Autonomous Vehicles on Argoverse CVPR 2020

Metric: MR (K=1) (higher is better)

LeaderboardDataset

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Results

#	Model↕	MR (K=1)▼	Extra Data	Paper	Date↕	Code
1	jwlhs104	1	No	-	-	-
2	Johnny Hsieh	0.9999	No	-	-	-
3	WST	0.9967	No	-	-	-
4	BaseballBro	0.9936	No	-	-	-
5	mtz	0.9878	No	-	-	-
6	Constant velocity map prior	0.9851	No	-	-	-
7	DQS	0.9572	No	-	-	-
8	AttnLstmMultiMod_fix	0.9563	No	-	-	-
9	xjtub09	0.9476	No	-	-	-
10	CoderTeam	0.943	No	-	-	-
11	suu	0.9386	No	-	-	-
12	BD	0.9238	No	-	-	-
13	yellowtaxi	0.912	No	-	-	-
14	wangwentong	0.9083	No	-	-	-
15	Trial	0.9068	No	-	-	-
16	D0.2	0.9064	No	-	-	-
17	GRU_CVAE	0.9056	No	-	-	-
18	randommm	0.904	No	-	-	-
19	ewta	0.8917	No	-	-	-
20	ffffx	0.8915	No	-	-	-
21	rishabh	0.8902	No	-	-	-
22	vectornet	0.889	No	-	-	-
23	zs	0.8884	No	-	-	-
24	Online_CL	0.8868	No	-	-	-
25	HYU_ACE	0.8774	No	-	-	-
26	NCTU-heheEECS	0.8715	No	-	-	-
27	NN(map)	0.8715	No	-	-	-
28	NN	0.8715	No	-	-	-
29	hell	0.8715	No	-	-	-
30	Ruochen	0.8709	No	-	-	-
31	SHM	0.8705	No	-	-	-
32	gogogo_zhigang	0.8696	No	-	-	-
33	haomo	0.8677	No	-	-	-
34	Speculators	0.8659	No	-	-	-
35	rush	0.8634	No	-	-	-
36	zys	0.8602	No	-	-	-
37	Whatever	0.8574	No	-	-	-
38	parallel_tl_123_0224	0.8556	No	-	-	-
39	HH	0.85	No	-	-	-
40	Tim	0.8457	No	-	-	-
41	RuSheng	0.8449	No	-	-	-
42	6 mode model	0.8405	No	-	-	-
43	Aaron Huang	0.8388	No	-	-	-
44	xjh199923	0.8388	No	-	-	-
45	lstm+cnn	0.8379	No	-	-	-
46	Gilgamesh	0.8355	No	-	-	-
47	NCTU-BNN	0.8355	No	-	-	-
48	zlewe	0.8348	No	-	-	-
49	NCTU_309512033	0.8348	No	-	-	-
50	null	0.8348	No	-	-	-
51	baseline_argo_contant_vel_01	0.8348	No	-	-	-
52	test_1	0.8348	No	-	-	-
53	mmmddd	0.8348	No	-	-	-
54	Holmes	0.82	No	-	-	-
55	pph	0.8197	No	-	-	-
56	posterior with best parameters	0.8151	No	-	-	-
57	que_anr	0.8141	No	-	-	-
58	Waldo	0.8139	No	-	-	-
59	Funtastic_4casting	0.8122	No	-	-	-
60	Constant-V-Forecating(very very naive)	0.8122	No	-	-	-
61	DeepPrediction	0.8122	No	-	-	-
62	lllsssjQuery33108037804872914465_1678097593292	0.8122	No	-	-	-
63	MT-PNC	0.8122	No	-	-	-
64	naive method test	0.8122	No	-	-	-
65	default	0.8122	No	-	-	-
66	efficent and adaptive	0.8122	No	-	-	-
67	haomokeji	0.8117	No	-	-	-
68	TangBug	0.8097	No	-	-	-
69	baguette	0.8087	No	-	-	-
70	gg6666	0.806	No	-	-	-
71	bartosz	0.8045	No	-	-	-
72	hale	0.802	No	-	-	-
73	0626	0.7964	No	-	-	-
74	fs	0.7907	No	-	-	-
75	llh	0.7892	No	-	-	-
76	multimodal	0.7861	No	-	-	-
77	Map Static+Specific	0.7837	No	-	-	-
78	Anonymous1234	0.7828	No	-	-	-
79	xmy	0.7824	No	-	-	-
80	lixin	0.7802	No	-	-	-
81	slf	0.779	No	-	-	-
