A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction
arXiv:2606.14956v1 Announce Type: new 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 capabilit
The proliferation of autonomous driving R&D necessitates more precise and efficient trajectory prediction, making optimization of core AI components like GNNs critical.
Improved GNN architectures for trajectory prediction directly enhance the safety and reliability of autonomous vehicles, accelerating their commercial deployment and societal integration.
This research provides a clearer methodology for selecting optimal GNN layers, potentially standardizing aspects of AI model design for autonomous driving and improving performance.
- · Autonomous vehicle developers
- · AI model developers
- · Graph Neural Network researchers
- · Logistics and transportation sectors
- · Companies relying on less optimized GNN architectures (temporarily)
- · Traditional trajectory prediction methods
More robust and safer autonomous driving systems become feasible.
Accelerated adoption of autonomous vehicles in freight and personal transport, leading to efficiency gains.
Reduced traffic accidents and fatalities due to human error, transforming urban planning and insurance sectors.
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