Embodied cognition-driven interpretable trajectory prediction of autonomous systems

For autonomous systems to operate safely and reliably in dense traffic, they must perform trajectory prediction with human-like, interpretable reasoning. Prevailing data-driven "black-box" models fundamentally lack this capability. This research proposes a paradigm shift toward embodied intelligence, unifying cognitive science principles into a hierarchical framework: a Scene Attention Mechanism for threat prioritization, Social Impact Theory-driven graphs for intent inference, and a physics-compliant Social Force Model. Experimental results demonstrate that our framework reduces average displacement error by 42% and Final Displacement Error by 40% compared to existing state-of-the-art models on ETH and UCY, while enabling near-real-time inference (0.003 s). Crucially, the model's interpretable architecture, which is validated through risk-sensitive heatmaps and graph visualizations, reveals how agents dynamically balance safety, efficiency, and socio-cultural norms. Beyond performance gains, this work constructs an interpretable bridge between computational models and human cognitive science, laying a foundation for trustworthy autonomous systems.