82	deleted_accout_because_of_submission_error	0.7789	No	-	-	-
83	par	0.7773	No	-	-	-
84	Vi yulia 51 prob	0.7756	No	-	-	-
85	grip	0.7701	No	-	-	-
86	GoodMountain	0.7668	No	-	-	-
87	fs_test	0.7656	No	-	-	-
88	LSTMs	0.7648	No	-	-	-
89	Model Agnostic Meta Learning	0.7601	No	-	-	-
90	miaomiao	0.7574	No	-	-	-
91	huhu	0.7508	No	-	-	-
92	Challenge	0.7498	No	-	-	-
93	Alice_Bob	0.7494	No	-	-	-
94	adl	0.7471	No	-	-	-
95	JMT	0.7452	No	-	-	-
96	msms	0.7411	No	-	-	-
97	cxl	0.737	No	-	-	-
98	Multiple Trajectories	0.7352	No	-	-	-
99	Xiaogang	0.7347	No	-	-	-
100	timtim	0.7301	No	-	-	-
101	Fong	0.7294	No	-	-	-
102	NCTU-GPL-GPAL	0.7285	No	-	-	-
103	team_moe	0.718	No	-	-	-
104	BNet-2S	0.7171	No	-	-	-
105	DF-RNN	0.7161	No	-	-	-
106	Tang Luqi	0.7159	No	-	-	-
107	Habitat-Web	0.7097	No	-	-	-
108	TNT - CoRL20	0.7097	No	TNT: Target-driveN Trajectory Prediction	2020-08-19	Code
109	Parallel x-transformers version1.1	0.7081	No	-	-	-
110	DGA_lg	0.7074	No	-	-	-
111	Anchor	0.7032	No	-	-	-
112	raster-to-svg-train(t70.5k-train120k)1e-4-2-bs5-39k	0.6982	No	-	-	-
113	El Camino	0.6971	No	-	-	-
114	huangmozhi	0.6879	No	-	-	-
115	tmtkinu	0.6868	No	-	-	-
116	Jean	0.6856	No	-	-	-
117	raster_v2	0.6825	No	-	-	-
118	Extended-Social-LSTM	0.6808	No	-	-	-
119	tjxu	0.6795	No	-	-	-
120	cls-token-transfer208k	0.6791	No	-	-	-
121	zhanzhenxueyuan	0.6779	No	-	-	-
122	TNT_20220819	0.6749	No	-	-	-
123	mt_navi	0.6742	No	-	-	-
124	huangmozhi9527	0.6702	No	-	-	-
125	CMAN(av1_demo)	0.669	No	-	-	-
126	Social-CVAE	0.6688	No	-	-	-
127	phuang	0.6685	No	-	-	-
128	arky	0.6589	No	-	-	-
129	ln	0.6576	No	-	-	-
130	tusiji12138	0.6574	No	-	-	-
131	vector-net-latest	0.6567	No	-	-	-
132	SCP	0.6542	No	-	-	-
133	lanegcn_pimp_curr_sgd	0.6517	No	-	-	-
134	lstm	0.6467	No	-	-	-
135	Alibaba-ADLab	0.6465	No	-	-	-
136	HGO (K=6)	0.6453	No	-	-	-
137	TrajectoryOracle	0.6422	No	-	-	-
138	UAR	0.639	No	-	-	-
139	idlaber-Hans	0.6375	No	-	-	-
140	TestForecast	0.637	No	-	-	-
141	somemethod	0.6354	No	-	-	-
142	player	0.6353	No	-	-	-
143	uulm-mrm	0.6344	No	-	-	-
144	leige	0.6333	No	-	-	-
145	SCP	0.6329	No	-	-	-
146	CRAT-Pred	0.6323	No	CRAT-Pred: Vehicle Trajectory Prediction with Cr...	2022-02-09	Code
147	lagat_mm	0.6322	No	-	-	-
148	chant	0.6321	No	-	-	-
149	tnt-mtp_100s_targetloss_1-1_6	0.6321	No	-	-	-
150	AutoBot-Ego	0.6312	No	-	-	-
151	dis102500	0.6306	No	-	-	-
152	tp	0.6305	No	-	-	-
153	wimp	0.6288	No	-	-	-
154	FGNet	0.6286	No	-	-	-
155	xjh	0.6278	No	-	-	-
156	Array	0.6277	No	-	-	-
157	wo_longterm	0.6272	No	-	-	-
158	1mode-groupEmbed-160000	0.6241	No	-	-	-
159	ORIGINAL	0.6237	No	-	-	-
160	1mode-groupEmbed-106000	0.6201	No	-	-	-
