A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction
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Computer Science > Machine Learning
Title:A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction
Abstract: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.
| Comments: | 6 pages, 1 figure |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.14956 [cs.LG] |
| (or arXiv:2606.14956v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14956
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | The IEEE Intelligent Vehicles Symposium (IEEE IV 2026) |
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