Autonomous Vehicles on Argoverse CVPR 2020

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

LeaderboardDataset

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Results

#	Model↕	minADE (K=6)▼	Extra Data	Paper	Date↕	Code
1	ewta	155985.7998	No	-	-	-
2	WST	28.3813	No	-	-	-
3	tusiji12138	14.9112	No	-	-	-
4	BaseballBro	12.6584	No	-	-	-
5	Constant velocity map prior	11.8296	No	-	-	-
6	Lane_vae	11.2834	No	-	-	-
7	DQS	7.1746	No	-	-	-
8	xjtub09	5.7132	No	-	-	-
9	suu	4.6564	No	-	-	-
10	Online_CL	3.9771	No	-	-	-
11	BD	3.9521	No	-	-	-
12	ffffx	3.9358	No	-	-	-
13	yellowtaxi	3.5646	No	-	-	-
14	jwlhs104	3.541	No	-	-	-
15	zlewe	3.5333	No	-	-	-
16	NCTU_309512033	3.5333	No	-	-	-
17	null	3.5333	No	-	-	-
18	baseline_argo_contant_vel_01	3.5333	No	-	-	-
19	test_1	3.5333	No	-	-	-
20	mmmddd	3.3861	No	-	-	-
21	RuSheng	3.3726	No	-	-	-
22	Trial	3.2553	No	-	-	-
23	Johnny Hsieh	3.2331	No	-	-	-
24	vectornet	3.1088	No	-	-	-
25	HH	2.9719	No	-	-	-
26	gogogo_zhigang	2.9498	No	-	-	-
27	D0.2	2.823	No	-	-	-
28	haomo	2.7385	No	-	-	-
29	pph	2.7291	No	-	-	-
30	zys	2.7271	No	-	-	-
31	Waldo	2.6003	No	-	-	-
32	Ruochen	2.5705	No	-	-	-
33	baguette	2.5344	No	-	-	-
34	gg6666	2.5329	No	-	-	-
35	lstm+cnn	2.4943	No	-	-	-
36	zs	2.4191	No	-	-	-
37	que_anr	2.418	No	-	-	-
38	haomokeji	2.3805	No	-	-	-
39	slf	2.3434	No	-	-	-
40	Funtastic_4casting	2.3432	No	-	-	-
41	Constant-V-Forecating(very very naive)	2.3432	No	-	-	-
42	DeepPrediction	2.3432	No	-	-	-
43	lllsssjQuery33108037804872914465_1678097593292	2.3432	No	-	-	-
44	MT-PNC	2.3432	No	-	-	-
45	naive method test	2.3432	No	-	-	-
46	default	2.3432	No	-	-	-
47	efficent and adaptive	2.3432	No	-	-	-
48	Whatever	2.3101	No	-	-	-
49	wangwentong	2.2968	No	-	-	-
50	grip	2.2923	No	-	-	-
51	LSTMs	2.2831	No	-	-	-
52	GRU_CVAE	2.2765	No	-	-	-
53	Tang Luqi	2.2719	No	-	-	-
54	par	2.2181	No	-	-	-
55	miaomiao	2.1962	No	-	-	-
56	GoodMountain	2.1875	No	-	-	-
57	Challenge	2.1686	No	-	-	-
58	posterior with best parameters	2.1564	No	-	-	-
59	cxl	2.1476	No	-	-	-
60	rishabh	2.1201	No	-	-	-
61	Xiaogang	2.1	No	-	-	-
62	Model Agnostic Meta Learning	2.0839	No	-	-	-
63	NCTU-BNN	2.0775	No	-	-	-
64	raster-to-svg-train(t70.5k-train120k)1e-4-2-bs5-39k	2.0489	No	-	-	-
65	fs	2.0458	No	-	-	-
66	Alice_Bob	2.0282	No	-	-	-
67	cls-token-transfer208k	2.0031	No	-	-	-
68	xjh199923	1.9767	No	-	-	-
69	Extended-Social-LSTM	1.9424	No	-	-	-
70	raster_v2	1.9123	No	-	-	-
71	Gilgamesh	1.888	No	-	-	-
72	tjxu	1.8705	No	-	-	-
73	0626	1.8703	No	-	-	-
74	lanegcn_pimp_curr_sgd	1.8694	No	-	-	-
75	tmtkinu	1.8684	No	-	-	-
76	vector-net-latest	1.8525	No	-	-	-
77	phuang	1.8312	No	-	-	-
