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Drivers of automated cars may occasionally need to take back manual control after a period of inattentiveness. At present, it is unknown how long it takes to build up situation awareness of a traffic situation. In this study, 34 participants were presented with animated video clips of traffic situations on a three-lane road, from an egocentric viewpoint on a monitor equipped with eye tracker. Each participant viewed 24 videos of different durations (1, 3, 7, 9, 12, or 20 s). After each video, participants reproduced the end of the video by placing cars in a top-down view, and indicated the relative speeds of the placed cars with respect to the ego-vehicle. Results showed that the longer the video length, the lower the absolute error of the number of placed cars, the lower the total distance error between the placed cars and actual cars, and the lower the geometric difference between the placed cars and the actual cars. These effects appeared to be saturated at video lengths of 7-12 s. The total speed error between placed and actual cars also reduced with video length, but showed no saturation up to 20 s. Glance frequencies to the mirrors decreased with observation time, which is consistent with the notion that participants first estimated the spatial pattern of cars after which they directed their attention to individual cars. In conclusion, observers are able to reproduce the layout of a situation quickly, but the assessment of relative speeds takes 20 s or more. [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]