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<p>This paper presents a novel method for predicting motor vehicle trajectory behavior, addressing the challenges of uncertainty and autonomy in trajectory forecasting. Our approach, DenseVectorNet, leverages a human–vehicle–road coupling model to extract interaction features through urban road traffic scene modeling, combining rasterized and vectorized coding to capture map and traffic participant features. The model integrates a hierarchical vector graph neural network, a convolutional neural network, and a multilayer perceptron to obtain global and local context features, enabling accurate trajectory prediction for surrounding vehicles. By incorporating vehicle-to-vehicle, vehicle-to-road, and vehicle-to-person interactions, our method significantly enhances prediction accuracy in complex road scenarios. Experimental results on the Waymo Open Motion Dataset demonstrate superior performance, with a minimum Average Displacement Error (minADE) of 0.8118 and a minimum Final Displacement Error (minFDE) of 1.6364, outperforming most existing models. These findings validate the effectiveness of our approach in improving trajectory prediction accuracy.</p>

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Behavior trajectory prediction for motor vehicle based on human–vehicle–road coupling model

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Xiangyun Ren,
  • Runda Niu,
  • Xi He,
  • Hongbo Gao,
  • Jie Li,
  • Jiawei Qin,
  • Keqiang Li,
  • Bo Cheng,
  • Xiaoyu Zhang

摘要

This paper presents a novel method for predicting motor vehicle trajectory behavior, addressing the challenges of uncertainty and autonomy in trajectory forecasting. Our approach, DenseVectorNet, leverages a human–vehicle–road coupling model to extract interaction features through urban road traffic scene modeling, combining rasterized and vectorized coding to capture map and traffic participant features. The model integrates a hierarchical vector graph neural network, a convolutional neural network, and a multilayer perceptron to obtain global and local context features, enabling accurate trajectory prediction for surrounding vehicles. By incorporating vehicle-to-vehicle, vehicle-to-road, and vehicle-to-person interactions, our method significantly enhances prediction accuracy in complex road scenarios. Experimental results on the Waymo Open Motion Dataset demonstrate superior performance, with a minimum Average Displacement Error (minADE) of 0.8118 and a minimum Final Displacement Error (minFDE) of 1.6364, outperforming most existing models. These findings validate the effectiveness of our approach in improving trajectory prediction accuracy.