78	dis102500	1.8179	No	-	-	-
79	hell	1.8114	No	-	-	-
80	1mode-groupEmbed-160000	1.809	No	-	-	-
81	1mode-groupEmbed-106000	1.8062	No	-	-	-
82	HYU_ACE	1.7875	No	-	-	-
83	SHM	1.7691	No	-	-	-
84	gcu_v2	1.7303	No	-	-	-
85	PRANK	1.7284	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
86	PRANK	1.7284	No	PRANK: motion Prediction based on RANKing	2020-10-22	-
87	NCTU-heheEECS	1.7129	No	-	-	-
88	NN(map)	1.7129	No	-	-	-
89	NN	1.7129	No	-	-	-
90	abac	1.6997	No	-	-	-
91	BNet-2S	1.6939	No	-	-	-
92	Speculators	1.684	No	-	-	-
93	cul2	1.6557	No	-	-	-
94	Aaron Huang	1.6327	No	-	-	-
95	Fong	1.6091	No	-	-	-
96	Tim	1.6056	No	-	-	-
97	Multiple Trajectories	1.6055	No	-	-	-
98	ussm v2-SAIPS	1.5969	No	-	-	-
99	team_moe	1.549	No	-	-	-
100	lt_cont_k6	1.5407	No	-	-	-
101	ln	1.5031	No	-	-	-
102	llh	1.4811	No	-	-	-
103	lixin	1.4608	No	-	-	-
104	Anonymous1234	1.4537	No	-	-	-
105	bartosz	1.4519	No	-	-	-
106	deleted_accout_because_of_submission_error	1.4307	No	-	-	-
107	fs_test	1.4161	No	-	-	-
108	hale	1.4024	No	-	-	-
109	TangBug	1.3998	No	-	-	-
110	xmy	1.3988	No	-	-	-
111	Holmes	1.3836	No	-	-	-
112	TestForecast	1.3818	No	-	-	-
113	Social-CVAE	1.3568	No	-	-	-
114	Vi yulia 51 prob	1.3502	No	-	-	-
115	arky	1.2904	No	-	-	-
116	Anchor	1.2774	No	-	-	-
117	randommm	1.2687	No	-	-	-
118	AttnLstmMultiMod_fix	1.2684	No	-	-	-
119	adl	1.2522	No	-	-	-
120	CoderTeam	1.2216	No	-	-	-
121	PRIME	1.2187	No	-	-	-
122	mtz	1.2128	No	-	-	-
123	huhu	1.2052	No	-	-	-
124	msms	1.1807	No	-	-	-
125	mmppgg	1.1741	No	-	-	-
126	DGA_lg	1.1662	No	-	-	-
127	huangmozhi	1.1624	No	-	-	-
128	TNT_20220819	1.1518	No	-	-	-
129	argo_test_18_5m_add_rotation_change_data_change_model_3_01	1.1463	No	-	-	-
130	multimodal	1.1394	No	-	-	-
131	timtim	1.1343	No	-	-	-
132	TrajectoryOracle	1.1322	No	-	-	-
133	El Camino	1.1287	No	-	-	-
134	Array	1.0783	No	-	-	-
135	CRAT-Pred	1.0626	No	CRAT-Pred: Vehicle Trajectory Prediction with Cr...	2022-02-09	Code
136	MultiLine	1.0416	No	-	-	-
137	zhanzhenxueyuan	1.0267	No	-	-	-
138	Jean	0.9973	No	-	-	-
139	lstm	0.99	No	-	-	-
140	pred	0.9892	No	-	-	-
141	6 mode model	0.9877	No	-	-	-
142	Tang Luqi	0.9779	No	-	-	-
143	SCP	0.9776	No	-	-	-
144	Map Static+Specific	0.9693	No	-	-	-
145	huangmozhi9527	0.9687	No	-	-	-
146	fyyclass	0.9602	No	-	-	-
147	DF-RNN	0.9583	No	-	-	-
148	Tang Luqi	0.9574	No	-	-	-
149	numberEight	0.9518	No	-	-	-
150	uulm-mrm	0.9436	No	-	-	-
151	GOHOME	0.9425	No	GOHOME: Graph-Oriented Heatmap Output for future...	2021-09-04	-
152	THOMAS	0.9423	No	THOMAS: Trajectory Heatmap Output with learned M...	2021-10-13	-
153	mt_navi	0.9414	No	-	-	-
154	tnt-mtp_100s_targetloss_1-1_6	0.9347	No	-	-	-
