用主动推理模型捕捉驾驶中的情绪变化，提升行为预测真实度
Emotion in an active inference model of human driving
- 将情绪建模为愉悦度与唤醒度，结合当前状态和未来预测
- 在两个交互式驾驶场景中验证，情绪信号与人类报告一致
- 适合交通行为建模、智能驾驶系统研究者参考
主动推理作为一种平衡目标导向行为与不确定性降低的原理性框架，已成功应用于生物与人工系统，包括近期的人类驾驶建模。然而，现有驾驶主动推理模型尚未考虑影响交通行为的重要因素——情绪状态，而情绪显著影响决策。先前非交通领域的工作曾探索基于环形模型中愉悦度与唤醒度的情绪表示，但仅限于离散状态空间的简化场景。本文提出一种扩展的愉悦度与唤醒度建模方法，可从具有连续状态的复杂驾驶主动推理模型中提取情绪信号。特别地，情绪估计不仅依赖当前状态，还融合对未来结果的预测。我们在两个交互式驾驶场景中评估该方法，结果显示生成的情绪信号与类似场景中人类报告的情绪模式高度一致。
原文摘要 · Abstract (English)
Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction. It has been successfully applied across biological and artificial systems, including recent work on human driving. However, existing active inference models of driving have yet to address an important determinant of behavior in traffic: affective state, which significantly influences decision-making. Prior work in non-traffic domains has explored active inference agents in which emotions are represented along the axes of valence and arousal in the circumplex model. However, this work has been limited to simplified settings with discrete state spaces. In this work, we propose an expanded formulation of valence and arousal that can be extracted from a more complex active inference model of driving with continuous states. In particular, we condition affective estimates not only on the current state but also on predicted future outcomes. We evaluate the proposed approach in two interactive driving scenarios and show that the resulting emotion signals correspond to affective patterns reported in similar scenarios.
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