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作者:

Xu, Dongwei (Xu, Dongwei.) | Shang, Xuetian (Shang, Xuetian.) | Liu, Yewanze (Liu, Yewanze.) | Peng, Hang (Peng, Hang.) | Li, Haijian (Li, Haijian.)

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摘要:

Vehicle trajectory prediction is a challenging problem in the field of autonomous driving, which is of great significance to the safety of autonomous driving and traffic roads. In view of the interaction between surrounding vehicles and target vehicle and its own trajectory, we propose a new graph network model to predict future vehicle trajectory. First, the correlation network of vehicles at each time is constructed based on the complex network method. In order to make up for the lack of real spatial relevance caused by the fixed graph, we propose an adaptive parameter matrix to coordinate and optimize the global spatio-temporal graph. Second, the global spatio-temporal features of vehicle historical trajectory data are extracted by stacked graph convolution module. Finally, the obtained graph features are coded based on seq2seq network, and the trajectory prediction of road vehicles at different times in the future is realized. Our model has been trained and verified on the published NGSIM US-101 and I-80 data sets. Compared with other advanced schemes, our model has more accurate results in the future time of 5 seconds. In predicting the future group trajectory of vehicles on the road, the accuracy of long-term prediction is 16.6% higher than that of the most advanced scheme.

关键词:

Correlation Automatic driving spatial topology graph Roads temporal logic network Topology Trajectory Index Terms Data models trajectory prediction intelligent transportation graph convolution neural network Predictive models Feature extraction

作者机构:

  • [ 1 ] [Xu, Dongwei]Zhejiang Univ Technol, Dept Inst Cyberspace Secur, Hangzhou 311121, Peoples R China
  • [ 2 ] [Xu, Dongwei]Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 311121, Peoples R China
  • [ 3 ] [Shang, Xuetian]Zhejiang Univ Technol, Dept Coll Informat Engn, Hangzhou 311121, Peoples R China
  • [ 4 ] [Liu, Yewanze]Zhejiang Univ Technol, Dept Coll Informat Engn, Hangzhou 311121, Peoples R China
  • [ 5 ] [Peng, Hang]Zhejiang Univ Technol, Dept Coll Informat Engn, Hangzhou 311121, Peoples R China
  • [ 6 ] [Shang, Xuetian]Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou 311121, Peoples R China
  • [ 7 ] [Liu, Yewanze]Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou 311121, Peoples R China
  • [ 8 ] [Peng, Hang]Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou 311121, Peoples R China
  • [ 9 ] [Li, Haijian]Beijing Univ Technol, Dept Beijing Key Lab Traff Engn, Beijing 100000, Peoples R China

通讯作者信息:

  • [Xu, Dongwei]Zhejiang Univ Technol, Dept Inst Cyberspace Secur, Hangzhou 311121, Peoples R China;;[Xu, Dongwei]Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 311121, Peoples R China;;

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来源 :

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

ISSN: 2379-8858

年份: 2023

期: 2

卷: 8

页码: 1219-1229

8 . 2 0 0

JCR@2022

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 32

ESI高被引论文在榜: 0 展开所有

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中文被引频次:

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