Inferring Takeover in SAE Level 2 Automated Vehicles Using Driver-Based Behavioral and Psychophysiological Signals

Abstract

The prevalence of Level 2 vehicle automation on U.S. roadways is increasing. As such, drivers are responsible for monitoring the automation and taking over control as necessary. However, it remains unclear when a driver may begin to exhibit behavioral responses that could indicate their intention to takeover. In this paper, we use an exhaustive approach to determine the features that best predict takeover, along with the time windows over which those features should be sampled. Specifically, we consider features that can be measured in real time and that are predominantly driver-based, including both behavioral and psychophysiological features. The resulting analysis highlights pupil diameter as the most significant predictor of takeover behavior. Finally, investigation into feature extraction windows indicates that window size may be feature-specific, and may not generalize across features of the same modality. These results have significance for what types of sensors should be chosen for takeover prediction in L2 automated vehicles in which real-time takeover prediction is of interest.