A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction
cs.LG
Submitted: 2026-06-12
Updated: 2026-06-12
Comments: 6 pages, 1 figure
Journal ref: The IEEE Intelligent Vehicles Symposium (IEEE IV 2026)
DOI: 10.1109/IVWorkshops71735.2026.11624111
License: http://creativecommons.org/licenses/by/4.0/
The gist: Autonomous driving systems rely on precise trajectory prediction to plan safe and efficient movement.
Terminology
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.
Sources
- Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
- VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
- Graph Attention Networks
- Semi-Supervised Classification with Graph Convolutional Networks
- Inductive Representation Learning on Large Graphs
- Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
- Attention-based Graph Neural Network for Semi-supervised Learning
- Beyond Low-frequency Information in Graph Convolutional Networks
- ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
- Do We Need Anisotropic Graph Neural Networks?
- Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
- How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
- Simplifying Graph Convolutional Networks
- MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
- Topology Adaptive Graph Convolutional Networks
- Convolutional Networks on Graphs for Learning Molecular Fingerprints
- Gated Graph Sequence Neural Networks
- Residual Gated Graph ConvNets
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks