Vehicle Trajectory Prediction Method Based on Graph Models and Attention Mechanism

doi:10.12382/bgxb.2022.0117

Abstract

Abstract:

In order to improve the accuracy of vehicle trajectory prediction in structured road scenes, a multimodal trajectory prediction method based on graph models and attention mechanism is proposed. For the purpose of modeling road environment characteristics, vehicle motion characteristics and characteristics of interaction between vehicles, the lane graph and vehicle interaction graph are constructed. Environment-vehicle feature fusion is completed by stacked attention modules, so as to realize the unification of static and dynamic features of traffic scenes. The final predicted trajectories and corresponding probabilities are obtained through a two-branch decoding module. The Argoverse dataset is used to train and validate the proposed method. The experimental results show that the proposed method achieves excellent performance of motion prediction in structured road scenes. The prediction accuracy is better than the current mainstream methods.

Key words: vehicle trajectory prediction, graph models, attention mechanism, multimodal

LIAN Jing, DING Rongqi, LI Linhui, WANG Xuecheng, ZHOU Yafu. Vehicle Trajectory Prediction Method Based on Graph Models and Attention Mechanism[J]. Acta Armamentarii, 2023, 44(7): 2162-2170.

Figures/Tables 9

References 24

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方法	单模态(k=1)			多模态(k=6)
方法	min ADE	min FDE	MR	min ADE	min FDE	MR
恒定速度基准方法^[22]	2.96	6.81	0.81	2.34	5.44	0.69
加权最邻近回归基准方法^[22]	3.45	7.88	0.87	1.71	3.28	0.54
Jean^[13]	1.74	4.24	0.69	1.00	1.42	0.13
TNT^[23]	1.77	3.91	0.59	0.94	1.54	0.13
WIMP^[24]	1.82	4.03	0.63	0.90	1.42	0.17
GA-MTP(本文方法)	1.74	3.89	0.60	0.86	1.34	0.15

方法	单模态(k=1)			多模态(k=6)
方法	min ADE	min FDE	MR	min ADE	min FDE	MR
恒定速度基准方法^[22]	2.96	6.81	0.81	2.34	5.44	0.69
加权最邻近回归基准方法^[22]	3.45	7.88	0.87	1.71	3.28	0.54
Jean^[13]	1.74	4.24	0.69	1.00	1.42	0.13
TNT^[23]	1.77	3.91	0.59	0.94	1.54	0.13
WIMP^[24]	1.82	4.03	0.63	0.90	1.42	0.17
GA-MTP(本文方法)	1.74	3.89	0.60	0.86	1.34	0.15

模型组件				评价指标
车交互图	车道图	V2L	L2V	min ADE₁	min FDE₁	min ADE₆	min FDE₆
√				2.14	5.23	1.01	2.06
√	√	√		1.82	4.84	0.89	1.69
√	√		√	1.63	4.27	0.82	1.50
√	√	√	√	1.55	3.57	0.79	1.32

模型组件				评价指标
车交互图	车道图	V2L	L2V	min ADE₁	min FDE₁	min ADE₆	min FDE₆
√				2.14	5.23	1.01	2.06
√	√	√		1.82	4.84	0.89	1.69
√	√		√	1.63	4.27	0.82	1.50
√	√	√	√	1.55	3.57	0.79	1.32