Autonomous Driving on Argoverse CVPR 2020

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

LeaderboardDataset

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Results

#	Model↕	MR (K=6)▼	Extra Data	Paper	Date↕	Code
1	WST	0.9893	No	-	-	-
2	Constant velocity map prior	0.9817	No	-	-	-
3	DQS	0.9572	No	-	-	-
4	xjtub09	0.9476	No	-	-	-
5	suu	0.9386	No	-	-	-
6	BaseballBro	0.9312	No	-	-	-
7	BD	0.9238	No	-	-	-
8	yellowtaxi	0.912	No	-	-	-
9	Trial	0.9068	No	-	-	-
10	ffffx	0.8915	No	-	-	-
11	vectornet	0.889	No	-	-	-
12	Online_CL	0.8868	No	-	-	-
13	gogogo_zhigang	0.8696	No	-	-	-
14	haomo	0.8677	No	-	-	-
15	zys	0.8602	No	-	-	-
16	HH	0.85	No	-	-	-
17	RuSheng	0.8449	No	-	-	-
18	zlewe	0.8348	No	-	-	-
19	NCTU_309512033	0.8348	No	-	-	-
20	null	0.8348	No	-	-	-
21	baseline_argo_contant_vel_01	0.8348	No	-	-	-
22	test_1	0.8348	No	-	-	-
23	jwlhs104	0.8261	No	-	-	-
24	Johnny Hsieh	0.8237	No	-	-	-
25	pph	0.8197	No	-	-	-
26	mmmddd	0.8168	No	-	-	-
27	que_anr	0.8141	No	-	-	-
28	Waldo	0.8139	No	-	-	-
29	haomokeji	0.8117	No	-	-	-
30	baguette	0.8087	No	-	-	-
31	gg6666	0.806	No	-	-	-
32	lstm+cnn	0.7805	No	-	-	-
33	par	0.7773	No	-	-	-
34	grip	0.7701	No	-	-	-
35	GoodMountain	0.7668	No	-	-	-
36	LSTMs	0.7648	No	-	-	-
37	miaomiao	0.7574	No	-	-	-
38	Challenge	0.7498	No	-	-	-
39	slf	0.7392	No	-	-	-
40	zs	0.7382	No	-	-	-
41	Xiaogang	0.7205	No	-	-	-
42	Tang Luqi	0.7159	No	-	-	-
43	fs	0.7119	No	-	-	-
44	D0.2	0.709	No	-	-	-
45	raster-to-svg-train(t70.5k-train120k)1e-4-2-bs5-39k	0.6982	No	-	-	-
46	Funtastic_4casting	0.6916	No	-	-	-
47	Constant-V-Forecating(very very naive)	0.6916	No	-	-	-
48	DeepPrediction	0.6916	No	-	-	-
49	lllsssjQuery33108037804872914465_1678097593292	0.6916	No	-	-	-
50	MT-PNC	0.6916	No	-	-	-
51	naive method test	0.6916	No	-	-	-
52	default	0.6916	No	-	-	-
53	efficent and adaptive	0.6916	No	-	-	-
54	posterior with best parameters	0.6899	No	-	-	-
55	tmtkinu	0.6868	No	-	-	-
56	raster_v2	0.6825	No	-	-	-
57	Extended-Social-LSTM	0.6808	No	-	-	-
58	tjxu	0.6795	No	-	-	-
59	cls-token-transfer208k	0.6791	No	-	-	-
60	GRU_CVAE	0.6785	No	-	-	-
61	Model Agnostic Meta Learning	0.678	No	-	-	-
62	phuang	0.6685	No	-	-	-
63	cxl	0.6583	No	-	-	-
64	Alice_Bob	0.6582	No	-	-	-
65	vector-net-latest	0.6567	No	-	-	-
66	Ruochen	0.655	No	-	-	-
67	rishabh	0.6457	No	-	-	-
68	dis102500	0.6306	No	-	-	-
69	1mode-groupEmbed-160000	0.6241	No	-	-	-
70	1mode-groupEmbed-106000	0.6201	No	-	-	-
71	gcu_v2	0.6142	No	-	-	-
72	cul2	0.6016	No	-	-	-
73	abac	0.5984	No	-	-	-
74	PRANK	0.5955	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
75	PRANK	0.5955	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
