We review the state of open science and the perspectives on open data sharing within the automotive user research community. Openness and transparency are critical not only for judging the quality of empirical research, but also for accelerating scientific progress and promoting an inclusive scientific community. However, there is little documentation of these aspects within the automotive user research community. To address this, we report two studies that identify (1) community perspectives on motivators and barriers to data sharing, and (2) how openness and transparency have changed in papers published at AutomotiveUI over the past 5 years. We show that while open science is valued by the community and openness and transparency have improved, overall compliance is low. The most common barriers are legal constraints and confidentiality concerns. Although research published at AutomotiveUI relies more on quantitative methods than research published at CHI, openness and transparency are not as well established. Based on our findings, we provide suggestions for improving openness and transparency, arguing that the motivators for open science must outweigh the barriers. All supporting materials are freely available at: https://osf.io/zdpek/

In this study, we focus on different strategies drivers use in terms of interleaving between driving and non-driving related tasks (NDRT) while taking back control from automated driving. We conducted two driving simulator experiments to examine how different cognitive demands of texting, priorities, and takeover time budgets affect drivers’ takeover strategies. We also evaluated how different takeover strategies affect takeover performance. We found that the choice of takeover strategy was influenced by the priority and takeover time budget but not by the cognitive demand of the NDRT. The takeover strategy did not have any effect on takeover quality or NDRT engagement but influenced takeover timing.

This paper reports results from a high-fidelity driving simulator study (N=215) about a head-up display (HUD) that conveys a conditional automated vehicle’s dynamic “uncertainty” about the current situation while fallback drivers watch entertaining videos. We compared (between-group) three design interventions: display (a bar visualisation of uncertainty close to the video), interruption (interrupting the video during uncertain situations), and combination (a combination of both), against a baseline (video-only). We visualised eye-tracking data to conduct a heatmap analysis of the four groups’ gaze behaviour over time. We found interruptions initiated a phase during which participants interleaved their attention between monitoring and entertainment. This improved monitoring behaviour was more pronounced in combination compared to interruption, suggesting pre-warning interruptions have positive effects. The same addition had negative effects without interruptions (comparing baseline & display). Intermittent interruptions may have safety benefits over placing additional peripheral displays without compromising usability.

We explore the transfer of control from an automated vehicle to the driver. Based on data from N=19 participants who participated in a driving simulator experiment, we find evidence that the transfer of control often does not take place in one step. In other words, when the automated system requests the transfer of control back to the driver, the driver often does not simply stop the non-driving task. Rather, the transfer unfolds as a process of interleaving the non-driving and driving tasks. We also find that the process is moderated by the length of time available for the transfer of control: interleaving is more likely when more time is available. Our interface designs for automated vehicles must take these results into account so as to allow drivers to safely take back control from automation.

A Hidden Markov Model framework is introduced to formalize the beliefs that humans may have about the mode in which a semi-automated vehicle is operating. Previous research has identified various “levels of automation,” which serve to clarify the different degrees of a vehicle’s automation capabilities and expected operator involvement. However, a vehicle that is designed to perform at a certain level of automation can actually operate across different modes of automation within its designated level, and its operational mode might also change over time. Confusion can arise when the user fails to understand the mode of automation that is in operation at any given time, and this potential for confusion is not captured in models that simply identify levels of automation. In contrast, the Hidden Markov Model framework provides a systematic and formal specification of mode confusion due to incorrect user beliefs. The framework aligns with theory and practice in various interdisciplinary approaches to the field of vehicle automation. Therefore, it contributes to the principled design and evaluation of automated systems and future transportation systems.

We review the history of human-automation interaction research, assess its current status and identify future directions. We start by reviewing articles that were published on this topic in the International Journal of Human-Computer Studies during the last 50 years. We find that over the years, automated systems have been used more frequently (1) in time-sensitive or safety-critical settings, (2) in embodied and situated systems, and (3) by non-professional users. Looking to the future, there is a need for human-automation interaction research to focus on (1) issues of function and task allocation between humans and machines, (2) issues of trust, incorrect use, and confusion, (3) the balance between focus, divided attention and attention management, (4) the need for interdisciplinary approaches to cover breadth and depth, (5) regulation and explainability, (6) ethical and social dilemmas, (7) allowing a human and humane experience, and (8) radically different human-automation interaction.

As vehicles of the future take on more of the driving responsibility and the role of the driver transitions into more of a monitoring capacity, the traditional notions of interruption and attention management needs to be reconsidered for automated vehicles. We argue that the transfer of control between the automated vehicle and the human driver can be considered as an interruption handling process, and that this process goes through a series of ten explicit stages. Each stage has its own characteristics and implications for practice and future research. Therefore, in this paper we identify for each stage what is known from theory, together with important implications for safety, design, and future research, especially for human-machine interaction. More generally, the framework makes explicit that it is not appropriate to think of transfer of control as a single event or even small set of events. The framework also highlights that it might not be realistic to expect human drivers to immediately respond correctly to a system initiated request to transfer control, given that humans interleave their attention between non-driving and driving tasks, and given that a transition constitutes of multiple stages. These nuances are accounted for in the framework.

Semi-autonomous vehicles occasionally require control to be handed over to the driver in situations where the vehicle is unable to operate safely. Currently, such handover requests require the driver to take control almost instantaneously. We investigate how auditory pre-alerts that occur well before the handover request impact the success of the handover in a dual task scenario. In a study with a driving simulator, drivers perform tasks on their phone while the car is in an autonomous mode. They receive a repeated burst audio pre-alert or an increasing pulse audio pre-alert preceding the standard warning for immediate handover. Results show that pre-alerts caused people to look more at the road before the handover occurred, and to disengage from the secondary task earlier, compared to when there was no pre-alert. This resulted in safer handover situations. Increasing pulse pre-alerts show particular promise due to their communication of urgency. Our detailed analysis informs the design and evaluation of alerts in safety-critical systems with automation.

Is there a difference in behavior when drivers start driving after parking compared to taking over from an autonomous driving car? In the former, the driving context switch (from static to driving) might be bigger than the latter, where drivers are already in a moving vehicle. This bigger difference might be paired with a decision to stop attending to any distracting task since drivers might find themselves in a different state after driving away. Participants drove a straight highway in a simulator. They either took over driving after being driven autonomously, or after being parked. Concurrently, we played distracting videos in the simulator. Participants looked more towards the road while the car was driving autonomously but there was no difference in driving performance and gazes towards the distraction after take-over compared to starting after parking. This implies that despite a difference in attention before takeover, the control switch is similar.

In-car devices that use audio output have been shown to be less distracting than traditional graphical user interfaces, but can be cumbersome and slow to use. In this paper, we report an experiment that demonstrates how these performance characteristics impact whether people will elect to use an audio interface in a multitasking situation. While steering a simulated vehicle, participants had to locate a source of information in a short passage of text. The text was presented either on a visual interface, or using a text-to-speech audio interface. The relative importance of each task was varied. A no-choice/choice paradigm was used in which participants first gained experience with each of the two interfaces, before being given a choice on which interface to use on later trials. The characteristics of the interaction with the interfaces, as measured in the no-choice phase, and the relative importance of each task, had an impact on which output modality was chosen in the choice phase. Participants that prioritized the secondary task tended to select the (faster yet more distracting) visual interface over the audio interface, and as a result had poorer lane keeping performance. This work demonstrates how a user's task objective will influence modality choices with multimodal devices in multitask environments.