000	0.9839	No	-	-	-
106	whr_test	0.9838	No	-	-	-
107	Model0	0.9838	No	-	-	-
108	FTGN	0.9837	No	Trajectory Forecasting on Temporal Graphs	2022-07-01	Code
109	daiyongjie	0.9836	No	-	-	-
110	YuNi	0.9836	No	-	-	-
111	watson	0.9836	No	-	-	-
112	CY_ng	0.9836	No	-	-	-
113	cbc	0.9836	No	-	-	-
114	julie-1	0.9836	No	-	-	-
115	ywyeh	0.9836	No	-	-	-
116	hitchhiker	0.9836	No	-	-	-
117	alfred	0.9836	No	-	-	-
118	hitljx_test_sub	0.9836	No	-	-	-
119	chailiang	0.9836	No	-	-	-
120	zhousihong	0.9836	No	-	-	-
121	TrajectoryOracle	0.9836	No	-	-	-
122	l_DGA_lg	0.9835	No	-	-	-
123	ussm v2-SAIPS	0.9835	No	-	-	-
124	scale4	0.9834	No	-	-	-
125	mymodel7_actornet_2_mapnet_a2m_a2a_222	0.9834	No	-	-	-
126	1mode-groupEmbed-160000	0.9834	No	-	-	-
127	MSPre	0.9833	No	-	-	-
128	HGO (K=6)	0.9833	No	-	-	-
129	dis102500	0.9833	No	-	-	-
130	numberEight	0.9831	No	-	-	-
131	fyyyy	0.9831	No	-	-	-
132	HOME + GOHOME	0.983	No	HOME: Heatmap Output for future Motion Estimation	2021-05-23	Code
133	rush	0.983	No	-	-	-
134	prediction-liangdian	0.9829	No	-	-	-
135	mmppgg	0.9829	No	-	-	-
136	vi_avp	0.9827	No	-	-	-
137	sihong	0.9826	No	-	-	-
138	CMAN(av1_demo)	0.9826	No	-	-	-
139	JAL-MTP	0.9825	No	-	-	-
140	leige	0.9825	No	-	-	-
141	lanegcn_12_epoch	0.9824	No	-	-	-
142	Transformer1	0.982	No	-	-	-
143	15 train 40e	0.9818	No	-	-	-
144	ORIGINAL	0.9818	No	-	-	-
145	Tang Luqi	0.9818	No	-	-	-
146	just_a_test	0.9817	No	-	-	-
147	SCP	0.9816	No	-	-	-
148	pred	0.9816	No	-	-	-
149	idlaber-Hans	0.9815	No	-	-	-
150	MTN	0.9815	No	-	-	-
151	wimp	0.9815	No	-	-	-
152	tnt-mtp_100s_targetloss_1-1_6	0.9815	No	-	-	-
153	LaneGCN-trainset-36	0.9814	No	-	-	-
154	Europa	0.9814	No	-	-	-
155	player	0.9814	No	-	-	-
156	lengyue	0.9812	No	-	-	-
157	LaneGCN	0.9812	No	-	-	-
158	chant	0.9812	No	-	-	-
159	SCP	0.9812	No	-	-	-
160	GOHOME	0.9811	No	GOHOME: Graph-Oriented Heatmap Output for future...	2021-09-04	-
161	s1	0.981	No	-	-	-
162	TestForecast	0.9808	No	-	-	-
163	MultiLine	0.9808	No	-	-	-
164	(VI)	0.9807	No	-	-	-
165	TPA+Laneloss	0.9807	No	-	-	-
166	huyuening	0.9805	No	-	-	-
167	ISY@TK	0.9805	No	-	-	-
168	LGN	0.98	No	-	-	-
169	damplyv1	0.98	No	-	-	-
170	cul2	0.98	No	-	-	-
171	sample_replace_gt_smalln_82	0.9799	No	-	-	-
172	gcu_v2	0.9799	No	-	-	-
173	SCM+Distill(best model)	0.9797	No	-	-	-
174	William	0.9793	No	-	-	-
175	mt_navi	0.979	No	-	-	-
176	THOMAS	0.9781	No	THOMAS: Trajectory Heatmap Output with learned M...	2021-10-13	-
177	LaneGCN-s	0.9776	No	-	-	-
178	NCTU-GPL-GPAL	0.9776	No	-	-	-
179	huangmozhi9527	0.9769	No	-	-	-
180	fyyclass	0.9762	No	-	-	-
181	6 mode model	0.9762	No	-	-	-
182	tjxu	0.9759	No	-	-	-
183	huhu	0.9758	No	-	-	-
184	UAR	0.9756	No	-	-	-
185	ulimit	0.9756	No	-	-	-
186	huangmozhi	0.9743	No	-	-	-
187	wangwentong	0.9742	No	-	-	-
188	phuang	0.9738	No	-	-	-
189	DGA_lg	0.973	No	-	-	-
190	uulm-mrm	0.9725	No	-	-	-
191	vector-net-latest	0.9725	No	-	-	-
192	Tim	0.9718	No	-	-	-
193	tmtkinu	0.97	No	-	-	-
194	Map Static+Specific	0.9699	No	-	-	-
195	raster_v2	0.9691	No	-	-	-
196	TangBug	0.9671	No	-	-	-
197	Gilgamesh	0.9671	No	-	-	-
198	Anchor	0.9656	No	-	-	-
199	NCTU-BNN	0.964	No	-	-	-
200	SHM	0.9637	No	-	-	-
201	mtz	0.9623	No	-	-	-
