Driving emotion is considered as driver's psychological reaction to a change in traffic environment, which affects driver's cognitive, judgement and behaviour. In anxiety, drivers are more likely to get engaged in distracted driving, increasing the likelihood of vehicle crash. Therefore, it is essential to identify driver's anxiety during driving, to provide a basis for driving safety. This study used multiple-electrocardiogram (ECG) feature fusion to recognise driver's emotion, based on back-propagation network and Dempster–Shafer evidence method. The three features of ECG signals, the time–frequency domain, waveform and non-linear characteristics were selected as the parameters for emotion recognition. An emotion recognition model was proposed to identify drivers’ calm and anxiety during driving. The results show after ECG evidence fusion, the proposed model can recognise drivers’ emotion, with an accuracy rate of 91.34% for calm and 92.89% for anxiety. The authors’ findings of this study can be used to develop the personalised driving warning system and intelligent human–machine interaction in vehicles. This study would be of great theoretical significance and application value for improving road traffic safety.

中文翻译：

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基于BP网络和D–S证据的多心电图特征融合的驾驶员情感识别

驾驶情感被认为是驾驶员对交通环境变化的心理反应，会影响驾驶员的认知，判断和行为。在焦虑中，驾驶员更可能分心驾驶，增加了撞车的可能性。因此，至关重要的是识别驾驶过程中驾驶员的焦虑，为驾驶安全提供基础。这项研究基于反向传播网络和Dempster-Shafer证据方法，使用多心电图（ECG）特征融合来识别驾驶员的情绪。选择心电信号的三个特征，时频域，波形和非线性特征作为情感识别的参数。提出了一种情感识别模型来识别驾驶员在驾驶过程中的镇静和焦虑。结果表明，经心电图证据融合后，所提模型能够识别驾驶员的情绪，镇定准确率达91.34％，焦虑准确率达92.89％。这项研究的作者的发现可用于开发个性化的驾驶警告系统和车辆中智能的人机交互。该研究对提高道路交通安全性具有重要的理论意义和应用价值。