发表机构
Delft University of Technology; Waymo LLC（代尔夫特理工大学; Waymo有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在连续状态的驾驶主动推理模型中，扩展了效价与唤醒的情感表征，结合当前状态与未来预测结果，在交互式驾驶场景中验证了该情感信号与实际模式相符。
AI 中文摘要
主动推理作为一种将目标导向行为与不确定性降低相平衡的原则性框架，已成功应用于生物与人工系统，包括近期的人类驾驶研究。然而，现有驾驶主动推理模型尚未解决交通中影响行为的重要决定因素——情感状态，其对决策有显著影响。非交通领域的前期工作已探索了在环形模型的效价与唤醒维度上表征情感的主动推理智能体，但这类工作仅局限于离散状态空间的简化场景。本研究提出了效价与唤醒的扩展表述，可从更复杂的连续状态驾驶主动推理模型中提取；具体而言，我们不仅将情感估计条件化于当前状态，还结合了预测的未来结果。我们在两个交互式驾驶场景中评估了所提方法，结果表明生成的情感信号与类似场景中报告的情感模式相符。
英文摘要
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.
