基于GNN-LSTM-CNN网络的6G车辆轨迹预测算法

doi:10.19665/j.issn1001-2400.2023.03.005

Abstract

Abstract:

The 6G era will realize the interconnection of all things and establish a multi-layer and full-coverage seamless connection.The Internet of Vehicles will be developed and deployed with the help of the 6G technology as a key area for the integration and intersection of communication,transportation,automobile and other industries.Aiming at the insufficient accuracy of the prediction of vehicle trajectories in the 6G Internet of Vehicles,this paper proposes a three-channel neural network model with the method of deep learning.This model takes the impacts of vehicle interaction information,target vehicle trajectories and lane structure information on trajectories into consideration.The long short-term memory network (LSTM) is used to extract the vehicle track information features,graph neural network (GNN) to extract interaction features between different vehicles,and the convolution neural network (CNN) is used to extract lane structure features.The predicted trajectory of the target vehicle is obtained by calculating the weight of the three-channel feature vector and the model is trained and tested by the NGSIM data set.The test results show that the three-channel network prediction method considering multi-dimension information has advantages in prediction accuracy and long time domain prediction compared with other prediction models,and the prediction accuracy is improved by more than 20%.Reducing the data transmission volume of the 6G Internet of Vehicles system can improve the user’s privacy security of the Internet of Vehicles system.

Key words: autopilot, trajectory prediction, neural network, long and short term memory network

CLC Number:

U461

CAI Gouqing,LIU Ling,ZHANG Chong,ZHOU Yiqing. Algorithm for prediction of the 6G vehicle trajectory based on the GNN-LSTM-CNN network[J].Journal of Xidian University, 2023, 50(3): 50-60.

Figures/Tables 11

References 33

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预测时间/s	文中方法	参考论文
预测时间/s	三通道法	CNN-LSTM网络^[28]	GNN网络^[29]	MHA-LSTM^[32]	CS-LSTM^[33]
1	0.57	0.64	0.59	0.41	0.62
2	0.77	0.96	0.82	1.01	1.29
3	0.93	1.22	1.21	1.74	2.13
4	1.16	1.53	1.67	2.67	3.20
5	1.60	2.09	1.96	3.83	4.52

预测时间/s	三通道法	双通道不使用GNN	双通道不使用RNN	双通道不使用CNN
1	0.57	0.63	0.59	0.60
2	0.77	0.95	0.82	0.78
3	0.93	1.25	1.03	1.08
4	1.16	1.62	1.27	1.25
5	1.60	2.21	1.92	1.95

模型	参数数量	训练时间(一个周期)/s	测试时间/s
CNN-LSTM网络^[28]	97 821	52	48
MHA-LSTM^[32]	67 342	26	22
三通道法	148 562	71	70

Algorithm for prediction of the 6G vehicle trajectory based on the GNN-LSTM-CNN network

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