161	mmTransformer	0.6178	No	-	-	-
162	MTN	0.6172	No	-	-	-
163	ISY@TK	0.6158	No	-	-	-
164	Tang Luqi	0.6151	No	-	-	-
165	gcu_v2	0.6142	No	-	-	-
166	HTTP	0.6133	No	-	-	-
167	fyyclass	0.6133	No	-	-	-
168	MultiLine	0.6127	No	-	-	-
169	cxx	0.6122	No	-	-	-
170	Tang Luqi	0.6118	No	-	-	-
171	ulimit	0.6097	No	-	-	-
172	mmppgg	0.6087	No	-	-	-
173	MR	0.6085	No	-	-	-
174	sihong	0.6074	No	-	-	-
175	sample_replace_gt_smalln_82	0.6036	No	-	-	-
176	Waypoint	0.6023	No	-	-	-
177	l_DGA_lg	0.6019	No	-	-	-
178	cul2	0.6016	No	-	-	-
179	numberEight	0.6008	No	-	-	-
180	Model0	0.6001	No	-	-	-
181	SCM+Distill(best model)	0.6001	No	-	-	-
182	mm_kk_parallel_distemb_91	0.5998	No	-	-	-
183	Miss Rate	0.5988	No	-	-	-
184	FTGN	0.5984	No	Trajectory Forecasting on Temporal Graphs	2022-07-01	Code
185	abac	0.5984	No	-	-	-
186	JAL-MTP	0.5981	No	-	-	-
187	Shangguan_IFS	0.5972	No	-	-	-
188	LaneGCN-s	0.5968	No	-	-	-
189	mymodel7_actornet_2_mapnet_a2m_a2a_222	0.5967	No	-	-	-
190	lanegcn_12_epoch	0.5966	No	-	-	-
191	s1	0.5962	No	-	-	-
192	PRANK	0.5955	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
193	PRANK	0.5955	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
194	LaneGCN-trainset-36	0.5945	No	-	-	-
195	scale4	0.5935	No	-	-	-
196	fyyyy	0.5933	No	-	-	-
197	pred	0.5933	No	-	-	-
198	William	0.5928	No	-	-	-
199	huyuening	0.5927	No	-	-	-
200	lengyue	0.5926	No	-	-	-
201	SceneTransformer	0.5921	No	-	-	-
202	argo_test_18_5m_add_rotation_change_data_change_model_3_01	0.5918	No	-	-	-
203	Transformer1	0.591	No	-	-	-
204	damplyv1	0.5909	No	-	-	-
205	LGN	0.5905	No	-	-	-
206	Europa	0.5891	No	-	-	-
207	15 train 40e	0.5889	No	-	-	-
208	Lane_vae	0.5887	No	-	-	-
209	Lotus	0.5877	No	-	-	-
210	LaneGCN	0.5877	No	-	-	-
211	leon	0.5876	No	-	-	-
212	vi_avp	0.5875	No	-	-	-
213	YuNi	0.5873	No	-	-	-
214	watson	0.5873	No	-	-	-
215	CY_ng	0.5873	No	-	-	-
216	cbc	0.5873	No	-	-	-
217	julie-1	0.5873	No	-	-	-
218	ywyeh	0.5873	No	-	-	-
219	hitchhiker	0.5873	No	-	-	-
220	alfred	0.5873	No	-	-	-
221	hitljx_test_sub	0.5873	No	-	-	-
222	chailiang	0.5873	No	-	-	-
223	zhousihong	0.5873	No	-	-	-
224	prediction-liangdian	0.5871	No	-	-	-
225	(VI)	0.5871	No	-	-	-
226	PRIME	0.5867	No	-	-	-
227	vilab	0.5862	No	-	-	-
228	TPA+Laneloss	0.5852	No	-	-	-
229	minFDE	0.5843	No	-	-	-
230	yiqi-fudan	0.5831	No	-	-	-
231	88	0.5831	No	-	-	-
232	SenseTime_AP	0.583	No	-	-	-
233	daiyongjie	0.5828	No	-	-	-
234	just_a_test	0.5827	No	-	-	-
235	FGNet_sub	0.5814	No	-	-	-
236	brier36	0.5795	No	-	-	-
237	MFT	0.5791	No	-	-	-
238	vill	0.5791	No	-	-	-
239	ATDSNet-v2	0.5766	No	-	-	-
240	lstm_v1	0.5764	No	-	-	-
