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May 18, 2026Journal of King Saud University - Computer and Information SciencesOpen Access

A causal inference-integrated graph neural network model for lane-changing intent prediction of vehicle driving

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Authors

GYGaoteng YuanPQPing QiuJZJun Zhuang

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Overview

Randomized trial demonstrates enhanced intention prediction in vehicle driving, suggesting improved traffic safety and efficiency.

Key Points

  • This study aims to improve vehicle lane change intention prediction by integrating causal inference with graph neural networks.
  • Proposed the Causality-Augmented Graph Convolutional Network (CA-GCN) for prediction.
  • Used Granger causality analysis to establish a directed sparse causal graph.
  • Conducted experiments on the NGSIM and HighD datasets to evaluate model performance.
  • CA-GCN achieved a trajectory prediction RMSE of 1.3 m, outperforming baseline GCN with 1.85 m (p<0.01).
  • Intention recognition accuracy exceeded 96%, indicating strong robustness in imbalanced traffic scenarios.

Cite This Study

Yuan et al. (2026) studied this question.

synapsesocial.com/papers/6a0aac2b5ba8ef6d83b6fb50https://doi.org/10.1007/s44443-026-00824-1
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