Driving can occupy a large portion of daily life and often can elicit negative emotional states like anger or stress, which can significantly impact road safety and long-term human health. In recent decades, the arrival of new tools to help recognize human affect has inspired increasing interest in how to develop emotion-aware systems for cars. To help researchers make needed advances in this area, this article provides a comprehensive literature survey of work addressing the problem of human emotion recognition in an automotive context. We systematically review the literature back to 2002 and identify 63 peer-review published articles on this topic. We overview each study’s methodology to measure and recognize emotions in the context of driving. Across the literature, we find a strong preference toward studying emotional states associated with high arousal and negative valence, monitoring the different states with cardiac, electrodermal activity, and speech signals, and using supervised machine learning to automatically infer the underlying human affective states. This article summarizes the existing work together with publicly available resources (e.g., datasets and tools) to help new researchers get started in this field. We also identify new research opportunities to help advance progress for improving driver emotion recognition.

中文翻译：

---

智能汽车驾驶员情绪识别

驾驶会占据日常生活的很大一部分，并且经常会引发愤怒或压力等负面情绪状态，这会严重影响道路安全和人类的长期健康。近几十年来，帮助识别人类情感的新工具的出现激发了人们对如何开发汽车情感感知系统的兴趣。为了帮助研究人员在该领域取得必要的进展，本文对解决汽车环境中人类情感识别问题的工作进行了全面的文献调查。我们系统地回顾了 2002 年的文献，并确定了 63 篇关于该主题的同行评议发表的文章。我们概述了每项研究在驾驶环境中测量和识别情绪的方法。纵观文献，我们发现强烈倾向于研究与高唤醒和负效价相关的情绪状态，用心脏、皮肤电活动和语音信号监测不同的状态，并使用监督机器学习自动推断潜在的人类情感状态。本文总结了现有的工作以及公开可用的资源（例如，数据集和工具），以帮助新的研究人员在该领域开始。我们还发现了新的研究机会，以帮助推进改善驾驶员情绪识别的进展。本文总结了现有的工作以及公开可用的资源（例如，数据集和工具），以帮助新的研究人员在该领域开始。我们还发现了新的研究机会，以帮助推进改善驾驶员情绪识别的进展。本文总结了现有的工作以及公开可用的资源（例如，数据集和工具），以帮助新的研究人员在该领域开始。我们还发现了新的研究机会，以帮助推进改善驾驶员情绪识别的进展。