202	xjh	0.9602	No	-	-	-
203	El Camino	0.9588	No	-	-	-
204	adl	0.9584	No	-	-	-
205	hale	0.958	No	-	-	-
206	Fong	0.9578	No	-	-	-
207	BNet-2S	0.9568	No	-	-	-
208	lixin	0.9567	No	-	-	-
209	arky	0.9563	No	-	-	-
210	CRAT-Pred	0.9558	No	CRAT-Pred: Vehicle Trajectory Prediction with Cr...	2022-02-09	Code
211	CoderTeam	0.9521	No	-	-	-
212	Extended-Social-LSTM	0.9518	No	-	-	-
213	llh	0.9514	No	-	-	-
214	xmy	0.9511	No	-	-	-
215	cls-token-transfer208k	0.951	No	-	-	-
216	raster-to-svg-train(t70.5k-train120k)1e-4-2-bs5-39k	0.9506	No	-	-	-
217	Vi yulia 51 prob	0.9495	No	-	-	-
218	timtim	0.9494	No	-	-	-
219	Model Agnostic Meta Learning	0.9485	No	-	-	-
220	Social-CVAE	0.947	No	-	-	-
221	Alice_Bob	0.946	No	-	-	-
222	0626	0.9437	No	-	-	-
223	Speculators	0.9408	No	-	-	-
224	msms	0.9397	No	-	-	-
225	Xiaogang	0.9384	No	-	-	-
226	Whatever	0.937	No	-	-	-
227	LSTMs	0.9298	No	-	-	-
228	miaomiao	0.9268	No	-	-	-
229	Challenge	0.9263	No	-	-	-
230	deleted_accout_because_of_submission_error	0.9197	No	-	-	-
231	par	0.9192	No	-	-	-
232	slf	0.9187	No	-	-	-
233	GoodMountain	0.9185	No	-	-	-
234	Multiple Trajectories	0.9183	No	-	-	-
235	ln	0.9179	No	-	-	-
236	grip	0.9176	No	-	-	-
237	fs	0.916	No	-	-	-
238	gg6666	0.9143	No	-	-	-
239	Aaron Huang	0.9139	No	-	-	-
240	HYU_ACE	0.9124	No	-	-	-
241	baguette	0.9118	No	-	-	-
242	AttnLstmMultiMod_fix	0.9105	No	-	-	-
243	Lane_vae	0.9092	No	-	-	-
244	pph	0.9044	No	-	-	-
245	haomokeji	0.9042	No	-	-	-
246	ffffx	0.9033	No	-	-	-
247	zs	0.8988	No	-	-	-
248	lstm+cnn	0.8983	No	-	-	-
249	que_anr	0.8982	No	-	-	-
250	Funtastic_4casting	0.8977	No	-	-	-
251	Constant-V-Forecating(very very naive)	0.8977	No	-	-	-
252	DeepPrediction	0.8977	No	-	-	-
253	lllsssjQuery33108037804872914465_1678097593292	0.8977	No	-	-	-
254	MT-PNC	0.8977	No	-	-	-
255	naive method test	0.8977	No	-	-	-
256	default	0.8977	No	-	-	-
257	efficent and adaptive	0.8977	No	-	-	-
258	Waldo	0.8965	No	-	-	-
259	multimodal	0.8934	No	-	-	-
260	zys	0.89	No	-	-	-
261	rishabh	0.8893	No	-	-	-
262	bartosz	0.8892	No	-	-	-
263	yellowtaxi	0.8879	No	-	-	-
264	Ruochen	0.8878	No	-	-	-
265	zlewe	0.8857	No	-	-	-
266	NCTU_309512033	0.8857	No	-	-	-
267	null	0.8857	No	-	-	-
268	baseline_argo_contant_vel_01	0.8857	No	-	-	-
269	test_1	0.8857	No	-	-	-
270	gogogo_zhigang	0.8805	No	-	-	-
271	xjh199923	0.879	No	-	-	-
272	randommm	0.8726	No	-	-	-
273	mmmddd	0.8722	No	-	-	-
274	HH	0.8704	No	-	-	-
275	vectornet	0.8697	No	-	-	-
276	RuSheng	0.8697	No	-	-	-
277	hell	0.8688	No	-	-	-
278	NCTU-heheEECS	0.8676	No	-	-	-
279	NN(map)	0.8676	No	-	-	-
280	NN	0.8676	No	-	-	-
281	Trial	0.8654	No	-	-	-
282	suu	0.864	No	-	-	-
283	haomo	0.8592	No	-	-	-
284	Online_CL	0.8441	No	-	-	-
285	DQS	0.8366	No	-	-	-
286	ewta	0.8332	No	-	-	-
287	D0.2	0.832	No	-	-	-
288	GRU_CVAE	0.7834	No	-	-	-
289	BD	0.7756	No	-	-	-
290	posterior with best parameters	0.7734	No	-	-	-
291	Constant velocity map prior	0.755	No	-	-	-
292	xjtub09	0.7064	No	-	-	-
293	WST	0.7042	No	-	-	-
294	BaseballBro	0.4323	No	-	-	-
295	lanegcn_pimp_curr_sgd	0.4222	No	-	-	-
296	cxl	0.3525	No	-	-	-
297	Johnny Hsieh	0.2939	No	-	-	-
298	tusiji12138	0.1816	No	-	-	-
299	jwlhs104	0.1475	No	-	-	-

