In this paper, four typical driving behaviors with strong risk of cognitive distraction were obtained through questionnaire survey, and simulated driving experiments of normal driving and four typical driving behavior sub-tasks were carried out. Eight characteristic indicators are selected from the characteristic values of vehicle operation information, and a long-short-term memory neural network model (LSTM) is established to discriminate driving distraction, which is compared with SVM model. The results show that the LSTM model can accurately identify the cognitive distraction state of the driver.
Driver cognitive distraction recognition
Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022) ; 2022 ; Guangzhou,China
Proc. SPIE ; 12302
23.11.2022
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Driver cognitive distraction recognition
British Library Conference Proceedings | 2022
|British Library Online Contents | 1997
|Driver cognitive distraction detection: feature estimation and implementation
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