155	xjh	0.9303	No	-	-	-
156	damplyv1	0.9296	No	-	-	-
157	tp	0.9193	No	-	-	-
158	UAR	0.9191	No	-	-	-
159	leige	0.9172	No	-	-	-
160	NCTU-GPL-GPAL	0.9163	No	-	-	-
161	SCP	0.916	No	-	-	-
162	HTTP	0.9116	No	-	-	-
163	Miss Rate	0.9106	No	-	-	-
164	CMAN(av1_demo)	0.9105	No	-	-	-
165	Habitat-Web	0.9097	No	-	-	-
166	TNT - CoRL20	0.9097	No	TNT: Target-driveN Trajectory Prediction	2020-08-19	Code
167	ORIGINAL	0.9096	No	-	-	-
168	Alibaba-ADLab	0.908	No	-	-	-
169	LaneRCNN (IROS 2021)	0.9038	No	LaneRCNN: Distributed Representations for Graph-...	2021-01-17	-
170	chant	0.9013	No	-	-	-
171	wimp	0.8995	No	-	-	-
172	ISY@TK	0.8986	No	-	-	-
173	cxx	0.8963	No	-	-	-
174	Lotus	0.8948	No	-	-	-
175	player	0.8943	No	-	-	-
176	HGO (K=6)	0.8935	No	-	-	-
177	HOME + GOHOME	0.8904	No	HOME: Heatmap Output for future Motion Estimation	2021-05-23	Code
178	rush	0.8892	No	-	-	-
179	somemethod	0.8888	No	-	-	-
180	s1	0.8883	No	-	-	-
181	leon	0.8879	No	-	-	-
182	TPA+Laneloss	0.8858	No	-	-	-
183	fyyyy	0.8853	No	-	-	-
184	lagat_mm	0.8841	No	-	-	-
185	idlaber-Hans	0.8832	No	-	-	-
186	ulimit	0.8832	No	-	-	-
187	minFDE	0.8817	No	-	-	-
188	lengyue	0.8809	No	-	-	-
189	Transformer1	0.8803	No	-	-	-
190	MTN	0.8779	No	-	-	-
191	LaneGCN-s	0.8775	No	-	-	-
192	AutoBot-Ego	0.8758	No	-	-	-
193	Europa	0.8741	No	-	-	-
194	huyuening	0.8716	No	-	-	-
195	sihong	0.8715	No	-	-	-
196	LaneGCN-trainset-36	0.8706	No	-	-	-
197	wo_longterm	0.8703	No	-	-	-
198	LaneGCN	0.8703	No	-	-	-
199	lanegcn_12_epoch	0.8702	No	-	-	-
200	SenseTime_AP	0.8688	No	-	-	-
201	William	0.8683	No	-	-	-
202	YuNi	0.8679	No	-	-	-
203	watson	0.8679	No	-	-	-
204	CY_ng	0.8679	No	-	-	-
205	cbc	0.8679	No	-	-	-
206	julie-1	0.8679	No	-	-	-
207	ywyeh	0.8679	No	-	-	-
208	hitchhiker	0.8679	No	-	-	-
209	alfred	0.8679	No	-	-	-
210	hitljx_test_sub	0.8679	No	-	-	-
211	chailiang	0.8679	No	-	-	-
212	zhousihong	0.8679	No	-	-	-
213	LGN	0.8679	No	-	-	-
214	DIDI	0.8674	No	-	-	-
215	mymodel7_actornet_2_mapnet_a2m_a2a_222	0.8667	No	-	-	-
216	15 train 40e	0.8663	No	-	-	-
217	ECARX-V1	0.866	No	-	-	-
218	just_a_test	0.866	No	-	-	-
219	l_DGA_lg	0.8642	No	-	-	-
220	Shangguan_IFS	0.8636	No	-	-	-
221	(VI)	0.8626	No	-	-	-
222	sample_replace_gt_smalln_82	0.8621	No	-	-	-
223	vi_avp	0.862	No	-	-	-
224	daiyongjie	0.8615	No	-	-	-
225	JAL-MTP	0.861	No	-	-	-
226	FTGN	0.8607	No	Trajectory Forecasting on Temporal Graphs	2022-07-01	Code
227	88	0.8605	No	-	-	-
228	brier36	0.8579	No	-	-	-
229	scale4	0.856	No	-	-	-
230	SCM+Distill(best model)	0.8536	No	-	-	-
231	MSPre	0.8525	No	-	-	-
232	JMT	0.848	No	-	-	-
233	DTNet(FDE)	0.8468	No	-	-	-