76	Multiple Trajectories	0.5901	No	-	-	-
77	NCTU-BNN	0.5821	No	-	-	-
78	ussm v2-SAIPS	0.5756	No	-	-	-
79	hell	0.5718	No	-	-	-
80	HYU_ACE	0.5504	No	-	-	-
81	ewta	0.5458	No	-	-	-
82	ln	0.5428	No	-	-	-
83	SHM	0.5423	No	-	-	-
84	xjh199923	0.5419	No	-	-	-
85	NCTU-heheEECS	0.5369	No	-	-	-
86	NN(map)	0.5369	No	-	-	-
87	NN	0.5369	No	-	-	-
88	BNet-2S	0.5363	No	-	-	-
89	lt_cont_k6	0.5348	No	-	-	-
90	Whatever	0.5247	No	-	-	-
91	Speculators	0.5203	No	-	-	-
92	bartosz	0.5201	No	-	-	-
93	wangwentong	0.5156	No	-	-	-
94	Fong	0.5128	No	-	-	-
95	Aaron Huang	0.4931	No	-	-	-
96	Tim	0.4903	No	-	-	-
97	Gilgamesh	0.4889	No	-	-	-
98	0626	0.4199	No	-	-	-
99	Holmes	0.4181	No	-	-	-
100	TangBug	0.4177	No	-	-	-
101	llh	0.4141	No	-	-	-
102	randommm	0.4083	No	-	-	-
103	lixin	0.4051	No	-	-	-
104	AttnLstmMultiMod_fix	0.4048	No	-	-	-
105	Vi yulia 51 prob	0.3983	No	-	-	-
106	deleted_accout_because_of_submission_error	0.3983	No	-	-	-
107	fs_test	0.3969	No	-	-	-
108	Social-CVAE	0.3857	No	-	-	-
109	xmy	0.3591	No	-	-	-
110	lanegcn_pimp_curr_sgd	0.3579	No	-	-	-
111	msms	0.3553	No	-	-	-
112	CoderTeam	0.3539	No	-	-	-
113	hale	0.3434	No	-	-	-
114	adl	0.3393	No	-	-	-
115	multimodal	0.3389	No	-	-	-
116	team_moe	0.3276	No	-	-	-
117	mmppgg	0.3264	No	-	-	-
118	huangmozhi	0.3191	No	-	-	-
119	mtz	0.3135	No	-	-	-
120	Anonymous1234	0.2904	No	-	-	-
121	huhu	0.2887	No	-	-	-
122	timtim	0.2845	No	-	-	-
123	CRAT-Pred	0.2624	No	CRAT-Pred: Vehicle Trajectory Prediction with Cr...	2022-02-09	Code
124	Anchor	0.2611	No	-	-	-
125	TNT_20220819	0.2585	No	-	-	-
126	El Camino	0.2508	No	-	-	-
127	DGA_lg	0.2471	No	-	-	-
128	damplyv1	0.2333	No	-	-	-
129	tp	0.2317	No	-	-	-
130	fyyclass	0.2208	No	-	-	-
131	Map Static+Specific	0.2189	No	-	-	-
132	6 mode model	0.2186	No	-	-	-
133	uulm-mrm	0.2179	No	-	-	-
134	xjh	0.214	No	-	-	-
135	huangmozhi9527	0.2129	No	-	-	-
136	arky	0.2096	No	-	-	-
137	argo_test_18_5m_add_rotation_change_data_change_model_3_01	0.205	No	-	-	-
138	NCTU-GPL-GPAL	0.1951	No	-	-	-
139	Array	0.195	No	-	-	-
140	SCP	0.1937	No	-	-	-
141	tnt-mtp_100s_targetloss_1-1_6	0.1891	No	-	-	-
142	mt_navi	0.1884	No	-	-	-
143	idlaber-Hans	0.1865	No	-	-	-
144	rush	0.1864	No	-	-	-
145	UAR	0.1858	No	-	-	-
146	CMAN(av1_demo)	0.1849	No	-	-	-
147	TrajectoryOracle	0.1823	No	-	-	-
148	zhanzhenxueyuan	0.1823	No	-	-	-
149	SCP	0.1804	No	-	-	-
150	player	0.1795	No	-	-	-
151	chant	0.1791	No	-	-	-
152	ulimit	0.1779	No	-	-	-
153	MultiLine	0.1779	No	-	-	-
154	sample_replace_gt_smalln_82	0.1764	No	-	-	-
155	LaneGCN-s	0.1738	No	-	-	-
156	SCM+Distill(best model)	0.1735	No	-	-	-
157	leige	0.1732	No	-	-	-
