Motion Forecasting on Argoverse CVPR 2020

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

LeaderboardDataset

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Results

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#	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	-	-	-

#1ewta
155985.7998
minADE (K=6)
No paper
#2WST
28.3813
minADE (K=6)
No paper
#3tusiji12138
14.9112
minADE (K=6)
No paper
#4BaseballBro
12.6584
minADE (K=6)
No paper
#5Constant velocity map prior
11.8296
minADE (K=6)
No paper
#6Lane_vae
11.2834
minADE (K=6)
No paper
#7DQS
7.1746
minADE (K=6)
No paper
#8xjtub09
5.7132
minADE (K=6)
No paper
#9suu
4.6564
minADE (K=6)
No paper
#10Online_CL
3.9771
minADE (K=6)
No paper
#11BD
3.9521
minADE (K=6)
No paper
#12ffffx
3.9358
minADE (K=6)
No paper
#13yellowtaxi
3.5646
minADE (K=6)
No paper
#14jwlhs104
3.541
minADE (K=6)
No paper
#15zlewe
3.5333
minADE (K=6)
No paper
#16NCTU_309512033
3.5333
minADE (K=6)
No paper
#17null
3.5333
minADE (K=6)
No paper
#18baseline_argo_contant_vel_01
3.5333
minADE (K=6)
No paper
#19test_1
3.5333
minADE (K=6)
No paper
#20mmmddd
3.3861
minADE (K=6)
No paper
#21RuSheng
3.3726
minADE (K=6)
No paper
#22Trial
3.2553
minADE (K=6)
No paper
#23Johnny Hsieh
3.2331
minADE (K=6)
No paper
#24vectornet
3.1088
minADE (K=6)
No paper
#25HH
2.9719
minADE (K=6)
No paper
#26gogogo_zhigang
2.9498
minADE (K=6)
No paper
#27D0.2
2.823
minADE (K=6)
No paper
#28haomo
2.7385
minADE (K=6)
No paper
#29pph
2.7291
minADE (K=6)
No paper
#30zys
2.7271
minADE (K=6)
No paper
#31Waldo
2.6003
minADE (K=6)
No paper
#32Ruochen
2.5705
minADE (K=6)
No paper
#33baguette
2.5344
minADE (K=6)
No paper
#34gg6666
2.5329
minADE (K=6)
No paper
#35lstm+cnn
2.4943
minADE (K=6)
No paper
#36zs
2.4191
minADE (K=6)
No paper
#37que_anr
2.418
minADE (K=6)
No paper
#38haomokeji
2.3805
minADE (K=6)
No paper
#39slf
2.3434
minADE (K=6)
No paper
#40Funtastic_4casting
2.3432
minADE (K=6)
No paper
#41Constant-V-Forecating(very very naive)
2.3432
minADE (K=6)
No paper
#42DeepPrediction
2.3432
minADE (K=6)
No paper
#43lllsssjQuery33108037804872914465_1678097593292
2.3432
minADE (K=6)
No paper
#44MT-PNC
2.3432
minADE (K=6)
No paper
#45naive method test
2.3432
minADE (K=6)
No paper
#46default
2.3432
minADE (K=6)
No paper
#47efficent and adaptive
2.3432
minADE (K=6)
No paper
#48Whatever
2.3101
minADE (K=6)
No paper
#49wangwentong
2.2968
minADE (K=6)
No paper
#50grip
2.2923
minADE (K=6)
No paper
#51LSTMs
2.2831
minADE (K=6)
No paper
#52GRU_CVAE
2.2765
minADE (K=6)
No paper
#53Tang Luqi
2.2719
minADE (K=6)
No paper
#54par
2.2181
minADE (K=6)
No paper
#55miaomiao
2.1962
minADE (K=6)
No paper
#56GoodMountain
2.1875
minADE (K=6)
No paper
#57Challenge
2.1686
minADE (K=6)
No paper
#58posterior with best parameters
2.1564
minADE (K=6)
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#59cxl
2.1476
minADE (K=6)
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#60rishabh
2.1201
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#61Xiaogang
2.1
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2.0839
minADE (K=6)
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#63NCTU-BNN
2.0775
minADE (K=6)
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#64raster-to-svg-train(t70.5k-train120k)1e-4-2-bs5-39k
2.0489
minADE (K=6)
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#65fs
2.0458
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#66Alice_Bob
2.0282
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#67cls-token-transfer208k
2.0031
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#68xjh199923
1.9767
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#69Extended-Social-LSTM
1.9424
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#70raster_v2
1.9123
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#71Gilgamesh
1.888
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#72tjxu
1.8705
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1.8703
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#74lanegcn_pimp_curr_sgd
1.8694
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1.8684
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#76vector-net-latest
1.8525
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#77phuang
1.8312
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#78dis102500
1.8179
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#79hell
1.8114
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#801mode-groupEmbed-160000
1.809
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#811mode-groupEmbed-106000
1.8062
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#82HYU_ACE
1.7875
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#83SHM
1.7691
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#84gcu_v2
1.7303
minADE (K=6)
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#85PRANKSOTA
1.7284
minADE (K=6)· 2020-10-22
PRANK: motion Prediction based on RANKing
#86PRANK
1.7284
minADE (K=6)· 2020-10-22
PRANK: motion Prediction based on RANKing
#87NCTU-heheEECS
1.7129
minADE (K=6)
No paper
#88NN(map)
1.7129
minADE (K=6)
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#89NN
1.7129
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#90abac
1.6997
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#91BNet-2S
1.6939
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#92Speculators
1.684
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#93cul2
1.6557
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#94Aaron Huang
1.6327
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#95Fong
1.6091
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#96Tim
1.6056
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#97Multiple Trajectories
1.6055
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#98ussm v2-SAIPS
1.5969
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#99team_moe
1.549
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#100lt_cont_k6
1.5407
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#101ln
1.5031
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#102llh
1.4811
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#103lixin
1.4608
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#104Anonymous1234
1.4537
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#105bartosz
1.4519
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#106deleted_accout_because_of_submission_error
1.4307
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#107fs_test
1.4161
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1.4024
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1.3998
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1.3988
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1.3836
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1.3818
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1.3568
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1.3502
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1.2904
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1.2774
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1.2687