234	lt_tv_cont	0.8444	No	-	-	-
235	mmTransformer	0.8436	No	-	-	-
236	prediction-liangdian	0.8406	No	-	-	-
237	Parallel x-transformers version1.1	0.8406	No	-	-	-
238	SSL-Lanes	0.8401	No	SSL-Lanes: Self-Supervised Learning for Motion F...	2022-06-28	Code
239	ToNet	0.8393	No	-	-	-
240	Waypoint	0.8372	No	-	-	-
241	MR	0.8367	No	-	-	-
242	vilab	0.836	No	-	-	-
243	Dcupqiu	0.8351	No	-	-	-
244	ATTTHOM	0.8344	No	-	-	-
245	Model0	0.8341	No	-	-	-
246	LTP	0.8335	No	-	-	-
247	ATDSNet-v2	0.8332	No	-	-	-
248	whr_test	0.8331	No	-	-	-
249	vectorgcn	0.8308	No	-	-	-
250	CU-aware LaneGCN	0.8294	No	-	-	-
251	Jack-M	0.8285	No	-	-	-
252	lstm_v1	0.8277	No	-	-	-
253	yiqi-fudan	0.827	No	-	-	-
254	DSP	0.8194	No	-	-	-
255	MFT	0.8186	No	-	-	-
256	EuclidNet-L	0.8181	No	-	-	-
257	HIKVISION-ADLab-hz	0.818	No	-	-	-
258	FRM	0.8165	No	Leveraging Future Relationship Reasoning for Veh...	2023-05-24	-
259	m2a2_34	0.8157	No	-	-	-
260	TPCN	0.8153	No	TPCN: Temporal Point Cloud Networks for Motion F...	2021-03-04	-
261	vill	0.8142	No	-	-	-
262	chl(yiqi)	0.813	No	-	-	-
263	Holistic Transformer	0.8123	No	Holistic Transformer: A Joint Neural Network for...	2022-06-17	-
264	MacFormer	0.8121	No	TENET: Transformer Encoding Network for Effectiv...	2022-06-30	-
265	be_s2lossf_3846	0.81	No	-	-	-
266	Met	0.8091	No	-	-	-
267	GANet	0.806	No	GANet: Goal Area Network for Motion Forecasting	2022-09-20	Code
268	Ameame	0.8057	No	-	-	-
269	DuanZX	0.8053	No	-	-	-
270	ProIn_av1	0.8046	No	-	-	-
271	mm_kk_parallel_distemb_91	0.8026	No	-	-	-
272	SceneTransformer	0.8026	No	-	-	-
273	PAGA	0.8014	No	-	-	-
274	FGNet_sub	0.8012	No	-	-	-
275	Hi_base	0.7973	No	-	-	-
276	parallel_tl_123_0224	0.7963	No	-	-	-
277	xia	0.7949	No	-	-	-
278	cls-weight1-5	0.7943	No	-	-	-
279	decoder-3-last	0.7933	No	-	-	-
280	cls_weight1.2-decoder5-use-last	0.7917	No	-	-	-
281	multipath++	0.7897	No	MultiPath++: Efficient Information Fusion and Tr...	2021-11-29	Code
282	Gnet	0.789	No	-	-	-
283	matrix_mugen	0.7863	No	-	-	-
284	FGNet	0.7854	No	-	-	-
285	TO	0.7819	No	-	-	-
286	TPCN++	0.7797	No	-	-	-
287	HiVT-128	0.7735	No	-	-	Code
288	LAformer	0.772	No	-	-	-
289	VI LaneIter	0.7709	No	-	-	-
290	Wayformer	0.7676	No	Wayformer: Motion Forecasting via Simple & Effic...	2022-07-12	Code
291	HiVT++	0.7673	No	-	-	Code
292	DCMS	0.7659	No	Bootstrap Motion Forecasting With Self-Consisten...	2022-04-12	-
293	R-Pred	0.7629	No	R-Pred: Two-Stage Motion Prediction Via Tube-Que...	2022-11-16	-
294	ProphNet	0.7623	No	-	-	-
295	FFINet	0.7606	No	-	-	-
296	shernan_wu	0.7543	No	-	-	-
297	TSGN	0.7537	No	-	-	-
298	QCNet	0.734	No	-	-	Code
299	SEPT	0.7282	No	-	-	-

