Practical aspects of measuring camera-based indicators of alcohol intoxication in manual and automated driving

Abstract

Camera-based Driver Monitoring Systems (DMS) have the potential to exploit eye tracking correlates of alcohol intoxication to detect drunk driving. This study investigates how glance, blink, saccade, and fixation metrics are affected by alcohol, and whether possible effects remain stable across three different camera setups, as well as when the driver is out-of-the-loop during level 4 automated driving (Wizard-of-Oz setup). Thirty-five participants drove on a test track first sober and then with increasing intoxication levels reaching a breath alcohol concentration (BrAC) of 1‰. Linear Mixed-Effects Regression analyses showed that with increasing intoxication levels, eye blinks became longer and slower, glances and fixations became fewer and longer, and more attention was directed to the road area, at the expense of more peripheral areas. Fixation and blink metrics were more robust to changes in automation mode, whereas glance-based metrics were highly context dependent. Not all effects of alcohol intoxication could be measured with all eye tracking setups, where one-camera systems showed lower data availability and higher noise levels compared to a five-camera system. This means that lab findings based on higher quality eye tracking data might not be directly applied to production settings because of hardware limitations.