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1.2684
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1.2522
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1.2216
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1.2187
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#122mtz
1.2128
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#123huhu
1.2052
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1.1807
minADE (K=6)
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1.1741
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#126DGA_lg
1.1662
minADE (K=6)
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#127huangmozhi
1.1624
minADE (K=6)
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#128TNT_20220819
1.1518
minADE (K=6)
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1.1463
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1.1394
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1.1343
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1.1322
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1.1287
minADE (K=6)
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1.0783
minADE (K=6)
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#135CRAT-Pred
1.0626
minADE (K=6)· 2022-02-09
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention Code
#136MultiLine
1.0416
minADE (K=6)
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#137zhanzhenxueyuan
1.0267
minADE (K=6)
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#138Jean
0.9973
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#139lstm
0.99
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#140pred
0.9892
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#1416 mode model
0.9877
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#142Tang Luqi
0.9779
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0.9776
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0.9693
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#145huangmozhi9527
0.9687
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0.9602
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0.9583
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#148Tang Luqi
0.9574
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#149numberEight
0.9518
minADE (K=6)
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#150uulm-mrm
0.9436
minADE (K=6)
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#151GOHOME
0.9425
minADE (K=6)· 2021-09-04
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation
#152THOMAS
0.9423
minADE (K=6)· 2021-10-13
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
#153mt_navi
0.9414
minADE (K=6)
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#154tnt-mtp_100s_targetloss_1-1_6
0.9347
minADE (K=6)
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#155xjh
0.9303
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#156damplyv1
0.9296
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#157tp
0.9193
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#158UAR
0.9191
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0.9172
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0.9163
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#161SCP
0.916
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0.9116
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#163Miss Rate
0.9106
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#164CMAN(av1_demo)
0.9105
minADE (K=6)
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#165Habitat-Web
0.9097
minADE (K=6)
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#166TNT - CoRL20SOTA
0.9097
minADE (K=6)· 2020-08-19
TNT: Target-driveN Trajectory Prediction Code
#167ORIGINAL
0.9096
minADE (K=6)
No paper
#168Alibaba-ADLab
0.908
minADE (K=6)
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#169LaneRCNN (IROS 2021)
0.9038
minADE (K=6)· 2021-01-17
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
#170chant
0.9013
minADE (K=6)
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#171wimp
0.8995
minADE (K=6)
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#172ISY@TK
0.8986
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#173cxx
0.8963
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#174Lotus
0.8948
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#175player
0.8943
minADE (K=6)
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#176HGO (K=6)
0.8935
minADE (K=6)
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#177HOME + GOHOME
0.8904
minADE (K=6)· 2021-05-23
HOME: Heatmap Output for future Motion Estimation Code
#178rush
0.8892
minADE (K=6)
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#179somemethod
0.8888
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#180s1
0.8883
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#181leon
0.8879
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#182TPA+Laneloss
0.8858
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#183fyyyy
0.8853
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#184lagat_mm
0.8841
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#185idlaber-Hans
0.8832
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#186ulimit
0.8832
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0.8817
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#188lengyue
0.8809
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#189Transformer1
0.8803
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0.8779
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#191LaneGCN-s
0.8775
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#192AutoBot-Ego
0.8758
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#193Europa
0.8741
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#194huyuening
0.8716
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#195sihong
0.8715
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#196LaneGCN-trainset-36
0.8706
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#197wo_longterm
0.8703
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#198LaneGCN
0.8703
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#199lanegcn_12_epoch
0.8702
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#200SenseTime_AP
0.8688
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0.8683
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0.8679
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0.8679
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0.8679
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0.8679
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0.8679
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0.8679
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0.8679
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#209alfred
0.8679
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#210hitljx_test_sub
0.8679
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#211chailiang
0.8679
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#212zhousihong
0.8679