241	ToNet	0.5764	No	-	-	-
242	ussm v2-SAIPS	0.5756	No	-	-	-
243	DSP	0.5754	No	-	-	-
244	Jack-M	0.575	No	-	-	-
245	MSPre	0.575	No	-	-	-
246	DIDI	0.5742	No	-	-	-
247	FRM	0.5728	No	Leveraging Future Relationship Reasoning for Veh...	2023-05-24	-
248	GOHOME	0.5724	No	GOHOME: Graph-Oriented Heatmap Output for future...	2021-09-04	-
249	HOME + GOHOME	0.5723	No	HOME: Heatmap Output for future Motion Estimation	2021-05-23	Code
250	Wayformer	0.5716	No	Wayformer: Motion Forecasting via Simple & Effic...	2022-07-12	Code
251	ECARX-V1	0.5715	No	-	-	-
252	whr_test	0.5701	No	-	-	-
253	DTNet(FDE)	0.5696	No	-	-	-
254	LaneRCNN (IROS 2021)	0.5685	No	LaneRCNN: Distributed Representations for Graph-...	2021-01-17	-
255	SSL-Lanes	0.5671	No	SSL-Lanes: Self-Supervised Learning for Motion F...	2022-06-28	Code
256	CU-aware LaneGCN	0.5665	No	-	-	-
257	multipath++	0.5645	No	MultiPath++: Efficient Information Fusion and Tr...	2021-11-29	Code
258	vectorgcn	0.5634	No	-	-	-
259	Hi_base	0.563	No	-	-	-
260	LTP	0.5625	No	-	-	-
261	THOMAS	0.5613	No	THOMAS: Trajectory Heatmap Output with learned M...	2021-10-13	-
262	m2a2_34	0.5606	No	-	-	-
263	TPCN	0.5601	No	TPCN: Temporal Point Cloud Networks for Motion F...	2021-03-04	-
264	Dcupqiu	0.5601	No	-	-	-
265	be_s2lossf_3846	0.5598	No	-	-	-
266	MacFormer	0.5596	No	TENET: Transformer Encoding Network for Effectiv...	2022-06-30	-
267	cls-weight1-5	0.5586	No	-	-	-
268	chl(yiqi)	0.5579	No	-	-	-
269	xia	0.5577	No	-	-	-
270	ATTTHOM	0.5573	No	-	-	-
271	decoder-3-last	0.5573	No	-	-	-
272	lt_tv_cont	0.5565	No	-	-	-
273	cls_weight1.2-decoder5-use-last	0.5563	No	-	-	-
274	matrix_mugen	0.5555	No	-	-	-
275	HIKVISION-ADLab-hz	0.553	No	-	-	-
276	TO	0.5528	No	-	-	-
277	Met	0.5517	No	-	-	-
278	Ameame	0.5514	No	-	-	-
279	EuclidNet-L	0.5503	No	-	-	-
280	GANet	0.5499	No	GANet: Goal Area Network for Motion Forecasting	2022-09-20	Code
281	Holistic Transformer	0.5496	No	Holistic Transformer: A Joint Neural Network for...	2022-06-17	-
282	ProIn_av1	0.5475	No	-	-	-
283	HiVT-128	0.5473	No	-	-	Code
284	LAformer	0.5467	No	-	-	-
285	Gnet	0.5449	No	-	-	-
286	TPCN++	0.5435	No	-	-	-
287	FFINet	0.5431	No	-	-	-
288	HiVT++	0.5431	No	-	-	Code
289	shernan_wu	0.5401	No	-	-	-
290	TSGN	0.54	No	-	-	-
291	PAGA	0.5395	No	-	-	-
292	DuanZX	0.5378	No	-	-	-
293	lt_cont_k6	0.5348	No	-	-	-
294	R-Pred	0.5344	No	R-Pred: Two-Stage Motion Prediction Via Tube-Que...	2022-11-16	-
295	DCMS	0.5322	No	Bootstrap Motion Forecasting With Self-Consisten...	2022-04-12	-
296	VI LaneIter	0.5275	No	-	-	-
297	ProphNet	0.5261	No	-	-	-
298	QCNet	0.5257	No	-	-	Code
299	SEPT	0.5154	No	-	-	-

