适用于多车交互场景的车辆轨迹预测模型

doi:10.13229/j.cnki.jdxbgxb.20220728

Abstract

Abstract:

A DIP-LSTM model with dynamic interactive poling layer is proposed， which enables neighboring vehicles to share hidden states of LSTM network by pooling to get the characteristic of historical trajectory， and then realizes interactive modeling of time-space relationship between target vehicle and surrounding vehicles. NGSIM from USA and High-D from Germany are used to train and test the model， and the accuracy， robustness and transferability of the model are verified. The results show that compared with the traditional model prediction method， the DIP-LSTM network show advantages in prediction accuracy and long-time prediction considering multi-vehicle interactive information， and the model has good transferability and robustness， which significantly improves the practicability and universality of intelligent vehicle trajectory prediction model.

Key words: vehicle engineering, automatic driving, trajectory prediction, multi-vehicle interaction, long short-term memory

CLC Number:

U495

Ling HUANG,Zuan CUI,Feng YOU,Pei-xin HONG,Hao-chuan ZHONG,Yi-xuan ZENG. Vehicle trajectory prediction model for multi-vehicle interaction scenario[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1188-1195.

Figures/Tables 8

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模型	预测时域
模型	1 s	2 s	3 s	4 s	5 s
LSTM	0.68	1.76	3.21	4.82	6.87
S-LSTM	0.63	1.43	2.63	4.12	6.18
SV-LSTM	0.62	1.41	2.44	3.87	5.73
DIP-LSTM	0.58	1.24	2.12	3.14	4.33

干扰	模型	预测时域/s
干扰	模型	1 s	2 s	3 s	4 s	5 s
±10%噪声	LSTM	1.57	4.08	7.25	11.18	16.91
	S-LSTM	1.32	3.15	5.87	9.24	14.23
	SV-LSTM	1.29	3.11	5.81	9.13	14.07
	DIP-LSTM	1.15	2.86	5.43	8.18	11.67
±20%噪声	LSTM	2.23	6.17	11.05	19.27	28.24
	S-LSTM	2.01	5.46	9.56	16.24	23.34
	SV-LSTM	1.97	5.41	9.47	15.82	22.67
	DIP-LSTM	1.85	4.97	8.61	14.17	19.87
±30%噪声	LSTM	3.35	9.25	16.48	31.28	41.25
	S-LSTM	3.08	8.32	14.64	26.32	37.62
	SV-LSTM	3.02	8.25	14.21	25.87	36.58
	DIP-LSTM	2.84	7.73	13.15	23.21	31.18

数据集	模型	RMSE预测时域/s
数据集	模型	1 s	2 s	3 s	4 s	5 s
NGSIM I-101	LSTM	0.86	2.31	4.94	8.36	11.31
	S-LSTM	0.73	1.86	3.76	6.63	8.21
	SV-LSTM	0.71	1.78	3.52	6.29	7.78
	DIP-LSTM	0.64	1.65	2.87	5.24	6.62
High-D	LSTM	2.16	5.57	8.86	15.02	21.12
	S-LSTM	1.83	4.67	7.24	11.56	16.72
	SV-LSTM	1.78	4.55	7.02	11.02	16.14
	DIP-LSTM	1.42	3.25	6.42	9.62	14.27

Vehicle trajectory prediction model for multi-vehicle interaction scenario

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