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#213LGN
0.8679
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#214DIDI
0.8674
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#215mymodel7_actornet_2_mapnet_a2m_a2a_222
0.8667
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#21615 train 40e
0.8663
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#217ECARX-V1
0.866
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#218just_a_test
0.866
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#219l_DGA_lg
0.8642
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#220Shangguan_IFS
0.8636
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#221(VI)
0.8626
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#222sample_replace_gt_smalln_82
0.8621
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#223vi_avp
0.862
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#224daiyongjie
0.8615
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#225JAL-MTP
0.861
minADE (K=6)
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#226FTGN
0.8607
minADE (K=6)· 2022-07-01
Trajectory Forecasting on Temporal Graphs Code
#22788
0.8605
minADE (K=6)
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#228brier36
0.8579
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#229scale4
0.856
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#230SCM+Distill(best model)
0.8536
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#231MSPre
0.8525
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#232JMT
0.848
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#233DTNet(FDE)
0.8468
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#234lt_tv_cont
0.8444
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#235mmTransformer
0.8436
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#236prediction-liangdian
0.8406
minADE (K=6)
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#237Parallel x-transformers version1.1
0.8406
minADE (K=6)
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#238SSL-Lanes
0.8401
minADE (K=6)· 2022-06-28
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving Code
#239ToNet
0.8393
minADE (K=6)
No paper
#240Waypoint
0.8372
minADE (K=6)
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#241MR
0.8367
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#242vilab
0.836
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#243Dcupqiu
0.8351
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#244ATTTHOM
0.8344
minADE (K=6)
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#245Model0
0.8341
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#246LTP
0.8335
minADE (K=6)
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#247ATDSNet-v2
0.8332
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#248whr_test
0.8331
minADE (K=6)
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#249vectorgcn
0.8308
minADE (K=6)
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#250CU-aware LaneGCN
0.8294
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#251Jack-M
0.8285
minADE (K=6)
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#252lstm_v1
0.8277
minADE (K=6)
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#253yiqi-fudan
0.827
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#254DSP
0.8194
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#255MFT
0.8186
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#256EuclidNet-L
0.8181
minADE (K=6)
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#257HIKVISION-ADLab-hz
0.818
minADE (K=6)
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#258FRM
0.8165
minADE (K=6)· 2023-05-24
Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
#259m2a2_34
0.8157
minADE (K=6)
No paper
#260TPCN
0.8153
minADE (K=6)· 2021-03-04
TPCN: Temporal Point Cloud Networks for Motion Forecasting
#261vill
0.8142
minADE (K=6)
No paper
#262chl(yiqi)
0.813
minADE (K=6)
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#263Holistic Transformer
0.8123
minADE (K=6)· 2022-06-17
Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles
#264MacFormer
0.8121
minADE (K=6)· 2022-06-30
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
#265be_s2lossf_3846
0.81
minADE (K=6)
No paper
#266Met
0.8091
minADE (K=6)
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#267GANet
0.806
minADE (K=6)· 2022-09-20
GANet: Goal Area Network for Motion Forecasting Code
#268Ameame
0.8057
minADE (K=6)
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#269DuanZX
0.8053
minADE (K=6)
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#270ProIn_av1
0.8046
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#271mm_kk_parallel_distemb_91
0.8026
minADE (K=6)
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#272SceneTransformer
0.8026
minADE (K=6)
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#273PAGA
0.8014
minADE (K=6)
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#274FGNet_sub
0.8012
minADE (K=6)
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#275Hi_base
0.7973
minADE (K=6)
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#276parallel_tl_123_0224
0.7963
minADE (K=6)
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#277xia
0.7949
minADE (K=6)
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#278cls-weight1-5
0.7943
minADE (K=6)
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#279decoder-3-last
0.7933
minADE (K=6)
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#280cls_weight1.2-decoder5-use-last
0.7917
minADE (K=6)
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#281multipath++
0.7897
minADE (K=6)· 2021-11-29
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction Code
#282Gnet
0.789
minADE (K=6)
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#283matrix_mugen
0.7863
minADE (K=6)
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#284FGNet
0.7854
minADE (K=6)
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#285TO
0.7819
minADE (K=6)
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#286TPCN++
0.7797
minADE (K=6)
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#287HiVT-128
0.7735
minADE (K=6)
No paperCode
#288LAformer
0.772
minADE (K=6)
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#289VI LaneIter
0.7709
minADE (K=6)
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#290Wayformer
0.7676
minADE (K=6)· 2022-07-12
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks Code
#291HiVT++
0.7673
minADE (K=6)
No paperCode
#292DCMS
0.7659
minADE (K=6)· 2022-04-12
Bootstrap Motion Forecasting With Self-Consistent Constraints
#293R-Pred
0.7629
minADE (K=6)· 2022-11-16
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement
#294ProphNet
0.7623
minADE (K=6)
No paper
#295FFINet
0.7606
minADE (K=6)
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#296shernan_wu
0.7543
minADE (K=6)
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#297TSGN
0.7537
minADE (K=6)
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#298QCNet
0.734
minADE (K=6)
No paperCode
#299SEPT
0.7282
minADE (K=6)
No paper