Model↕

minADE (K=6)▼

Extra Data

Paper

Date↕

Code

ewta

155985.7998

WST

28.3813

tusiji12138

14.9112

BaseballBro

12.6584

Constant velocity map prior

11.8296

Lane_vae

11.2834

DQS

7.1746

xjtub09

5.7132

suu

4.6564

Online_CL

3.9771

3.9521

ffffx

3.9358

yellowtaxi

3.5646

jwlhs104

3.541

zlewe

3.5333

NCTU_309512033

3.5333

null

3.5333

baseline_argo_contant_vel_01

3.5333

test_1

3.5333

mmmddd

3.3861

RuSheng

3.3726

Trial

3.2553

Johnny Hsieh

3.2331

vectornet

3.1088

2.9719

gogogo_zhigang

2.9498

D0.2

2.823

haomo

2.7385

pph

2.7291

zys

2.7271

Waldo

2.6003

Ruochen

2.5705

baguette

2.5344

gg6666

2.5329

lstm+cnn

2.4943

2.4191

que_anr

2.418

haomokeji

2.3805

slf

2.3434

Funtastic_4casting

2.3432

Constant-V-Forecating(very very naive)

2.3432

DeepPrediction

2.3432

lllsssjQuery33108037804872914465_1678097593292

2.3432

MT-PNC

2.3432

naive method test

2.3432

default

2.3432

efficent and adaptive

2.3432

Whatever

2.3101

wangwentong

2.2968

grip

2.2923

LSTMs

2.2831

GRU_CVAE

2.2765

Tang Luqi

2.2719

par

2.2181

miaomiao

2.1962

GoodMountain

2.1875

Challenge

2.1686

posterior with best parameters

2.1564

cxl

2.1476

rishabh

2.1201

Xiaogang

2.1

Model Agnostic Meta Learning

2.0839

NCTU-BNN

2.0775

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

2.0489

2.0458

Alice_Bob

2.0282

cls-token-transfer208k

2.0031

xjh199923

1.9767

Extended-Social-LSTM

1.9424

raster_v2

1.9123

Gilgamesh

1.888

tjxu

1.8705

0626

1.8703

lanegcn_pimp_curr_sgd

1.8694

tmtkinu

1.8684

vector-net-latest

1.8525

phuang

1.8312

dis102500

1.8179

hell

1.8114

1mode-groupEmbed-160000

1.809

1mode-groupEmbed-106000

1.8062

HYU_ACE

1.7875

SHM

1.7691

gcu_v2

1.7303

PRANK

1.7284

PRANK: motion Prediction based on RANKing

2020-10-22

PRANK

1.7284

PRANK: motion Prediction based on RANKing

2020-10-22

NCTU-heheEECS

1.7129

NN(map)

