基于驾驶人认知决策空间的换道意图预测

doi:10.19562/j.chinasae.qcgc.2025.08.004

Abstract

Abstract:

The key to human-machine collaboration in intelligent vehicles is to focus on people. Lane changing is one of the most basic driving tasks. Accurately and efficiently predicting the driver's lane changing intention is crucial to the development of humanized human-machine collaboration. Based on the theory of driver cognitive-making space， in this paper the driver's lane changing intention prediction experiment is designed. The relationship between vehicle operation data， driver's visual characteristics and driving scenes is analyzed， and a topological relationship diagram between the driver's gaze area and the driving scene is generated. Then the driver's lane changing intention prediction model dataset with different time windows is constructed. Based on the inverse residual deep separable convolution of the ConvNeXt （convolutional network） model， combined with the attention mechanism ECA （efficient channel attention）， ConvLSTM （convolutional long short term the memory） network and GCN （graph convolutional networks） figure structure， a predictive model of driver intention lane changing based on attention mechanism is built. The results show that when the time width of the data set is 3 s， the prediction accuracy of the model is the best， which is 91.15%， and the superior performance of the proposed driver lane change intention prediction model based on attention mechanism is fully verified by comparison and ablation experiments.

Key words: intelligent vehicle, lane change intention, cognitive-making space, visual properties, image detection, deep learning

Qinyu Sun,Hang Zhou,Rui Fu,Chang Wang,Tao Huang,Junfeng Yang,Yunhao Wang. Prediction of Lane Change Intention Based on Driver's Cognitive-Making Space[J].Automotive Engineering, 2025, 47(8): 1468-1478.

Figures/Tables 28

References 26

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类别	左换道意图时窗/s	右换道意图时窗/s
均值	2.96	3.09
标准差	0.56	0.43

项目	版本型号
CPU	i9-10700CPU
GPU	NVIDIA RTX 3090
内存	64 GB
环境	Keras2.2.4和Tensorflow1.15

参数名称	数值
Epoch	100
Batch	32
初始学习率	0.001
权值衰减系数	0.000 5
动量系数	0.9

输入	操作	c	n	k	s	C-S
3×256×192×3	TB（Z-P-2D）			（3，3）
3×262×198×3	TB（C-A-B-M）	64		（7，7）	2
3×128×96×64	TB（M-P-2D）			（3，3）	2
3×64×48×64	DSC-ECA	［64，64，128］	1		1	是
3×64×48×128	DSC-ECA	［64，64，128］	3			否
3×64×48×128	DSC-ECA	［128，128，256］	1		2	是
3×32×24×256	DSC-ECA	［128，128，256］	2			否
3×32×24×256	DSC-ECA	［256，256，512］	1		2	是
3×16×12×512	DSC-ECA	［256，256，512］	2			否
3×16×12×512	ConvLSTM2D	512		（3，3）	2
16×12×512	GCN	512
16×12×512	C-E-B-M	512		（3，3）	2
8×6×512	GAP
512	FC	256
256	FC	64
64	FC	3

模型	训练损失	准确率/%	t/s
ResNet^［17］	0.192	79.67	0.28
AlexNet^［18］	0.126	81.42	0.30
Transformer^［7］	0.178	85.56	0.26
本模型	0.021	91.15	0.31

Prediction of Lane Change Intention Based on Driver's Cognitive-Making Space

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Figures/Tables 28

References 26

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模型	训练损失	准确率/%
模型1	0.055	85.68
模型2	0.075	82.16
模型3	0.096	86.41
本模型	0.021	91.15

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