Steering Wheel Behavior Based Estimation of Fatigue
This paper examined a steering behavior based fatigue monitoring system. The advantages of using steering behavior for detecting fatigue are that these systems measure continuously, cheaply, non-intrusively, and robustly even under extremely demanding environmental conditions. The expected fatigue induced changes in steering behavior are a pattern of slow drifting and fast corrective counter steering. Using advanced signal processing procedures for feature extraction, we computed 3 feature set in the time, frequency and state space domain (a total number of 1251 features) to capture fatigue impaired steering patterns. Each feature set was separately fed into 5 machine learning methods (e.g. Support Vector Machine, K-Nearest Neighbor). The outputs of each single classifier were combined to an ensemble classification value. Finally we combined the ensemble values of 3 feature subsets to a of meta-ensemble classification value. To validate the steering behavior analysis, driving samples are taken from a driving simulator during a sleep deprivation study (N=12). We yielded a recognition rate of 86.1% in classifying slight from strong fatigue.
How to Cite:
Krajewski, J. & Sommer, D. & Trutschel, U. & Edwards, D. & Golz, M., (2009) “Steering Wheel Behavior Based Estimation of Fatigue”, Driving Assessment Conference 5(2009), 118-124. doi: https://doi.org/10.17077/drivingassessment.1311
Rights: Copyright © 2009 the author(s)