Model↕

DAC (K=6)▼

Extra Data

Paper

Date↕

Code

lt_cont_k6

0.9931

Holmes

0.9927

SEPT

0.9922

HTTP

0.9922

R-Pred

0.992

R-Pred: Two-Stage Motion Prediction Via Tube-Que...

2022-11-16

ATTTHOM

0.9913

0.9912

argo_test_18_5m_add_rotation_change_data_change_model_3_01

0.9912

Anonymous1234

0.991

HIKVISION-ADLab-hz

0.9909

chl(yiqi)

0.9903

LaneRCNN (IROS 2021)

0.9903

LaneRCNN: Distributed Representations for Graph-...

2021-01-17

DCMS

0.9902

Bootstrap Motion Forecasting With Self-Consisten...

2022-04-12

Jack-M

0.99

GANet

0.9899

GANet: Goal Area Network for Motion Forecasting

2022-09-20

Code

SceneTransformer

0.9899

Ameame

0.9898

FGNet_sub

0.9898

PRIME

0.9898

VI LaneIter

0.9897

LAformer

0.9897

Met

0.9896

FGNet

0.9895

ProIn_av1

0.9894

ProphNet

0.9893

Wayformer

0.9893

Wayformer: Motion Forecasting via Simple & Effic...

2022-07-12

Code

TPCN++

0.9893

DSP

0.9892

cls_weight1.2-decoder5-use-last

0.9892

HiVT++

0.9891

Code

Shangguan_IFS

0.9891

PRANK

0.9891

PRANK: motion Prediction based on RANKing

2020-10-22

PRANK

0.9891

PRANK: motion Prediction based on RANKing

2020-10-22

vilab

0.9889

Habitat-Web

0.9889

TNT - CoRL20

0.9889

TNT: Target-driveN Trajectory Prediction

2020-08-19

Code

m2a2_34

0.9888

HiVT-128

0.9888

Code

DIDI

0.9888

QCNet

0.9887

Code

ATDSNet-v2

0.9887

lstm

0.9887

Gnet

0.9886

xia

0.9886

DTNet(FDE)

0.9886

decoder-3-last

0.9885

LTP

0.9884

Hi_base

0.9884

TPCN

0.9884

TPCN: Temporal Point Cloud Networks for Motion F...

2021-03-04

PAGA

0.9883

matrix_mugen

0.9883

cls-weight1-5

0.9883

shernan_wu

0.9882

TSGN

0.9881

DuanZX

0.9878

FRM

0.9878

Leveraging Future Relationship Reasoning for Veh...

