Scholarlink

<p>Predicting the motion of surrounding vehicles is critical for autonomous driving systems. However, existing prediction models relying on high-definition (HD) maps experience degraded performance in scenarios where map data is incomplete or unavailable. To address this limitation, a map-free vehicle trajectory prediction framework, termed Dynamic Scene Prediction (DynaScene-Pred), is proposed. This method extracts implicit road topology priors from historical vehicle trajectories using OPTICS clustering and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to generate virtual lane features, compensating for the absence of explicit map data. A heterogeneous graph is constructed using vehicle and virtual lane nodes. Leveraging graph convolutional networks (GCN) and a multi-dimensional attention mechanism (incorporating spatial, lane, and temporal attention), the model captures complex vehicle-vehicle social interactions and vehicle-lane spatial constraints. A Conditional Variational Autoencoder (CVAE) decoding module is further employed to generate diverse and physically plausible multi-modal trajectories by aligning latent distributions with the inferred scene constraints. Experimental results on the Argoverse benchmark demonstrate that DynaScene-Pred yields competitive performance among map-free baselines. On the validation set, compared to the TR-Pred model, the proposed method achieves relative improvements of 5.7% and 1.8% in <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text {minADE}_6\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\text {minFDE}_6\)</EquationSource> </InlineEquation>, respectively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Map-free vehicle trajectory prediction method based on heterogeneous graphs and dynamic scene constraints

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

查看全文
  • Huai Liu,
  • Yinxin Bao,
  • Yingyan Hou,
  • Han Wang,
  • Quan Shi

摘要

Predicting the motion of surrounding vehicles is critical for autonomous driving systems. However, existing prediction models relying on high-definition (HD) maps experience degraded performance in scenarios where map data is incomplete or unavailable. To address this limitation, a map-free vehicle trajectory prediction framework, termed Dynamic Scene Prediction (DynaScene-Pred), is proposed. This method extracts implicit road topology priors from historical vehicle trajectories using OPTICS clustering and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to generate virtual lane features, compensating for the absence of explicit map data. A heterogeneous graph is constructed using vehicle and virtual lane nodes. Leveraging graph convolutional networks (GCN) and a multi-dimensional attention mechanism (incorporating spatial, lane, and temporal attention), the model captures complex vehicle-vehicle social interactions and vehicle-lane spatial constraints. A Conditional Variational Autoencoder (CVAE) decoding module is further employed to generate diverse and physically plausible multi-modal trajectories by aligning latent distributions with the inferred scene constraints. Experimental results on the Argoverse benchmark demonstrate that DynaScene-Pred yields competitive performance among map-free baselines. On the validation set, compared to the TR-Pred model, the proposed method achieves relative improvements of 5.7% and 1.8% in \(\text {minADE}_6\) and \(\text {minFDE}_6\) , respectively.