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<p>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.</p>

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Embodied cognition-driven interpretable trajectory prediction of autonomous systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

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  • Xiao Wang,
  • Quancheng Du,
  • Qiong Wu,
  • Xiaofeng Jia,
  • Liang Lin,
  • Ljubo Vlacic,
  • Changyin Sun,
  • Fei-Yue Wang

摘要

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.