Model↕

MR (K=1)▼

Extra Data

Paper

Date↕

Code

jwlhs104

Johnny Hsieh

0.9999

WST

0.9967

BaseballBro

0.9936

mtz

0.9878

Constant velocity map prior

0.9851

DQS

0.9572

AttnLstmMultiMod_fix

0.9563

xjtub09

0.9476

CoderTeam

0.943

suu

0.9386

0.9238

yellowtaxi

0.912

wangwentong

0.9083

Trial

0.9068

D0.2

0.9064

GRU_CVAE

0.9056

randommm

0.904

ewta

0.8917

ffffx

0.8915

rishabh

0.8902

vectornet

0.889

0.8884

Online_CL

0.8868

HYU_ACE

0.8774

NCTU-heheEECS

0.8715

NN(map)

0.8715

hell

0.8715

Ruochen

0.8709

SHM

0.8705

gogogo_zhigang

0.8696

haomo

0.8677

Speculators

0.8659

rush

0.8634

zys

0.8602

Whatever

0.8574

parallel_tl_123_0224

0.8556

0.85

Tim

0.8457

RuSheng

0.8449

6 mode model

0.8405

Aaron Huang

0.8388

xjh199923

0.8388

lstm+cnn

0.8379

Gilgamesh

0.8355

NCTU-BNN

0.8355

zlewe

0.8348

NCTU_309512033

0.8348

null

0.8348

baseline_argo_contant_vel_01

0.8348

test_1

0.8348

mmmddd

0.8348

Holmes

0.82

pph

0.8197

posterior with best parameters

0.8151

que_anr

0.8141

Waldo

0.8139

Funtastic_4casting

0.8122

Constant-V-Forecating(very very naive)