2023-05-24

Tang Luqi

0.9878

MFT

0.9877

ToNet

0.9877

lagat_mm

0.9877

multipath++

0.9876

MultiPath++: Efficient Information Fusion and Tr...

2021-11-29

Code

0.9876

team_moe

0.9876

FFINet

0.9875

minFDE

0.9875

TNT_20220819

0.9875

be_s2lossf_3846

0.9874

EuclidNet-L

0.9874

vill

0.9873

ECARX-V1

0.9872

Lotus

0.9871

parallel_tl_123_0224

0.987

Alibaba-ADLab

0.987

Jean

0.9868

lt_tv_cont

0.9866

Holistic Transformer

0.9865

Holistic Transformer: A Joint Neural Network for...

2022-06-17

JMT

0.9865

fs_test

0.9865

vectorgcn

0.9864

MacFormer

0.9863

TENET: Transformer Encoding Network for Effectiv...

2022-06-30

mm_kk_parallel_distemb_91

0.9862

Dcupqiu

0.9859

SenseTime_AP

0.9857

DF-RNN

0.9856

cxx

0.9854

Waypoint

0.9852

Array

0.9851

leon

0.985

Miss Rate

0.985

wo_longterm

0.9849

AutoBot-Ego

0.9848

somemethod

0.9848

lstm_v1

0.9847

yiqi-fudan

0.9846

abac

0.9845

SSL-Lanes

0.9844

SSL-Lanes: Self-Supervised Learning for Motion F...

2022-06-28

Code

CU-aware LaneGCN

0.9843

100

Parallel x-transformers version1.1

0.9842

101

mmTransformer

0.9842

102

zhanzhenxueyuan

0.9842

103

brier36

0.984

104

Tang Luqi

0.984

105

1mode-groupEmbed-106000

0.9839

106

whr_test

0.9838

107

Model0

0.9838

108

FTGN

0.9837

Trajectory Forecasting on Temporal Graphs

2022-07-01

Code

109

daiyongjie

0.9836

110

YuNi

0.9836

111

watson

0.9836

112

CY_ng

0.9836

113

cbc

0.9836

114

julie-1

0.9836

115

ywyeh

0.9836

116

hitchhiker

0.9836

117

alfred

0.9836

118

hitljx_test_sub

0.9836

119

chailiang

0.9836

120

zhousihong

0.9836

121

TrajectoryOracle

0.9836

122

l_DGA_lg

0.9835

123

ussm v2-SAIPS

0.9835

124

scale4

0.9834

125

mymodel7_actornet_2_mapnet_a2m_a2a_222

0.9834

126

1mode-groupEmbed-160000

0.9834

127

MSPre

0.9833

128

HGO (K=6)

0.9833

129

dis102500

0.9833

130

numberEight

0.9831

131

fyyyy

0.9831

132

HOME + GOHOME

0.983

HOME: Heatmap Output for future Motion Estimation

2021-05-23

Code

133

rush

0.983

134

prediction-liangdian

0.9829

135

mmppgg

0.9829

136

vi_avp

0.9827

137

sihong

0.9826

138

CMAN(av1_demo)

0.9826

139

JAL-MTP

0.9825

140

leige

0.9825

141

lanegcn_12_epoch

0.9824

142

Transformer1

0.982

143

15 train 40e

0.9818

144

ORIGINAL

0.9818

145

Tang Luqi

0.9818

146

just_a_test

0.9817

147

SCP

0.9816

148

pred

0.9816

149

idlaber-Hans

0.9815

150

MTN

0.9815

151

wimp

0.9815

152

tnt-mtp_100s_targetloss_1-1_6

0.9815

153

LaneGCN-trainset-36

0.9814

154

Europa

0.9814

155

player

0.9814

156

lengyue

0.9812

157

LaneGCN

0.9812

158

chant

0.9812

159

SCP

0.9812

160

GOHOME

0.9811

GOHOME: Graph-Oriented Heatmap Output for future...

2021-09-04

161

0.981

162

TestForecast

0.9808

163

MultiLine

0.9808

164

(VI)

0.9807

165

TPA+Laneloss

0.9807

166

huyuening

0.9805

167

ISY@TK

0.9805

168

LGN

0.98

169

damplyv1

0.98

170

cul2

0.98

171

sample_replace_gt_smalln_82

0.9799

172

gcu_v2

0.9799

173

SCM+Distill(best model)

0.9797

174

William

0.9793

175

mt_navi

0.979

176

THOMAS

0.9781

THOMAS: Trajectory Heatmap Output with learned M...

