Wang Yi, Zhou Xin, Yang Yang, Zhang Wei
National Key Laboratory of Human Factors Engineering, Department of Industrial Engineering, Tsinghua University, Beijing, China.
School of Economics and Management, Beihang University, Beijing, China.
Ergonomics. 2024 Oct 22:1-20. doi: 10.1080/00140139.2024.2418303.
Driving anger is a serious global issue that poses risks to road safety, thus necessitating the development of effective detection and intervention methods. This study investigated the feasibility of using smartphones to capture facial expressions to detect event-related driving anger. Sixty drivers completed the driving tasks in scenarios with and without multi-stage road events and were induced to angry and neutral states, respectively. Their physiological signals, facial expressions, and subjective data were collected. Four feature combinations and six machine learning algorithms were used to construct driving anger detection models. The model combining facial features and the XGBoost algorithm outperformed models using physiological features or other algorithms, achieving an accuracy of 87.04% and an F1-score of 85.06%. Eyes, mouth, and brows were identified as anger-sensitive facial areas. Additionally, incorporating individual characteristics into models further improved classification performance. This study provides a contactless and highly accessible approach for event-related driving anger detection. This study proposed a cost-effective and contactless approach for event-related and real-time driving anger detection and could potentially provide insights into the design of emotional interactions in intelligent vehicles.
驾驶愤怒是一个严重的全球性问题，对道路安全构成风险，因此需要开发有效的检测和干预方法。本研究调查了使用智能手机捕捉面部表情以检测与事件相关的驾驶愤怒的可行性。60名驾驶员在有和没有多阶段道路事件的场景中完成驾驶任务，并分别被诱导进入愤怒和中立状态。收集了他们的生理信号、面部表情和主观数据。使用四种特征组合和六种机器学习算法构建驾驶愤怒检测模型。结合面部特征和XGBoost算法的模型优于使用生理特征或其他算法的模型，准确率达到87.04%，F1分数达到85.06%。眼睛、嘴巴和眉毛被确定为对愤怒敏感的面部区域。此外，将个体特征纳入模型进一步提高了分类性能。本研究为与事件相关的驾驶愤怒检测提供了一种非接触式且高度可及的方法。本研究提出了一种经济高效的非接触式方法用于与事件相关的实时驾驶愤怒检测，并可能为智能车辆中的情感交互设计提供见解。