0.8122

DeepPrediction

0.8122

lllsssjQuery33108037804872914465_1678097593292

0.8122

MT-PNC

0.8122

naive method test

0.8122

default

0.8122

efficent and adaptive

0.8122

haomokeji

0.8117

TangBug

0.8097

baguette

0.8087

gg6666

0.806

bartosz

0.8045

hale

0.802

0626

0.7964

0.7907

llh

0.7892

multimodal

0.7861

Map Static+Specific

0.7837

Anonymous1234

0.7828

xmy

0.7824

lixin

0.7802

slf

0.779

deleted_accout_because_of_submission_error

0.7789

par

0.7773

Vi yulia 51 prob

0.7756

grip

0.7701

GoodMountain

0.7668

fs_test

0.7656

LSTMs

0.7648

Model Agnostic Meta Learning

0.7601

miaomiao

0.7574

huhu

0.7508

Challenge

0.7498

Alice_Bob

0.7494

adl

0.7471

JMT

0.7452

msms

0.7411

cxl

0.737

Multiple Trajectories

0.7352

Xiaogang

0.7347

100

timtim

0.7301

101

Fong

0.7294

102

NCTU-GPL-GPAL

0.7285

103

team_moe

0.718

104

BNet-2S

0.7171

105

DF-RNN

0.7161

106

Tang Luqi

0.7159

107

Habitat-Web

0.7097

108

TNT - CoRL20

0.7097

TNT: Target-driveN Trajectory Prediction

2020-08-19

Code

109

Parallel x-transformers version1.1

0.7081

110

DGA_lg

0.7074

111

Anchor

0.7032

112

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

0.6982

113

El Camino

0.6971

114

huangmozhi

0.6879

115

tmtkinu

0.6868

116

Jean

0.6856

117

raster_v2

0.6825

118

Extended-Social-LSTM

0.6808

119

tjxu

0.6795

120

cls-token-transfer208k

0.6791

121

zhanzhenxueyuan

0.6779

122

TNT_20220819

0.6749

123

mt_navi

0.6742

124

huangmozhi9527

0.6702

125

CMAN(av1_demo)

0.669

126

Social-CVAE

0.6688

127

phuang

0.6685

128

arky

0.6589

129

0.6576

130

tusiji12138

0.6574

131

vector-net-latest

0.6567

132

SCP

0.6542

133

lanegcn_pimp_curr_sgd

0.6517

134

lstm

0.6467

135

Alibaba-ADLab

0.6465

136

HGO (K=6)

0.6453

137

TrajectoryOracle

0.6422

138

UAR

0.639

139

idlaber-Hans

0.6375

140

TestForecast

0.637

141

somemethod

0.6354

142

player

0.6353

143

uulm-mrm

0.6344

144

leige

0.6333

145

SCP

0.6329

146

CRAT-Pred

0.6323

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

2022-02-09

Code

147

lagat_mm

0.6322

148

chant

0.6321

149

tnt-mtp_100s_targetloss_1-1_6

0.6321

150

AutoBot-Ego

0.6312

151

dis102500

0.6306

152

0.6305

153

wimp

0.6288

154

FGNet

0.6286

155

xjh

0.6278

156

Array

0.6277

157

wo_longterm

0.6272

158

1mode-groupEmbed-160000

0.6241

159

ORIGINAL

0.6237

160

1mode-groupEmbed-106000

0.6201

161

mmTransformer

0.6178

162

MTN

0.6172

163

ISY@TK

0.6158

164

Tang Luqi

0.6151

165

gcu_v2

0.6142

166

HTTP

0.6133

167

fyyclass

0.6133

168

MultiLine

0.6127

169

cxx

0.6122

170

Tang Luqi

0.6118

171

ulimit

0.6097

172

mmppgg

0.6087

173

0.6085

174

sihong

0.6074

175

sample_replace_gt_smalln_82

0.6036

176

Waypoint

0.6023

177

l_DGA_lg

0.6019

178

cul2

0.6016

179

numberEight

0.6008

180

Model0

0.6001

181

SCM+Distill(best model)