1.7129

abac

1.6997

BNet-2S

1.6939

Speculators

1.684

cul2

1.6557

Aaron Huang

1.6327

Fong

1.6091

Tim

1.6056

Multiple Trajectories

1.6055

ussm v2-SAIPS

1.5969

team_moe

1.549

100

lt_cont_k6

1.5407

101

1.5031

102

llh

1.4811

103

lixin

1.4608

104

Anonymous1234

1.4537

105

bartosz

1.4519

106

deleted_accout_because_of_submission_error

1.4307

107

fs_test

1.4161

108

hale

1.4024

109

TangBug

1.3998

110

xmy

1.3988

111

Holmes

1.3836

112

TestForecast

1.3818

113

Social-CVAE

1.3568

114

Vi yulia 51 prob

1.3502

115

arky

1.2904

116

Anchor

1.2774

117

randommm

1.2687

118

AttnLstmMultiMod_fix

1.2684

119

adl

1.2522

120

CoderTeam

1.2216

121

PRIME

1.2187

122

mtz

1.2128

123

huhu

1.2052

124

msms

1.1807

125

mmppgg

1.1741

126

DGA_lg

1.1662

127

huangmozhi

1.1624

128

TNT_20220819

1.1518

129

argo_test_18_5m_add_rotation_change_data_change_model_3_01

1.1463

130

multimodal

1.1394

131

timtim

1.1343

132

TrajectoryOracle

1.1322

133

El Camino

1.1287

134

Array

1.0783

135

CRAT-Pred

1.0626

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

2022-02-09

Code

136

MultiLine

1.0416

137

zhanzhenxueyuan

1.0267

138

Jean

0.9973

139

lstm

0.99

140

pred

0.9892

141

6 mode model

0.9877

142

Tang Luqi

0.9779

143

SCP

0.9776

144

Map Static+Specific

0.9693

145

huangmozhi9527

0.9687

146

fyyclass

0.9602

147

DF-RNN

0.9583

148

Tang Luqi

0.9574

149

numberEight

0.9518

150

uulm-mrm

0.9436

151

GOHOME

0.9425

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

2021-09-04

152

THOMAS

0.9423

THOMAS: Trajectory Heatmap Output with learned M...

2021-10-13

153

mt_navi

0.9414

154

tnt-mtp_100s_targetloss_1-1_6

0.9347

155

xjh

0.9303

156

damplyv1

0.9296

157

0.9193

158

UAR

0.9191

159

leige

0.9172

160

NCTU-GPL-GPAL

0.9163

161

SCP

0.916

162

HTTP

0.9116

163

Miss Rate

0.9106

164

CMAN(av1_demo)

0.9105

165

Habitat-Web

0.9097

166

TNT - CoRL20

0.9097

TNT: Target-driveN Trajectory Prediction

2020-08-19

Code

167

ORIGINAL

0.9096

168

Alibaba-ADLab

0.908

169

LaneRCNN (IROS 2021)

0.9038

LaneRCNN: Distributed Representations for Graph-...

2021-01-17

170

chant

0.9013

171

wimp

0.8995

172

ISY@TK

0.8986

173

cxx

0.8963

174

Lotus

0.8948

175

player

0.8943

176

HGO (K=6)

