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Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings

Research Article

Vehicle Trajectory Prediction Model Based on Fusion Neural Network

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-34790-0_24,
        author={Xuemei Mou and Xiang Yu and Binbin Wang and Ziyi Wang and Fugui Deng},
        title={Vehicle Trajectory Prediction Model Based on Fusion Neural Network},
        proceedings={Communications and Networking. 17th EAI International Conference, Chinacom 2022, Virtual Event, November 19-20, 2022, Proceedings},
        proceedings_a={CHINACOM},
        year={2023},
        month={6},
        keywords={Intelligent traffic Trajectory prediction Deep learning Spatial-temporal relationships},
        doi={10.1007/978-3-031-34790-0_24}
    }
    
  • Xuemei Mou
    Xiang Yu
    Binbin Wang
    Ziyi Wang
    Fugui Deng
    Year: 2023
    Vehicle Trajectory Prediction Model Based on Fusion Neural Network
    CHINACOM
    Springer
    DOI: 10.1007/978-3-031-34790-0_24
Xuemei Mou1,*, Xiang Yu1, Binbin Wang1, Ziyi Wang1, Fugui Deng1
  • 1: School of Communication and Information Engineering
*Contact email: mouxuemei175101@163.com

Abstract

To address the problem of the lack of interpretability of vehicle trajectory prediction models based on deep learning, this paper proposes a Fusion Neural network with the Spatio-Temporal Attention (STA-FNet) model. The model outputs a predictive distribution of future vehicle trajectories based on different vehicle trajectories and traffic environment factors, with an in-depth analysis of the Spatio-temporal attention weights learned from various urban road traffic scenarios. In this paper, the proposed model is evaluated using the publicly available NGSIM dataset, and the experimental results show that the model not only explains the influence of historical trajectories and road traffic environment on the target vehicle trajectories but also obtains better prediction results in complex traffic environments.

Keywords
Intelligent traffic Trajectory prediction Deep learning Spatial-temporal relationships
Published
2023-06-10
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-34790-0_24
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