Non-intrusive driver drowsiness monitoring via artificial neural networks

J. Culp, M. El-Gindy, Md Amanul Haque

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


In this paper, a completely non-intrusive method of monitoring driver drowsiness is described. Because of their abilities to learn behavior and represent very complex relationships, artificial neural networks are the basis of the method presented. Four artificial neural networks are designed based on the hypothesis that the time derivative of force (jerk) exerted by the driver at the steering wheel and accelerator pedal can be used to discern levels of alertness. The artificial neural networks are trained to replicate non-drowsy input, and then tested with unseen data. Data sets that are similar to the training sets will pass through the network with little change, and sets that are different will be changed considerably by the network. Thus, the further the driver's jerk profile deviates from the non-drowsy jerk profile, the greater the error between the input and output of the network will be. The changes in network error with drive time are presented from testing the networks with simulated driving data and the performance of the artificial neural network designs are compared.

Original languageEnglish (US)
StatePublished - Dec 1 2008
Event2008 World Congress - Detroit, MI, United States
Duration: Apr 14 2008Apr 17 2008


Other2008 World Congress
Country/TerritoryUnited States
CityDetroit, MI

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Non-intrusive driver drowsiness monitoring via artificial neural networks'. Together they form a unique fingerprint.

Cite this