Evaluating Drivers' States in Sleepiness Countermeasures Experiments Using Physiological and Eye Data - Hybrid Logistic and Linear Regression Model

Objective sleepiness evaluation is essential for the effect analysis of countermeasures for driver sleepiness, such as in-car stimulants. Furthermore, measuring drivers’ sleepiness in simulator studies also becomes important when investigating causes for task-related sleepiness, for example driving on monotonous routes, which requires little driver engagement. To evaluate driver sleepiness and the effect of countermeasures, the authors developed a model for predicting sleepiness using both simple logistic and linear regression of heart rate variability, skin conductance and pupil diameter. The algorithm was trained and tested with data from 88 participants in driving simulator studies. A prediction accuracy of 77% was achieved and the model’s sensitivity to thermal stimulation was shown.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: pp 284-290
  • Monograph Title: Driving Assessment 2017: Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design

Subject/Index Terms

Filing Info

  • Accession Number: 01666957
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 17 2018 3:08PM