158	s1	0.1725	No	-	-	-
159	lagat_mm	0.1707	No	-	-	-
160	HGO (K=6)	0.1699	No	-	-	-
161	ORIGINAL	0.1699	No	-	-	-
162	TPA+Laneloss	0.1696	No	-	-	-
163	cxx	0.1692	No	-	-	-
164	huyuening	0.1689	No	-	-	-
165	somemethod	0.1677	No	-	-	-
166	lstm	0.1676	No	-	-	-
167	Europa	0.1674	No	-	-	-
168	fyyyy	0.1674	No	-	-	-
169	15 train 40e	0.1673	No	-	-	-
170	wimp	0.1669	No	-	-	-
171	lanegcn_12_epoch	0.1666	No	-	-	-
172	ISY@TK	0.1666	No	-	-	-
173	William	0.1657	No	-	-	-
174	Habitat-Web	0.1656	No	-	-	-
175	TNT - CoRL20	0.1656	No	TNT: Target-driveN Trajectory Prediction	2020-08-19	Code
176	Transformer1	0.1652	No	-	-	-
177	just_a_test	0.1651	No	-	-	-
178	lengyue	0.1647	No	-	-	-
179	LaneGCN-trainset-36	0.1638	No	-	-	-
180	AutoBot-Ego	0.1635	No	-	-	-
181	LGN	0.1634	No	-	-	-
182	mymodel7_actornet_2_mapnet_a2m_a2a_222	0.1623	No	-	-	-
183	leon	0.1622	No	-	-	-
184	LaneGCN	0.162	No	-	-	-
185	sihong	0.1619	No	-	-	-
186	Model0	0.1618	No	-	-	-
187	Alibaba-ADLab	0.1615	No	-	-	-
188	(VI)	0.1611	No	-	-	-
189	scale4	0.1606	No	-	-	-
190	JAL-MTP	0.1603	No	-	-	-
191	YuNi	0.1597	No	-	-	-
192	watson	0.1597	No	-	-	-
193	CY_ng	0.1597	No	-	-	-
194	cbc	0.1597	No	-	-	-
195	julie-1	0.1597	No	-	-	-
196	ywyeh	0.1597	No	-	-	-
197	hitchhiker	0.1597	No	-	-	-
198	alfred	0.1597	No	-	-	-
199	hitljx_test_sub	0.1597	No	-	-	-
200	chailiang	0.1597	No	-	-	-
201	zhousihong	0.1597	No	-	-	-
202	MTN	0.1592	No	-	-	-
203	wo_longterm	0.1591	No	-	-	-
204	vi_avp	0.1591	No	-	-	-
205	JMT	0.1589	No	-	-	-
206	DF-RNN	0.1589	No	-	-	-
207	l_DGA_lg	0.1586	No	-	-	-
208	daiyongjie	0.158	No	-	-	-
209	brier36	0.1576	No	-	-	-
210	MSPre	0.154	No	-	-	-
211	mmTransformer	0.154	No	-	-	-
212	MR	0.1539	No	-	-	-
213	FTGN	0.1528	No	Trajectory Forecasting on Temporal Graphs	2022-07-01	Code
214	Parallel x-transformers version1.1	0.1522	No	-	-	-
215	88	0.151	No	-	-	-
216	Lane_vae	0.1501	No	-	-	-
217	vilab	0.148	No	-	-	-
218	CU-aware LaneGCN	0.1474	No	-	-	-
219	LTP	0.1472	No	-	-	-
220	prediction-liangdian	0.1471	No	-	-	-
221	Shangguan_IFS	0.146	No	-	-	-
222	whr_test	0.1438	No	-	-	-
223	vill	0.1437	No	-	-	-
224	Lotus	0.1434	No	-	-	-
225	MFT	0.1432	No	-	-	-
226	FRM	0.143	No	Leveraging Future Relationship Reasoning for Veh...	2023-05-24	-
227	Waypoint	0.1429	No	-	-	-
228	lt_tv_cont	0.1427	No	-	-	-
229	ECARX-V1	0.1425	No	-	-	-
230	HTTP	0.142	No	-	-	-
231	ToNet	0.1418	No	-	-	-
232	Dcupqiu	0.1416	No	-	-	-
233	vectorgcn	0.1408	No	-	-	-
234	TestForecast	0.1397	No	-	-	-
235	lstm_v1	0.1395	No	-	-	-
236	ATDSNet-v2	0.1383	No	-	-	-
237	yiqi-fudan	0.1376	No	-	-	-
238	m2a2_34	0.1374	No	-	-	-
239	EuclidNet-L	0.1362	No	-	-	-
