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<p>In heterogeneous traffic flow environments, it is critical to accurately predict the future trajectories of human-driven vehicles around intelligent vehicles in real time. This paper introduces a neural network model that integrates both spatial interaction information and the long-term and short-term characteristics of the time series. Initially, the historical state information of both the target vehicle and its surrounding counterparts, along with their spatial interaction relationships, are fed into a Graph Attention Network (GAT) encoder. The graph attention layer effectively manages the intricate relationships among vehicle nodes. Subsequently, this information undergoes processing through a Transformer encoder to extract global dependencies; additionally, residual connections are incorporated to enhance feature representation capabilities. Finally, these data are further captured by the LSTM encoder for capturing short-term features within the time series, and the LSTM decoder receives the hidden state and generates the future trajectory of the target vehicle. Validation conducted on a public dataset demonstrates that the predictive performance of this model significantly outperforms that of the baseline models.</p>

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A vehicle trajectory prediction model that integrates spatial interaction and multiscale temporal features

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月10日

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  • Yuan Gao,
  • Kaifeng Yang,
  • Yibing Yue,
  • Yunfeng Wu

摘要

In heterogeneous traffic flow environments, it is critical to accurately predict the future trajectories of human-driven vehicles around intelligent vehicles in real time. This paper introduces a neural network model that integrates both spatial interaction information and the long-term and short-term characteristics of the time series. Initially, the historical state information of both the target vehicle and its surrounding counterparts, along with their spatial interaction relationships, are fed into a Graph Attention Network (GAT) encoder. The graph attention layer effectively manages the intricate relationships among vehicle nodes. Subsequently, this information undergoes processing through a Transformer encoder to extract global dependencies; additionally, residual connections are incorporated to enhance feature representation capabilities. Finally, these data are further captured by the LSTM encoder for capturing short-term features within the time series, and the LSTM decoder receives the hidden state and generates the future trajectory of the target vehicle. Validation conducted on a public dataset demonstrates that the predictive performance of this model significantly outperforms that of the baseline models.