Vehicle trajectory prediction combined with high definition map in graph attention mode

被引：0

作者：

Liu Y.-R. ^{[1
,2
]}

Meng Q.-Y. ^{[1
,2
]}

Guo H.-Y. ^{[1
,2
]}

Li J.-L. ^{[1
,2
]}

机构：

[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun

[2] College of Communication Engineering, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 03期

关键词：

graph attention network; high definition map; long-short term memory network; trajectory prediction; vehicle engineering;

D O I：

10.13229/j.cnki.jdxbgxb20221259

中图分类号：

学科分类号：

摘要：

In order to accurately and reasonably predict the future trajectories of vehicles and understand the changes of surrounding traffic flow, a trajectory prediction method combined with high definition map in graph attention mode was proposed. The encoder-decoder framework based on LSTM network was designed, and the model structure with vehicle historical status and high-precision map information as input was established. A graph query mechanism combining local and global features of vehicles was proposed to output vehicle prediction trajectory. The results of experiments carried out on the nuScenes dataset show that the comprehensive prediction performance of our model is better than other state-of-the-art methods，such as Traj++，CoverNet，etc.，and it has good anti-interference. © 2023 Editorial Board of Jilin University. All rights reserved.

引用

页码：792 / 801

页数：9

共 20 条

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