原文传递
Driver Emotion Recognition Involving Multimodal Signals: Electrophysiological Response, Nasal-Tip Temperature, and Vehicle Behavior
| 题名： | Driver Emotion Recognition Involving Multimodal Signals: Electrophysiological Response, Nasal-Tip Temperature, and Vehicle Behavior |
| 正文语种： | eng |
| 作者： | Jie Ni;Wanying Xie;Yiping Liu;Jike Zhang;Yugu Wan;Huimin Ge |
| 作者单位： | Dept. of Automobile and Traffic Engineering Jiangsu Univ. Zhenjiang Jiangsu 212013 China;Dept. of Automobile and Traffic Engineering Jiangsu Univ. Zhenjiang Jiangsu 212013 China;Dept of Automobile and Traffic Engineering Jiangsu Univ. Zhenjiang Jiangsu 212013 China;Dept. of Transportation and Logistics Engineering Wuhan Univ. of Technology Wuhan Hubei 430063 China;Dept of Automobile and Traffic Engineering Jiangsu Univ. Zhenjiang Jiangsu 212013 China;Dept. of Automobile and Traffic Engineering Jiangsu Univ. Zhenjiang Jiangsu 212013 China |
| 关键词： | Driver emotion; Emotion recognition; Multimodal parameters; Machine learning; Human-computer interaction (HCI) |
| 摘要： | Accurate driver emotion recognition is one of the key challenges in the construction of an intelligent vehicle safety assistant system. In this paper, we conduct a driving simulator study on driver emotion recognition. Taking the car-following scene as an example, the multimodal parameters of a driver in the five emotional states of neutral, joy, fear, sadness, and anger are obtained from the emotion induction experiment and the simulated driving experiment. Wavelet denoising and debase processing are used to reduce the influence of signal noise and the individual differences between drivers. The statistical domain and the time-frequency domain features of the electrophysiological response signals, nasal-tip temperature signals, and vehicle behavior signals are analyzed. The factor analysis method is used to extract and reduce the feature parameters, and the driver's emotion recognition model is established based on machine learning methods such as random forest (RF), K-nearest-neighbor (KNN), and extreme gradient boosting (XGBoost). Through the verification and the comparison of different modalities and different modality combinations with different machine learning methods, the RF model, based on the feature combination of three types of modal data, has the best model recognition effect. The research results can provide a theoretical basis for driver emotion recognition of intelligent vehicles and have positive significance for promoting the development of human-computer interaction (HCI) systems of intelligent vehicles and improving road traffic safety. |
| 出版年： | 2024 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 150 |
| 期： | 1 |
| 页码： | 04023125.1-04023125.11 |
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