240	cls-weight1-5	0.136	No	-	-	-
241	Hi_base	0.1354	No	-	-	-
242	mm_kk_parallel_distemb_91	0.1352	No	-	-	-
243	xia	0.1351	No	-	-	-
244	parallel_tl_123_0224	0.1349	No	-	-	-
245	FGNet_sub	0.1341	No	-	-	-
246	TPCN	0.1333	No	TPCN: Temporal Point Cloud Networks for Motion F...	2021-03-04	-
247	decoder-3-last	0.1331	No	-	-	-
248	DIDI	0.1327	No	-	-	-
249	SSL-Lanes	0.1326	No	SSL-Lanes: Self-Supervised Learning for Motion F...	2022-06-28	Code
250	multipath++	0.1324	No	MultiPath++: Efficient Information Fusion and Tr...	2021-11-29	Code
251	cls_weight1.2-decoder5-use-last	0.1324	No	-	-	-
252	Jean	0.1308	No	-	-	-
253	be_s2lossf_3846	0.1306	No	-	-	-
254	DSP	0.1303	No	-	-	-
255	Holistic Transformer	0.1303	No	Holistic Transformer: A Joint Neural Network for...	2022-06-17	-
256	matrix_mugen	0.1297	No	-	-	-
257	FGNet	0.129	No	-	-	-
258	DTNet(FDE)	0.1282	No	-	-	-
259	MacFormer	0.1272	No	TENET: Transformer Encoding Network for Effectiv...	2022-06-30	-
260	HiVT-128	0.1267	No	-	-	Code
261	Jack-M	0.1266	No	-	-	-
262	minFDE	0.1258	No	-	-	-
263	SceneTransformer	0.1255	No	-	-	-
264	LAformer	0.1245	No	-	-	-
265	LaneRCNN (IROS 2021)	0.1232	No	LaneRCNN: Distributed Representations for Graph-...	2021-01-17	-
266	TSGN	0.1223	No	-	-	-
267	shernan_wu	0.1223	No	-	-	-
268	HiVT++	0.1221	No	-	-	Code
269	pred	0.1217	No	-	-	-
270	chl(yiqi)	0.121	No	-	-	-
271	Met	0.1209	No	-	-	-
272	HIKVISION-ADLab-hz	0.1209	No	-	-	-
273	ATTTHOM	0.1207	No	-	-	-
274	Ameame	0.1207	No	-	-	-
275	SenseTime_AP	0.1203	No	-	-	-
276	Wayformer	0.1186	No	Wayformer: Motion Forecasting via Simple & Effic...	2022-07-12	Code
277	GANet	0.1179	No	GANet: Goal Area Network for Motion Forecasting	2022-09-20	Code
278	ProIn_av1	0.1177	No	-	-	-
279	Gnet	0.1168	No	-	-	-
280	R-Pred	0.1165	No	R-Pred: Two-Stage Motion Prediction Via Tube-Que...	2022-11-16	-
281	TPCN++	0.1163	No	-	-	-
282	DuanZX	0.1155	No	-	-	-
283	PRIME	0.115	No	-	-	-
284	numberEight	0.1146	No	-	-	-
285	PAGA	0.1143	No	-	-	-
286	FFINet	0.1124	No	-	-	-
287	ProphNet	0.1101	No	-	-	-
288	DCMS	0.1094	No	Bootstrap Motion Forecasting With Self-Consisten...	2022-04-12	-
289	TO	0.1075	No	-	-	-
290	VI LaneIter	0.1065	No	-	-	-
291	Tang Luqi	0.1059	No	-	-	-
292	QCNet	0.1056	No	-	-	Code
293	GOHOME	0.1048	No	GOHOME: Graph-Oriented Heatmap Output for future...	2021-09-04	-
294	Tang Luqi	0.1048	No	-	-	-
295	THOMAS	0.1038	No	THOMAS: Trajectory Heatmap Output with learned M...	2021-10-13	-
296	tusiji12138	0.1035	No	-	-	-
297	SEPT	0.1032	No	-	-	-
298	Miss Rate	0.1032	No	-	-	-
299	HOME + GOHOME	0.0846	No	HOME: Heatmap Output for future Motion Estimation	2021-05-23	Code

