Tuesday, November 6, 2012

Anthony McDonald Takes Top Honors at HFES 2012 Annual Meeting

STTG member Anthony McDonald received dual honors at the 2012 HFES annual meeting for his lead role in the paper titled “Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle.” After being named Best Student Paper by the Surface Transportation Technical Group, the article also won the Alphonse Chapanis award, the top honor for student-authored submissions to the conference. The paper’s co-authors are Chris Schwarz, John Lee, and Timothy Brown. An abstract is included below.

Tony is a graduate student in the School of Industrial & Systems Engineering at the University of Wisconsin–Madison. You can learn more about him at his website, https://mywebspace.wisc.edu/admcdonald/web/.


Real-Time Detection of Drowsiness Related Lane Departures
Using Steering Wheel Angle

Anthony D. McDonald (1), Chris Schwarz (2), John D. Lee (1), Timothy L. Brown (2)
(1) University of Wisconsin-Madison, Madison, WI, USA
(2) National Advanced Driving Simulator, Iowa City, IA, USA


Drowsy driving is a significant factor in many motor vehicle crashes in the United States and across the world. Efforts to reduce these crashes have developed numerous algorithms to detect both acute and chronic drowsiness. These algorithms employ behavioral and physiological data, and have used different machine learning techniques. This work proposes a new approach for detecting drowsiness related lane departures, which uses unfiltered steering wheel angle data and a random forest algorithm. Using a data set from the National Advanced Driving Simulator the algorithm was compared with a commonly used algorithm, PERCLOS and a simpler algorithm constructed from distribution parameters. The random forest algorithm had higher accuracy and Area Under the receiver operating characteristic Curve (AUC) than PERCLOS and had comparable positive predictive value. The results show that steering-angle can be used to predict drowsiness related lane-departures six seconds before they occur, and suggest that the random forest algorithm, when paired with an alert system, could significantly reduce vehicle crashes.