2021-10-13

177

LaneGCN-s

0.9776

178

NCTU-GPL-GPAL

0.9776

179

huangmozhi9527

0.9769

180

fyyclass

0.9762

181

6 mode model

0.9762

182

tjxu

0.9759

183

huhu

0.9758

184

UAR

0.9756

185

ulimit

0.9756

186

huangmozhi

0.9743

187

wangwentong

0.9742

188

phuang

0.9738

189

DGA_lg

0.973

190

uulm-mrm

0.9725

191

vector-net-latest

0.9725

192

Tim

0.9718

193

tmtkinu

0.97

194

Map Static+Specific

0.9699

195

raster_v2

0.9691

196

TangBug

0.9671

197

Gilgamesh

0.9671

198

Anchor

0.9656

199

NCTU-BNN

0.964

200

SHM

0.9637

201

mtz

0.9623

202

xjh

0.9602

203

El Camino

0.9588

204

adl

0.9584

205

hale

0.958

206

Fong

0.9578

207

BNet-2S

0.9568

208

lixin

0.9567

209

arky

0.9563

210

CRAT-Pred

0.9558

CRAT-Pred: Vehicle Trajectory Prediction with Cr...

2022-02-09

Code

211

CoderTeam

0.9521

212

Extended-Social-LSTM

0.9518

213

llh

0.9514

214

xmy

0.9511

215

cls-token-transfer208k

0.951

216

raster-to-svg-train(t70.5k-train120k)1e-4-2-bs5-39k

0.9506

217

Vi yulia 51 prob

0.9495

218

timtim

0.9494

219

Model Agnostic Meta Learning

0.9485

220

Social-CVAE

0.947

221

Alice_Bob

0.946

222

0626

0.9437

223

Speculators

0.9408

224

msms

0.9397

225

Xiaogang

0.9384

226

Whatever

0.937

227

LSTMs

0.9298

228

miaomiao

0.9268

229

Challenge

0.9263

230

deleted_accout_because_of_submission_error

0.9197

231

par

0.9192

232

slf

0.9187

233

GoodMountain

0.9185

234

Multiple Trajectories

0.9183

235

0.9179

236

grip

0.9176

237

0.916

238

gg6666

0.9143

239

Aaron Huang

0.9139

240

HYU_ACE

0.9124

241

baguette

0.9118

242

AttnLstmMultiMod_fix

0.9105

243

Lane_vae

0.9092

244

pph

0.9044

245

haomokeji

0.9042

246

ffffx

0.9033

247

0.8988

248

lstm+cnn

0.8983

249

que_anr

0.8982

250

Funtastic_4casting

0.8977

251

Constant-V-Forecating(very very naive)

0.8977

252

DeepPrediction

0.8977

253

lllsssjQuery33108037804872914465_1678097593292

0.8977

254

MT-PNC

0.8977

255

naive method test

0.8977

256

default

0.8977

257

efficent and adaptive

0.8977

258

Waldo

0.8965

259

multimodal

0.8934

260

zys

0.89

261

rishabh

0.8893

262

bartosz

0.8892

263

yellowtaxi

0.8879

264

Ruochen

0.8878

265

zlewe

0.8857

266

NCTU_309512033

0.8857

267

null

0.8857

268

baseline_argo_contant_vel_01

0.8857

269

test_1

0.8857

270

gogogo_zhigang

0.8805

271

xjh199923

0.879

272

randommm

0.8726

273

mmmddd

0.8722

274

0.8704

275

vectornet

0.8697

276

RuSheng

0.8697

277

hell

0.8688

278

NCTU-heheEECS

0.8676

279

NN(map)

0.8676

280

0.8676

281

Trial

0.8654

282

suu

0.864

283

haomo

0.8592

284

Online_CL

0.8441

285

DQS

0.8366

286

ewta

0.8332

287

D0.2

0.832

288

GRU_CVAE

0.7834

289

0.7756

290

posterior with best parameters

0.7734

291

Constant velocity map prior

0.755

292

xjtub09

0.7064

293

WST

0.7042

294

BaseballBro

0.4323

295

lanegcn_pimp_curr_sgd

0.4222

296

cxl

0.3525

297

Johnny Hsieh

0.2939

298

tusiji12138

0.1816

299

jwlhs104

0.1475