Physics-Guided Multi-Agent Trajectory Prediction with Adaptive Fused Graph Neural Architectures
Keywords:
trajectory prediction, multi-agent systems, graph neural networks, physics-guided machine learning, adaptive fusion, socio-technical infrastructure, robustness, fairnessAbstract
Accurate trajectory prediction for multiple interacting agents is a cornerstone of autonomous systems, ranging from self-driving vehicles to drone swarms and pedestrian tracking. While graph neural architectures have advanced the modeling of spatiotemporal dependencies among agents, they often produce predictions that violate fundamental physical laws, such as momentum conservation or collision avoidance. This paper proposes a physics-guided multi-agent trajectory prediction framework that integrates adaptive fused graph neural architectures with explicit physical constraints. The system architecture comprises a spatiotemporal graph encoder that captures agent interactions, a physics-guided module that enforces Newtonian dynamics and geometric consistency, and an adaptive fusion mechanism that dynamically balances data-driven and physics-based predictions based on contextual cues. The paper emphasizes system-level considerations, including structural trade-offs between physical fidelity and model flexibility, computational efficiency, and robustness to noisy observations. It further examines governance challenges related to certification and interpretability, fairness implications arising from biased training data or physics priors, and sustainability concerns in deployment across resource-constrained platforms. Through cross-domain case illustrations involving autonomous driving, pedestrian crowds, and aerial swarms, the study highlights how adaptive fusion can improve prediction reliability while maintaining generalizability. The work concludes with a forward-looking perspective on integrating uncertainty quantification, lifelong learning, and policy frameworks to ensure safe and equitable deployment of physics-guided trajectory prediction systems.
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