Graph Neural Network-Based Motion Prediction for Dense Traffic Scenarios
Keywords:
graph neural networks, motion prediction, dense traffic, autonomous driving, socio-technical systems, safety governanceAbstract
Dense traffic scenarios represent one of the most demanding settings for motion prediction because they combine rapid interaction, heterogeneous agents, partial observability, and safety-critical decision timelines. Graph neural networks have emerged as a powerful modeling family for such environments because they can represent vehicles, pedestrians, cyclists, and infrastructure elements as relational entities whose future behavior depends on local and global context. This paper examines graph neural network-based motion prediction from a system-level perspective rather than treating it solely as a machine learning accuracy problem. It analyzes the architectural trade-offs among graph construction, temporal aggregation, multimodal decoding, perception coupling, and real-time deployment. The discussion then expands to data infrastructure, continual learning, robustness, fairness, governance, and sustainability. It argues that dense traffic prediction is not merely a technical pipeline but a socio-technical infrastructure problem in which sensing, communication, computation, regulation, and public accountability intersect. The paper also considers cross-domain comparisons with aviation, rail, logistics, and smart urban systems to identify transferable lessons. It concludes that long-term progress requires integrated benchmarks, safety cases, energy-aware design, equitable deployment policies, and governance mechanisms that can accommodate uncertainty while preserving public trust. The central claim is that graph-based prediction can support dense traffic only if technical architecture and institutional architecture evolve together.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
