Understanding and predicting pedestrian crossing behavioral intention is crucial for autonomous vehicles driving safety. Nonetheless, challenges emerge when using promising images or environmental context masks to extract various factors for time-series network modeling, causing pre-processing errors or a loss in efficiency. Typically, pedestrian positions captured by onboard cameras are often distorted and do not accurately reflect their actual movements. To address these issues, GTransPDM -- a Graph-embedded Transformer with a Position Decoupling Module -- was developed for pedestrian crossing intention prediction by leveraging multi-modal features. First, a positional decoupling module was proposed to decompose the pedestrian lateral movement and simulate depth variations in the image view. Then, a graph-embedded Transformer was designed to capture the spatial-temporal dynamics of human pose skeletons, integrating essential factors such as position, skeleton, and ego-vehicle motion. Experimental results indicate that the proposed method achieves 92% accuracy on the PIE dataset and 87% accuracy on the JAAD dataset, with a processing speed of 0.05ms. It outperforms the state-of-the-art in comparison.

本研究解决了使用图像或环境上下文掩码提取行人过马路行为意图时出现的处理错误和效率损失问题。通过提出位置解耦模块和图嵌入变换器，研究创新性地整合了多模态特征，显著提升了对人类姿态动态的建模能力。实验结果表明，该方法在PIE数据集上达到92%准确率，在JAAD数据集上达到87%准确率，显示出优异的性能。

GTransPDM：一种基于图嵌入的变换器与位置解耦模块用于行人过马路意图预测