融合交通信号状态的行人意图预测模型，提升自动驾驶安全性。
Traffic-Aware Pedestrian Intention Prediction
- 引入动态交通灯状态与边界框大小作为关键特征
- 在PIE数据集上比基线高4.75%准确率
- 适合关注城市复杂场景下自动驾驶决策的研究者
准确的行人意图估计对自动驾驶车辆的安全导航至关重要，但现有模型往往未能充分考虑动态交通信号和上下文场景信息，而这些在真实应用中极为关键。本文提出一种交通感知时空图卷积网络（TA-STGCN），将交通标志及其状态（红、黄、绿）融入行人意图预测。该方法引入动态交通信号状态与边界框大小作为关键特征，使模型能够捕捉复杂城市环境中时空依赖关系。实验表明，TA-STGCN在PIE数据集上的准确率比基线模型高出4.75%，验证了其在提升行人意图预测性能方面的有效性。
原文摘要 · Abstract (English)
Accurate pedestrian intention estimation is crucial for the safe navigation of autonomous vehicles (AVs) and hence attracts a lot of research attention. However, current models often fail to adequately consider dynamic traffic signals and contextual scene information, which are critical for real-world applications. This paper presents a Traffic-Aware Spatio-Temporal Graph Convolutional Network (TA-STGCN) that integrates traffic signs and their states (Red, Yellow, Green) into pedestrian intention prediction. Our approach introduces the integration of dynamic traffic signal states and bounding box size as key features, allowing the model to capture both spatial and temporal dependencies in complex urban environments. The model surpasses existing methods in accuracy. Specifically, TA-STGCN achieves a 4.75% higher accuracy compared to the baseline model on the PIE dataset, demonstrating its effectiveness in improving pedestrian intention prediction.
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