Model↕

MR (K=6)▼

Extra Data

Paper

Date↕

Code

WST

0.9893

Constant velocity map prior

0.9817

DQS

0.9572

xjtub09

0.9476

suu

0.9386

BaseballBro

0.9312

0.9238

yellowtaxi

0.912

Trial

0.9068

ffffx

0.8915

vectornet

0.889

Online_CL

0.8868

gogogo_zhigang

0.8696

haomo

0.8677

zys

0.8602

0.85

RuSheng

0.8449

zlewe

0.8348

NCTU_309512033

0.8348

null

0.8348

baseline_argo_contant_vel_01

0.8348

test_1

0.8348

jwlhs104

0.8261

Johnny Hsieh

0.8237

pph

0.8197

mmmddd

0.8168

que_anr

0.8141

Waldo

0.8139

haomokeji

0.8117

baguette

0.8087

gg6666

0.806

lstm+cnn

0.7805

par

0.7773

grip

0.7701

GoodMountain

0.7668

LSTMs

0.7648

miaomiao

0.7574

Challenge

0.7498

slf

0.7392

0.7382

Xiaogang

0.7205

Tang Luqi

0.7159

0.7119

D0.2

0.709

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

0.6982

Funtastic_4casting

0.6916

Constant-V-Forecating(very very naive)

0.6916

DeepPrediction

0.6916

lllsssjQuery33108037804872914465_1678097593292

0.6916

MT-PNC

0.6916

naive method test

0.6916

default

0.6916

efficent and adaptive

0.6916

posterior with best parameters

0.6899

tmtkinu

0.6868

raster_v2

0.6825

Extended-Social-LSTM

0.6808

tjxu

0.6795

cls-token-transfer208k

0.6791

GRU_CVAE

0.6785

Model Agnostic Meta Learning

0.678

phuang

0.6685

cxl

0.6583

Alice_Bob

0.6582

vector-net-latest

0.6567

Ruochen

0.655

rishabh

0.6457

dis102500

0.6306

1mode-groupEmbed-160000

0.6241

1mode-groupEmbed-106000

0.6201

gcu_v2

0.6142

cul2

0.6016

abac

0.5984

PRANK

0.5955

PRANK: motion Prediction based on RANKing

2020-10-22

PRANK

0.5955

PRANK: motion Prediction based on RANKing

2020-10-22

Multiple Trajectories

0.5901

NCTU-BNN

0.5821

ussm v2-SAIPS

0.5756

hell

0.5718

HYU_ACE

0.5504

ewta

0.5458

0.5428

SHM

0.5423

xjh199923

0.5419

NCTU-heheEECS

0.5369

NN(map)

