Recent technological developments have been used extensively in manufacturing vehicles in order to improve the driving experience and add multiple safety features. This article introduces a novel machine learning approach using physiological sensors and thermal imaging of the subjects to detect human thermal discomfort in order to develop a fully automated climate control system in the vehicles that does not need any explicit input from individuals. To achieve this goal, a dataset of thermal videos and physiological signals from 50 subjects is collected, an extensive analysis of different feature sets is conducted, a multimodal approach is experimented, and a cascaded classification system is proposed. Our results evidently show the capability of specific feature sets of detecting human thermal discomfort as well as the superior performance of integrating multimodal features.

中文翻译：

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使用生理传感器和热成像检测驾驶员的热不适

最近的技术发展已广泛用于制造车辆，以改善驾驶体验并增加多项安全功能。本文介绍了一种新的机器学习方法，它使用生理传感器和受试者的热成像来检测人体热不适，以便在车辆中开发一种不需要个人任何明确输入的全自动气候控制系统。为了实现这一目标，收集了来自 50 个对象的热视频和生理信号数据集，对不同的特征集进行了广泛的分析，对多模态方法进行了实验，并提出了级联分类系统。