城市道路场景下考虑多类交通参与者的多模态车辆轨迹预测方法研究

doi:10.19562/j.chinasae.qcgc.2024.03.003

Abstract

Abstract:

Vehicle trajectory prediction is one of the key technologies for autonomous driving. In view of the problem that previous prediction models rarely consider multiple types of participants other than vehicles in urban road scenarios， a multimodal vehicle trajectory prediction model considering multiple types of traffic participants is proposed in this paper，. The historical trajectory information is encoded by gated recurrent units， and the features of multiple types of traffic participants are mapped into the driving scene expressed by a graph structure through the attention mechanism， with the context feature extracted by the graph attention network， so that the model can perceive different traffic participants in the environment. In addition， the final multimodal trajectory prediction results are output through the node trajectory prediction module and the coordinate trajectory prediction module. Experiments on nuScenes， a dataset under urban road scenarios， show that， compared to similar existing models， the model proposed has lower computational requirements and more accurate prediction， which is applicable to urban road driving scenarios with mixed human and vehicle traffic.

Key words: vehicle trajectory prediction, autonomous driving, graph attention network（GAT）, attention mechanism, gated recurrent unit（GRU）

Yiwei Zhou,Mo Xia,Bing Zhu. Multimodal Vehicle Trajectory Prediction Methods Considering Multiple Traffic Participants in Urban Road Scenarios[J].Automotive Engineering, 2024, 46(3): 396-406.

Figures/Tables 13

$k$	$λ$	$m i n_A D E$	$m i n_F D E$	$M R$
5	0.5	1.29	2.53	0.52
	0.3	1.26	2.45	0.52
	0.1	1.27	2.48	0.52
	0.05	1.28	2.49	0.54
10	0.5	0.97	1.62	0.35
	0.3	0.94	1.54	0.33
	0.1	0.94	1.55	0.33
	0.05	0.96	1.58	0.35
15	0.5	0.89	1.35	0.30
	0.3	0.87	1.31	0.27
	0.1	0.88	1.34	0.27
	0.05	0.88	1.35	0.28

模型	$k = 5$			$k = 10$
模型	$m i n_A D E$	$m i n_F D E$	$M R$	$m i n_A D E$	$m i n_F D E$	$M R$
不考虑交通参与者	1.36	2.69	0.59	0.99	1.68	0.39
车辆	1.28	2.50	0.54	0.95	1.58	0.35
车辆+行人	1.26	2.45	0.52	0.94	1.54	0.33
车辆+行人+障碍物	1.26	2.46	0.52	0.94	1.55	0.34

模型	$k = 5$		$k = 10$
模型	$m i n_A D E$	$M R$	$m i n_A D E$	$M R$
Trajctron++	1.88	0.70	1.51	0.57
LaPred	1.53	0.53	1.12	0.46
P2T	1.45	0.64	1.16	0.46
GOHOME	1.42	0.57	1.15	0.47
AutoBot	1.37	0.62	1.03	0.44
THOMAS	1.33	0.57	1.04	0.42
PGP	1.30	0.61	1.00	0.37
LAformer	1.19	0.48	0.93	0.33
本模型	1.26	0.52	0.94	0.33

References 33

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Multimodal Vehicle Trajectory Prediction Methods Considering Multiple Traffic Participants in Urban Road Scenarios

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