Drowsy driving is a pervasive problem among drivers, and is also an important contributor to motor vehicle accidents. It is very important to be able to estimate a driver's drowsiness level online so that preventative actions could be taken to avoid accidents. However, because of large individual differences, it is very challenging to design an estimation algorithm whose parameters fit all subjects. Some subject-specific calibration data must be used to tailor the algorithm for each new subject. This paper proposes a domain adaptation with model fusion (DAMF) online drowsiness estimation approach using EEG signals. By making use of EEG data from other subjects in a transfer learning framework, DAMF requires very little subject-specific calibration data, which significantly increases its utility in practice. We demonstrate using a simulated driving experiment and 15 subjects that DAMF can achieve much better performance than several other approaches.
['Dongrui Wu', 'Chun-Hsiang Chuang', 'Chin-Teng Lin']
Online driver's drowsiness estimation using domain adaptation with model fusion