对比19种图神经网络层，找出驾驶轨迹预测中更优的结构组合。
A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction
- 系统比较19种图层，分析其时空建模能力。
- ARMA、Chebyshev与拓扑感知层表现最优，精度显著提升。
- 提出聚合方式、注意力机制与多跳加权等实用设计原则。
自动驾驶系统依赖精准的轨迹预测来规划安全高效的行驶路径。图神经网络（GNN）已成为建模道路参与者之间时空交互的有力方法。然而，针对轨迹预测的GNN架构设计仍缺乏标准化，尚无明确指导说明哪些图层能有效捕捉空间交互与时间动态。本文对19种图层类型进行了详细对比研究，聚焦其空间与时间处理能力，以识别适用于轨迹预测的最佳架构组合。在所探索的超参数设置下，我们发现五种突出的层组合，其中ARMA、Chebyshev及拓扑感知层表现持续优于其他类型。除性能指标外，研究还提炼出实用的设计原则：求和聚合优于均值聚合，多头注意力机制可增强交互表达，对不同跳数赋予不同权重能显著提升预测精度。这些发现为构建更具可解释性与高效性的轨迹预测模型提供了重要参考。
原文摘要 · Abstract (English)
Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement. Graph Neural Networks (GNNs) have become a promising approach for modelling spatiotemporal interactions among road agents. However, designing GNN architectures for trajectory prediction remains non-standardized, with little guidance on which graph layers effectively capture spatial interactions and temporal dynamics. This paper offers a detailed comparative study of 19 graph layer types, focusing on their spatial and temporal processing capabilities to discover the most effective architectures for trajectory prediction. Within the explored hyperparameter setting, we highlight five standout layer combinations, with ARMA, Chebyshev, and topology-aware layers consistently performing better than others. Beyond performance metrics, our findings yield practical design principles: sum-based aggregation is more effective than mean-based methods, multi-head attention mechanisms enable richer interactions, and assigning different weights to different hop distances significantly improves prediction accuracy. These findings offer useful guidance for designing more interpretable and effective trajectory prediction models.
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