Driver distraction detection for vehicular monitoring

Jing Yang, Timothy N. Chang, Edwin Hou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


This paper describes a driver distraction detection scenario which is important to enhance driving safety. We employ data obtained by a GPS to reproduce the driver behavior. Gaussian Mixture model (GMM) is used to capture the sequence of driving characteristics according to the reconstructed vehicle's information and it is also used as a classifier to assign the driving behavior to normal or distraction category. In our work, we consider using a low cost 1Hz GPS receiver as the vehicle data acquisition equipment instead of the costly sensors (steering angle sensor, throttle/brake position sensor, etc). The nonlinear extended 2-wheel vehicle dynamic model is adopted in this study. Firstly, two states, i.e. the sideslip angle and the yaw rate are calculated since they are not available from GPS measurements. Secondly, a piecewise optimization scheme is proposed to reconstruct the driving behaviors which include the steering angle and the longitude force. Finally, a GMM classifier is applied to identify whether the driver is under distraction.

Original languageEnglish (US)
Title of host publicationProceedings - IECON 2010, 36th Annual Conference of the IEEE Industrial Electronics Society
Number of pages6
StatePublished - 2010
Event36th Annual Conference of the IEEE Industrial Electronics Society, IECON 2010 - Glendale, AZ, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

NameIECON Proceedings (Industrial Electronics Conference)


Other36th Annual Conference of the IEEE Industrial Electronics Society, IECON 2010
Country/TerritoryUnited States
CityGlendale, AZ

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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