State recognition in well-known and customizable environments such as vehicles enables novel insights into users and potentially their intentions. Besides safety-relevant insights into, for example, fatigue, user experience-related assessments become increasingly relevant. As thermal comfort is vital for overall comfort, we introduce a dataset for its prediction in vehicles incorporating 31 input signals and self-labeled user ratings based on a 7-point Likert scale (-3 to +3) by 21 subjects. An importance ranking of such signals indicates higher impact on prediction for signals like ambient temperature, ambient humidity, radiation temperature, and skin temperature. Leveraging modern machine learning architectures enables us to not only automatically recognize human thermal comfort state but also predict future states. We provide details on how we train a recurrent network-based classifier and, thus, perform an initial performance benchmark of our proposed thermal comfort dataset. Ultimately, we compare our collected dataset to publicly available datasets.

翻译：在车辆等常见且可定制环境中进行状态识别，能够为用户及其潜在意图提供新颖洞察。除涉及疲劳等安全相关的评估外，用户体验相关的评估正变得日益重要。鉴于热舒适度对整体舒适性的关键作用，我们引入了一个用于车辆内热舒适预测的数据集，该数据集包含31个输入信号及21名受试者基于7点李克特量表（-3至+3）的自我标注评分。对这些信号的重要性排序表明，环境温度、环境湿度、辐射温度及皮肤温度等信号对预测具有更高影响力。利用现代机器学习架构，我们不仅能自动识别人体热舒适状态，还能预测未来状态。我们详述了如何训练基于循环网络的分类器，并据此对所提出的热舒适数据集进行了初步性能基准测试。最终，我们将收集的数据集与公开可用数据集进行了比较。