For vehicle safety, the in-time monitoring of the driver and assessing his/her state is a demanding issue. Frustration can lead to aggressive driving behaviours, which play a decisive role in up to one-third of fatal road accidents. Consequently, the authors present the automatic analysis of the emotional driver states of frustration, anxiety, positive and neutral. Based on experiments with normal drivers within cars in real-world (low expressivity) situations, they use speech data, as speech can be recorded with zero invasiveness and comes naturally in driving situations. A careful selection of speech features, subject data identification, hyper-parameter optimisation, and machine learning algorithms was applied for this difficult 4-emotion-class detection problem, where the literature hardly reports results above chance level. In-car assistance demands real-time computing. A very detailed analysis yields best results with relatively small random forests, and with an optimal feature set containing only 65 features (6.51% of the standard emobase feature set) which outperformed all other feature sets, producing 35.38% unweighted average recall (53.26% precision) with low computational effort, and also reducing the inevitably high confusion of ‘neutral’ with low-expressed emotions. This result is comparable to and even outperforming other reported studies of emotion recognition in the wild. Their work, therefore, triggers adaptive automotive safety applications.

中文翻译：

---

迈向具有影响力的车辆以提高安全性和舒适性：在现实的驾驶研究中从录音中识别驾驶员的情绪

为了车辆安全，对驾驶员进行及时监控并评估其状态是一个严峻的问题。挫败感可能导致激进的驾驶行为，这在多达三分之一的致命交通事故中起着决定性的作用。因此，作者提出了对沮丧，焦虑，积极和中立的情绪驱动状态的自动分析。基于在现实世界中（低表现力）情况下汽车中普通驾驶员的实验，他们使用语音数据，因为语音可以零侵入性录制，并且在驾驶情况下自然而然地出现。对于这个困难的4情感类检测问题，人们仔细选择了语音功能，主题数据识别，超参数优化和机器学习算法，在这些文献中，文献很少报告高于机会水平的结果。车载辅助需要实时计算。非常详细的分析可以在相对较小的随机森林和仅包含65个要素（标准的6.51％emobase功能集），其性能优于所有其他功能集，并以较低的计算量产生了35.38％的加权平均召回率（准确度为53.26％），还减少了不可避免的“中立”和低表达情绪的混淆。该结果与野外情绪识别的其他报道研究相当甚至优于。因此，他们的工作触发了自适应汽车安全应用。