0.6001

182

mm_kk_parallel_distemb_91

0.5998

183

Miss Rate

0.5988

184

FTGN

0.5984

Trajectory Forecasting on Temporal Graphs

2022-07-01

Code

185

abac

0.5984

186

JAL-MTP

0.5981

187

Shangguan_IFS

0.5972

188

LaneGCN-s

0.5968

189

mymodel7_actornet_2_mapnet_a2m_a2a_222

0.5967

190

lanegcn_12_epoch

0.5966

191

0.5962

192

PRANK

0.5955

PRANK: motion Prediction based on RANKing

2020-10-22

193

PRANK

0.5955

PRANK: motion Prediction based on RANKing

2020-10-22

194

LaneGCN-trainset-36

0.5945

195

scale4

0.5935

196

fyyyy

0.5933

197

pred

0.5933

198

William

0.5928

199

huyuening

0.5927

200

lengyue

0.5926

201

SceneTransformer

0.5921

202

argo_test_18_5m_add_rotation_change_data_change_model_3_01

0.5918

203

Transformer1

0.591

204

damplyv1

0.5909

205

LGN

0.5905

206

Europa

0.5891

207

15 train 40e

0.5889

208

Lane_vae

0.5887

209

Lotus

0.5877

210

LaneGCN

0.5877

211

leon

0.5876

212

vi_avp

0.5875

213

YuNi

0.5873

214

watson

0.5873

215

CY_ng

0.5873

216

cbc

0.5873

217

julie-1

0.5873

218

ywyeh

0.5873

219

hitchhiker

0.5873

220

alfred

0.5873

221

hitljx_test_sub

0.5873

222

chailiang

0.5873

223

zhousihong

0.5873

224

prediction-liangdian

0.5871

225

(VI)

0.5871

226

PRIME

0.5867

227

vilab

0.5862

228

TPA+Laneloss

0.5852

229

minFDE

0.5843

230

yiqi-fudan

0.5831

231

0.5831

232

SenseTime_AP

0.583

233

daiyongjie

0.5828

234

just_a_test

0.5827

235

FGNet_sub

0.5814

236

brier36

0.5795

237

MFT

0.5791

238

vill

0.5791

239

ATDSNet-v2

0.5766

240

lstm_v1

0.5764

241

ToNet

0.5764

242

ussm v2-SAIPS

0.5756

243

DSP

0.5754

244

Jack-M

0.575

245

MSPre

0.575

246

DIDI

0.5742

247

FRM

0.5728

Leveraging Future Relationship Reasoning for Veh...

2023-05-24

248

GOHOME

0.5724

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

2021-09-04

249

HOME + GOHOME

0.5723

HOME: Heatmap Output for future Motion Estimation

2021-05-23

Code

250

Wayformer

0.5716

Wayformer: Motion Forecasting via Simple & Effic...

2022-07-12

Code

251

ECARX-V1

0.5715

252

whr_test

0.5701

253

DTNet(FDE)

0.5696

254

LaneRCNN (IROS 2021)

0.5685

LaneRCNN: Distributed Representations for Graph-...

2021-01-17

255

SSL-Lanes

0.5671

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

2022-06-28

Code

256

CU-aware LaneGCN

0.5665

257

multipath++

0.5645

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

2021-11-29

Code

258

vectorgcn

0.5634

259

Hi_base

0.563

260

LTP

0.5625

261

THOMAS

0.5613

THOMAS: Trajectory Heatmap Output with learned M...

2021-10-13

262

m2a2_34

0.5606

263

TPCN

0.5601

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

2021-03-04

264

Dcupqiu

0.5601

265

be_s2lossf_3846

0.5598

266

MacFormer

0.5596

TENET: Transformer Encoding Network for Effectiv...

2022-06-30

267

cls-weight1-5

0.5586

268

chl(yiqi)

0.5579

269

xia

0.5577

270

ATTTHOM

0.5573

271

decoder-3-last

0.5573

272

lt_tv_cont

0.5565

273

cls_weight1.2-decoder5-use-last

0.5563

274

matrix_mugen

0.5555

275

HIKVISION-ADLab-hz

0.553

276

0.5528

277

Met

0.5517

278

Ameame

0.5514

279

EuclidNet-L

0.5503

280

GANet

0.5499

GANet: Goal Area Network for Motion Forecasting

2022-09-20

Code

281

Holistic Transformer

0.5496

Holistic Transformer: A Joint Neural Network for...

2022-06-17

282

ProIn_av1

0.5475

283

HiVT-128

0.5473

Code

284

LAformer

0.5467

285

Gnet

0.5449

286

TPCN++

0.5435

287

FFINet

0.5431

288

HiVT++

0.5431

Code

289

shernan_wu

0.5401

290

TSGN

0.54

291

PAGA

0.5395

292

DuanZX

0.5378

293

lt_cont_k6

0.5348

294

R-Pred

0.5344

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

2022-11-16

295

DCMS

0.5322

Bootstrap Motion Forecasting With Self-Consisten...

2022-04-12

296

VI LaneIter

0.5275

297

ProphNet

0.5261

298

QCNet

0.5257

Code

299

SEPT

0.5154