0.5369

BNet-2S

0.5363

lt_cont_k6

0.5348

Whatever

0.5247

Speculators

0.5203

bartosz

0.5201

wangwentong

0.5156

Fong

0.5128

Aaron Huang

0.4931

Tim

0.4903

Gilgamesh

0.4889

0626

0.4199

Holmes

0.4181

100

TangBug

0.4177

101

llh

0.4141

102

randommm

0.4083

103

lixin

0.4051

104

AttnLstmMultiMod_fix

0.4048

105

Vi yulia 51 prob

0.3983

106

deleted_accout_because_of_submission_error

0.3983

107

fs_test

0.3969

108

Social-CVAE

0.3857

109

xmy

0.3591

110

lanegcn_pimp_curr_sgd

0.3579

111

msms

0.3553

112

CoderTeam

0.3539

113

hale

0.3434

114

adl

0.3393

115

multimodal

0.3389

116

team_moe

0.3276

117

mmppgg

0.3264

118

huangmozhi

0.3191

119

mtz

0.3135

120

Anonymous1234

0.2904

121

huhu

0.2887

122

timtim

0.2845

123

CRAT-Pred

0.2624

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

2022-02-09

Code

124

Anchor

0.2611

125

TNT_20220819

0.2585

126

El Camino

0.2508

127

DGA_lg

0.2471

128

damplyv1

0.2333

129

0.2317

130

fyyclass

0.2208

131

Map Static+Specific

0.2189

132

6 mode model

0.2186

133

uulm-mrm

0.2179

134

xjh

0.214

135

huangmozhi9527

0.2129

136

arky

0.2096

137

argo_test_18_5m_add_rotation_change_data_change_model_3_01

0.205

138

NCTU-GPL-GPAL

0.1951

139

Array

0.195

140

SCP

0.1937

141

tnt-mtp_100s_targetloss_1-1_6

0.1891

142

mt_navi

0.1884

143

idlaber-Hans

0.1865

144

rush

0.1864

145

UAR

0.1858

146

CMAN(av1_demo)

0.1849

147

TrajectoryOracle

0.1823

148

zhanzhenxueyuan

0.1823

149

SCP

0.1804

150

player

0.1795

151

chant

0.1791

152

ulimit

0.1779

153

MultiLine

0.1779

154

sample_replace_gt_smalln_82

0.1764

155

LaneGCN-s

0.1738

156

SCM+Distill(best model)

0.1735

157

leige

0.1732

158

0.1725

159

lagat_mm

0.1707

160

HGO (K=6)

0.1699

161

ORIGINAL

0.1699

162

TPA+Laneloss

0.1696

163

cxx

0.1692

164

huyuening

0.1689

165

somemethod

0.1677

166

lstm

0.1676

167

Europa

0.1674

168

fyyyy

0.1674

169

15 train 40e

0.1673

170

wimp

0.1669

171

lanegcn_12_epoch

0.1666

172

ISY@TK

0.1666

173

William

0.1657

174

Habitat-Web

0.1656

175

TNT - CoRL20

0.1656

TNT: Target-driveN Trajectory Prediction

2020-08-19

Code

176

Transformer1

0.1652

177

just_a_test

0.1651

178

lengyue

0.1647

179

LaneGCN-trainset-36

0.1638

180

AutoBot-Ego

0.1635

181

LGN

0.1634

182

mymodel7_actornet_2_mapnet_a2m_a2a_222

0.1623

183

leon

0.1622

184

LaneGCN

0.162

185

sihong

0.1619

186

Model0

0.1618

187

Alibaba-ADLab

0.1615

188

(VI)

