驾驶轨迹预测中交互建模的图神经网络层选择比较研究

arXiv:2606.14956v1 Announce Type: new Abstract: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.图神经网络（GNN）已成为对道路代理之间时空交互进行建模的一种有前景的方法。然而，设计用于轨迹预测的 GNN 架构仍然是非标准化的，对于哪些图层有效捕获空间交互和时间动态几乎没有指导。 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.这些发现为设计更可解释和更有效的轨迹预测模型提供了有用的指导。