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• Participants viewed animations of take-over scenarios. • Animations had different lengths and featured a hazard or no hazard. • Participants had to rebuild the traffic situation, an index of situation awareness. • Hazards did not significantly affect situation awareness scores, except for the shortest animations. • Eye-tracking results suggest that situation awareness was impaired due to the 'weapon-focus' effect. In highly automated driving, drivers occasionally need to take over control of the car due to limitations of the automated driving system. Research has shown that visually distracted drivers need about 7 s to regain situation awareness (SA). However, it is unknown whether the presence of a hazard affects SA. In the present experiment, 32 participants watched animated video clips from a driver's perspective while their eyes were recorded using eye-tracking equipment. The videos had lengths between 1 and 20 s and contained either no hazard or an impending crash in the form of a stationary car in the ego lane. After each video, participants had to (1) decide (no need to take over, evade left, evade right, brake only), (2) rate the danger of the situation, (3) rebuild the situation from a top-down perspective, and (4) rate the difficulty of the rebuilding task. The results showed that the hazard situations were experienced as more dangerous than the non-hazard situations, as inferred from self-reported danger and pupil diameter. However, there were no major differences in SA: hazard and non-hazard situations yielded equivalent speed and distance errors in the rebuilding task and equivalent self-reported difficulty scores. An exception occurred for the shortest time budget (1 s) videos, where participants showed impaired SA in the hazard condition, presumably because the threat inhibited participants from looking into the rear-view mirror. Correlations between measures of SA and decision-making accuracy were low to moderate. It is concluded that hazards do not substantially affect the global awareness of the traffic situation, except for short time budgets. [ABSTRACT FROM AUTHOR]

The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control, longitudinal control, and monitoring) between the driver and the automation. A transition in automated driving is defined as the process during which the human-automation system changes from one driving state to another, with transitions of monitoring activity and transitions of control being among the possibilities. Based on ‘Is the transition required?’, ‘Who initiates the transition?’, and ‘Who is in control after the transition?’, we define six types of control transitions between the driver and automation: (1) Optional Driver-Initiated Driver-in-Control, (2) Mandatory Driver-Initiated Driver-in-Control, (3) Optional Driver-Initiated Automation-in-Control, (4) Mandatory Driver-Initiated Automation-in-Control, (5) Automation-Initiated Driver-in-Control, and (6) Automation-Initiated Automation-in-Control. Use cases per transition type are introduced. Finally, we interpret previous experimental studies on transitions using our framework and identify areas for future research. We conclude that our framework of driving states and transitions is an important complement to the levels of automation proposed by transportation agencies, because it describes what the driver and automation are doing, rather than should be doing, at a moment of time. [ABSTRACT FROM AUTHOR]