Differentiating Alcohol-Induced Driving Behavior Using Steering Wheel Signals

Detection of alcohol-induced driving impairment through vehicle-based sensor signals is of paramount importance for road safety. To differentiate the driving conditions with and without alcohol-induced impairment, data were collected from 108 drivers under both conditions in a high-fidelity driving simulator. With this data set, various quantitative measures of steering wheel movement, including not only simple statistics such as the mean and the standard deviation but nonlinear dynamic invariant measures such as sample entropy and Lyapunov exponent as well, are compared in terms of their differentiating capabilities. Nonlinear invariant measures are more robust and consistent than the simple measures in differentiating the impairment. Furthermore, people respond to alcohol-induced impairment quite differently, and for a certain group of people, the alcohol-induced impairment can be well detected using these nonlinear invariant measures. Many interesting insights into characterizing the effect of alcohol on driving behavior are obtained in this paper. This paper lays a foundation for the future development of a real-time detection method for alcohol-induced impairment.