Emotion in an active inference model of human driving

人类驾驶主动推理模型中的情绪研究

Abstract: 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.

摘要： 主动推理（Active inference）已成为一种通过平衡目标导向行为与不确定性降低来建模适应性行为的原则性框架。它已成功应用于生物和人工系统，包括近期在人类驾驶领域的研究。

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.

然而，现有的驾驶主动推理模型尚未解决交通行为中一个重要的决定因素：情感状态，它对决策有着显著影响。此前在非交通领域的研究已经探索了主动推理智能体，其中情绪通过环状模型（circumplex model）中的效价（valence）和唤醒度（arousal）轴来表示。

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.

但这些工作仅限于具有离散状态空间的简化设置。在这项工作中，我们提出了一种扩展的效价和唤醒度公式，该公式可以从更复杂的连续状态驾驶主动推理模型中提取出来。

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.

具体而言，我们将情感估计不仅建立在当前状态的基础上，还结合了对未来结果的预测。我们在两个交互式驾驶场景中评估了所提出的方法，结果表明，由此产生的情绪信号与类似场景中报告的情感模式相吻合。

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Authors: Julian F. Schumann, Johan Engström, Ran Wei, Jens Kober, Martijn Wisse, Arkady Zgonnikov 作者： Julian F. Schumann, Johan Engström, Ran Wei, Jens Kober, Martijn Wisse, Arkady Zgonnikov

Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Robotics (cs.RO) 学科分类： 人工智能 (cs.AI)；人机交互 (cs.HC)；机器学习 (cs.LG)；机器人学 (cs.RO)