0.8935

177

HOME + GOHOME

0.8904

HOME: Heatmap Output for future Motion Estimation

2021-05-23

Code

178

rush

0.8892

179

somemethod

0.8888

180

0.8883

181

leon

0.8879

182

TPA+Laneloss

0.8858

183

fyyyy

0.8853

184

lagat_mm

0.8841

185

idlaber-Hans

0.8832

186

ulimit

0.8832

187

minFDE

0.8817

188

lengyue

0.8809

189

Transformer1

0.8803

190

MTN

0.8779

191

LaneGCN-s

0.8775

192

AutoBot-Ego

0.8758

193

Europa

0.8741

194

huyuening

0.8716

195

sihong

0.8715

196

LaneGCN-trainset-36

0.8706

197

wo_longterm

0.8703

198

LaneGCN

0.8703

199

lanegcn_12_epoch

0.8702

200

SenseTime_AP

0.8688

201

William

0.8683

202

YuNi

0.8679

203

watson

0.8679

204

CY_ng

0.8679

205

cbc

0.8679

206

julie-1

0.8679

207

ywyeh

0.8679

208

hitchhiker

0.8679

209

alfred

0.8679

210

hitljx_test_sub

0.8679

211

chailiang

0.8679

212

zhousihong

0.8679

213

LGN

0.8679

214

DIDI

0.8674

215

mymodel7_actornet_2_mapnet_a2m_a2a_222

0.8667

216

15 train 40e

0.8663

217

ECARX-V1

0.866

218

just_a_test

0.866

219

l_DGA_lg

0.8642

220

Shangguan_IFS

0.8636

221

(VI)

0.8626

222

sample_replace_gt_smalln_82

0.8621

223

vi_avp

0.862

224

daiyongjie

0.8615

225

JAL-MTP

0.861

226

FTGN

0.8607

Trajectory Forecasting on Temporal Graphs

2022-07-01

Code

227

0.8605

228

brier36

0.8579

229

scale4

0.856

230

SCM+Distill(best model)

0.8536

231

MSPre

0.8525

232

JMT

0.848

233

DTNet(FDE)

0.8468

234

lt_tv_cont

0.8444

235

mmTransformer

0.8436

236

prediction-liangdian

0.8406

237

Parallel x-transformers version1.1

0.8406

238

SSL-Lanes

0.8401

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

2022-06-28

Code

239

ToNet

0.8393

240

Waypoint

0.8372

241

0.8367

242

vilab

0.836

243

Dcupqiu

0.8351

244

ATTTHOM

0.8344

245

Model0

0.8341

246

LTP

0.8335

247

ATDSNet-v2

0.8332

248

whr_test

0.8331

249

vectorgcn

0.8308

250

CU-aware LaneGCN

0.8294

251

Jack-M

0.8285

252

lstm_v1

0.8277

253

yiqi-fudan

0.827

254

DSP

0.8194

255

MFT

0.8186

256

EuclidNet-L

0.8181

257

HIKVISION-ADLab-hz

0.818

258

FRM

0.8165

Leveraging Future Relationship Reasoning for Veh...

2023-05-24

259

m2a2_34

0.8157

260

TPCN

0.8153

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

2021-03-04

261

vill

0.8142

262

chl(yiqi)

0.813

263

Holistic Transformer

0.8123

Holistic Transformer: A Joint Neural Network for...

2022-06-17

264

MacFormer

0.8121

TENET: Transformer Encoding Network for Effectiv...

2022-06-30

265

be_s2lossf_3846

0.81

266

Met

0.8091

267

GANet

0.806

GANet: Goal Area Network for Motion Forecasting

2022-09-20

Code

268

Ameame

0.8057

269

DuanZX

0.8053

270

ProIn_av1

0.8046

271

mm_kk_parallel_distemb_91

0.8026

272

SceneTransformer

0.8026

273

PAGA

0.8014

274

FGNet_sub

0.8012

275

Hi_base

0.7973

276

parallel_tl_123_0224

0.7963

277

xia

0.7949

278

cls-weight1-5

0.7943

279

decoder-3-last

0.7933

280

cls_weight1.2-decoder5-use-last

0.7917

281

multipath++

0.7897

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

2021-11-29

Code

282

Gnet

0.789

283

matrix_mugen

0.7863

284

FGNet

0.7854

285

0.7819

286

TPCN++

0.7797

287

HiVT-128

0.7735

Code

288

LAformer

0.772

289

VI LaneIter

0.7709

290

Wayformer

0.7676

Wayformer: Motion Forecasting via Simple & Effic...

2022-07-12

Code

291

HiVT++

0.7673

Code

292

DCMS

0.7659

Bootstrap Motion Forecasting With Self-Consisten...

2022-04-12

293

R-Pred

0.7629

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

2022-11-16

294

ProphNet

0.7623

295

FFINet

0.7606

296

shernan_wu

0.7543

297

TSGN

0.7537

298

QCNet

0.734

Code

299

SEPT

0.7282