A driving simulator study was designed to investigate drivers’ ability to resume control from a highly automated vehicle in two conditions: (i) when automation was switched off and manual control was required at a system-based, regular interval and (ii) when transition to manual was based on the length of time drivers were looking away from the road ahead. In addition to studying the time it took drivers to successfully resume control from the automated system, eye tracking data were used to observe visual attention to the surrounding environment and the pattern of drivers’ eye fixations as manual control was resumed in the two conditions. Results showed that drivers’ pattern of eye movement fixations remained variable for some time after automation was switched off, if disengagement was actually based on drivers’ distractions away from the road ahead. When disengagement was more predictable and system-based, drivers’ attention towards the road centre was higher and more stable. Following a lag of around 10s, drivers’ lateral control of driving and steering corrections (as measured by SDLP and high frequency component of steering, respectively) were more stable when transition to manual control was predictable and based on a fixed time. Whether automation transition to manual was based on a fixed or variable interval, it took drivers around 35–40s to stabilise their lateral control of the vehicle. The results of this study indicate that if drivers are out of the loop due to control of the vehicle in a limited self-driving situation (Level 3 automation), their ability to regain control of the vehicle is better if they are expecting automation to be switched off. As regular disengagement of automation is not a particularly practical method for keeping drivers in the loop, future research should consider how to best inform drivers of their obligation to resume control of driving from an automated system

Objective: A driving simulator study compared the effect of changes in workload on performance in manual and highly automated driving. Changes in driver state were also observed by examining variations in blink patterns. Background: With the addition of a greater number of advanced driver assistance systems in vehicles, the driver’s role is likely to alter in the future from an operator in manual driving to a supervisor of highly automated cars. Understanding the implications of such advancements on drivers and road safety is important. Method: A total of 50 participants were recruited for this study and drove the simulator in both manual and highly automated mode. As well as comparing the effect of adjustments in driving-related workload on performance, the effect of a secondary Twenty Questions Task was also investigated. Results: In the absence of the secondary task, drivers’ response to critical incidents was similar in manual and highly automated driving conditions. The worst performance was observed when drivers were required to regain control of driving in the automated mode while distracted by the secondary task. Blink frequency patterns were more consistent for manual than automated driving but were generally suppressed during conditions of high workload. Conclusion: Highly automated driving did not have a deleterious effect on driver performance, when attention was not diverted to the distracting secondary task. Application: As the number of systems implemented in cars increases, an understanding of the implications of such automation on drivers’ situation awareness, workload, and ability to remain engaged with the driving task is important.

Objective: The study was designed to show how driver attention to the road scene and engagement of a choice of secondary tasks are affected by the level of automation provided to assist or take over the basic task of vehicle control. It was also designed to investigate the difference between support in longitudinal control and support in lateral control. Background: There is comparatively little literature on the implications of automation for drivers’ engagement in the driving task and for their willingness to engage in non-driving-related activities. Method: A study was carried out on a high-level driving simulator in which drivers experienced three levels of automation: manual driving, semiautomated driving with either longitudinal or lateral control provided, and highly automated driving with both longitudinal and lateral control provided. Drivers were free to pay attention to the roadway and traffic or to engage in a range of entertainment and grooming tasks. Results: Engagement in the nondriving tasks increased from manual to semiautomated driving and increased further with highly automated driving. There were substantial differences in attention to the road and traffic between the two types of semiautomated driving. Conclusion: The literature on automation and the various task analyses of driving do not currently help to explain the effects that were found. Lateral support and longitudinal support may be the same in terms of levels of automation but appear to be regarded rather differently by drivers.

Despite an abundant use of the term “Out of the loop” (OOTL) in the context of automated driving and human factors research, there is currently a lack of consensus on its precise definition, how it can be measured, and the practical implications of being in or out of the loop during automated driving. The main objective of this paper is to consider the above issues, with the goal of achieving a shared understanding of the OOTL concept between academics and practitioners. To this end, the paper reviews existing definitions of OOTL and outlines a set of concepts, which, based on the human factors and driver behaviour literature, could serve as the basis for a commonly-agreed definition. Following a series of working group meetings between representatives from academia, research institutions and industrial partners across Europe, North America, and Japan, we suggest a precise definition of being in, out, and on the loop in the driving context. These definitions are linked directly to whether or not the driver is in physical control of the vehicle, and also the degree of situation monitoring required and afforded by the driver. A consideration of how this definition can be operationalized and measured in empirical studies is then provided, and the paper concludes with a short overview of the implications of this definition for the development of automated driving functions.

Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees’ statements – which appear to be in line with a long history of HFs research – and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.