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.
Driver emotion recognition of multiple‐ECG feature fusion based on BP network and D–S evidence
IET Intelligent Transport Systems ; 14 , 8 ; 815-824
2020-08-01
10 pages
Article (Journal)
Electronic Resource
English
anxiety , ECG evidence fusion , driver information systems , backpropagation , road safety , psychology , ECG signals , multiple‐ECG feature fusion , road traffic , D–S evidence , emotion recognition model , multiple‐electrocardiogram feature fusion , road accidents , driving safety , distracted driving , back‐propagation network , emotion recognition , driving emotion , cognition , driver emotion recognition , Dempster–Shafer evidence method , traffic environment , electrocardiography , BP network , uncertainty handling , nonlinear characteristics , inference mechanisms , feature extraction , road traffic safety , driver psychological reaction , sensor fusion , time–frequency domain , personalised driving warning system , medical signal processing , vehicle crash
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