0.1611

189

scale4

0.1606

190

JAL-MTP

0.1603

191

YuNi

0.1597

192

watson

0.1597

193

CY_ng

0.1597

194

cbc

0.1597

195

julie-1

0.1597

196

ywyeh

0.1597

197

hitchhiker

0.1597

198

alfred

0.1597

199

hitljx_test_sub

0.1597

200

chailiang

0.1597

201

zhousihong

0.1597

202

MTN

0.1592

203

wo_longterm

0.1591

204

vi_avp

0.1591

205

JMT

0.1589

206

DF-RNN

0.1589

207

l_DGA_lg

0.1586

208

daiyongjie

0.158

209

brier36

0.1576

210

MSPre

0.154

211

mmTransformer

0.154

212

0.1539

213

FTGN

0.1528

Trajectory Forecasting on Temporal Graphs

2022-07-01

Code

214

Parallel x-transformers version1.1

0.1522

215

0.151

216

Lane_vae

0.1501

217

vilab

0.148

218

CU-aware LaneGCN

0.1474

219

LTP

0.1472

220

prediction-liangdian

0.1471

221

Shangguan_IFS

0.146

222

whr_test

0.1438

223

vill

0.1437

224

Lotus

0.1434

225

MFT

0.1432

226

FRM

0.143

Leveraging Future Relationship Reasoning for Veh...

2023-05-24

227

Waypoint

0.1429

228

lt_tv_cont

0.1427

229

ECARX-V1

0.1425

230

HTTP

0.142

231

ToNet

0.1418

232

Dcupqiu

0.1416

233

vectorgcn

0.1408

234

TestForecast

0.1397

235

lstm_v1

0.1395

236

ATDSNet-v2

0.1383

237

yiqi-fudan

0.1376

238

m2a2_34

0.1374

239

EuclidNet-L

0.1362

240

cls-weight1-5

0.136

241

Hi_base

0.1354

242

mm_kk_parallel_distemb_91

0.1352

243

xia

0.1351

244

parallel_tl_123_0224

0.1349

245

FGNet_sub

0.1341

246

TPCN

0.1333

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

2021-03-04

247

decoder-3-last

0.1331

248

DIDI

0.1327

249

SSL-Lanes

0.1326

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

2022-06-28

Code

250

multipath++

0.1324

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

2021-11-29

Code

251

cls_weight1.2-decoder5-use-last

0.1324

252

Jean

0.1308

253

be_s2lossf_3846

0.1306

254

DSP

0.1303

255

Holistic Transformer

0.1303

Holistic Transformer: A Joint Neural Network for...

2022-06-17

256

matrix_mugen

0.1297

257

FGNet

0.129

258

DTNet(FDE)

0.1282

259

MacFormer

0.1272

TENET: Transformer Encoding Network for Effectiv...

2022-06-30

260

HiVT-128

0.1267

Code

261

Jack-M

0.1266

262

minFDE

0.1258

263

SceneTransformer

0.1255

264

LAformer

0.1245

265

LaneRCNN (IROS 2021)

0.1232

LaneRCNN: Distributed Representations for Graph-...

2021-01-17

266

TSGN

0.1223

267

shernan_wu

0.1223

268

HiVT++

0.1221

Code

269

pred

0.1217

270

chl(yiqi)

0.121

271

Met

0.1209

272

HIKVISION-ADLab-hz

0.1209

273

ATTTHOM

0.1207

274

Ameame

0.1207

275

SenseTime_AP

0.1203

276

Wayformer

0.1186

Wayformer: Motion Forecasting via Simple & Effic...

2022-07-12

Code

277

GANet

0.1179

GANet: Goal Area Network for Motion Forecasting

2022-09-20

Code

278

ProIn_av1

0.1177

279

Gnet

0.1168

280

R-Pred

0.1165

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

2022-11-16

281

TPCN++

0.1163

282

DuanZX

0.1155

283

PRIME

0.115

284

numberEight

0.1146

285

PAGA

0.1143

286

FFINet

0.1124

287

ProphNet

0.1101

288

DCMS

0.1094

Bootstrap Motion Forecasting With Self-Consisten...

2022-04-12

289

0.1075

290

VI LaneIter

0.1065

291

Tang Luqi

0.1059

292

QCNet

0.1056

Code

293

GOHOME

0.1048

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

2021-09-04

294

Tang Luqi

0.1048

295

THOMAS

0.1038

THOMAS: Trajectory Heatmap Output with learned M...

2021-10-13

296

tusiji12138

0.1035

297

SEPT

0.1032

298

Miss Rate

0.1032

299

HOME + GOHOME

0.0846

HOME: Heatmap Output for future Motion Estimation

2021-05-23

Code