A difficult challenge for today’s driver monitoring systems is the detection of cognitive distraction. The present research presents the development of a theory-driven approach for cognitive distraction detection during manual driving based on temporal control theories. It is based solely on changes in the temporal variance of driving-relevant gaze behavior, such as gazes onto the dashboard (TDGV). Validation of the detection method happened in a field and in a simulator study by letting participants drive, alternating with and without a secondary task inducing external cognitive distraction (auditory continuous performance task). The general accuracy of the distraction detection method varies between 68% and 81% based on the quality of an individual prerecorded baseline measurement. As a theory-driven system, it represents not only a step towards a sophisticated cognitive distraction detection method, but also explains that changes in temporal dashboard gaze variance (TDGV) are a useful behavioral indicator for detecting cognitive distraction.

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A critical challenge of higher levels of automated driving (SAE level 3) is the reengagement of the driver to take back manual control. In this relatively novel mode of automated driving that is slowly becoming commercially available, the driver can perform non-driving related tasks but has to take over when the vehicle reaches the boundaries for its design domain for automated driving. The challenge here is for the driver to build sufficient situation awareness of the vehicle and the environment before taking back control in a timely and safe manner. In this study we investigated to what extent the driver could be helped by receiving predictive information about the duration of the automated driving as well as the available time for the reengagement.To address this research question, we conducted a simulator study where 41 participants drove alternating in manual mode and automated mode. Multiple times the participants had to take back control of driving prior to a stationary vehicle that blocked the lane. Audiovisual cues informed the participants about the necessary take-over 15 seconds in advance. The cockpit display showed the current driving mode (automated versus manual), as well as one of four types of prediction information: a) The baseline display type showed no prediction of time at all, b) the transition prediction (TP) display type showed the available time for the take-over, c) the automated driving prediction (AP) display type showed a remaining time during automated driving, and d) the combined display type showed both types of information (TP and AP). We compared the perceived usefulness of the prediction types in a questionnaire, the gaze behavior during control transition/automated driving segments as well as driving performance.The results indicate that the combined display type was perceived to be highly useful by the participants when transitioning control from automated to manual driving. This perceived usefulness is positively associated with their intention to use such a system in their daily lives. An analysis of the driver’s gaze indicates that drivers used the combined display type during takeovers and automated driving more than the other display types. Furthermore, drivers apparently acquired over time a safer gaze behavior with the combined display type as they monitored the road environment more during control transitions than in the other conditions. This effect was only present after a couple of takeovers, showing a gradual increase, which indicates that it requires some learning to fully utilize the time predictions.The results imply that a combined display type of predictive information about the duration of automated driving and reengagement time was perceived to be highly useful by the participants. In this paper we describe the results along with performance results and a comprehensive assessment with implications for further research.