Attention and automation: New perspectives on mental underload and performance

There is considerable evidence in the ergonomics literature that automation can significantly reduce operator mental workload. Furthermore, reducing mental workload is not necessarily a good thing, particularly in cases where the level is already manageable. This raises the issue of mental underload, which can be at least as detrimental to performance as overload. However, although it is widely recognized that mental underload is detrimental to performance, there are very few attempts to explain why this may be the case. It is argued in this paper that, until the need for a human operator is completely eliminated, automation has psychological implications relevant in both theoretical and applied domains. The present paper reviews theories of attention, as well as the literature on mental workload and automation, to synthesize a new explanation for the effects of mental underload on performance. Malleable attentional resources theory proposes that attentional capacity shrinks to accommodate reductions in mental workload, and that this shrinkage is responsible for the underload effect. The theory is discussed with respect to the applied implications for ergonomics research

Attention and Task Engagement During Automated Driving

Many young drivers suffer fatal crashes each year in the United States at a rate approximately three times greater than more experienced drivers. Automated driving systems may serve to mitigate young drivers high crash rates but remain underexplored in research. This dissertation project examined the effects of levels of automation and interestingness of auditory clips on latent hazard anticipation in young drivers during simulated driving. Participants drove a vehicle at varying levels of vehicle automation (SAE Level 0, 2, or 3) in simulated scenarios, each containing a latent hazard event during which a boring, neutral, or interesting auditory clip was played. After completing all scenarios, participants completed an auditory stimuli recognition test and a questionnaire measuring the drivers’ calibration of their LHA performance. Results demonstrated that those in the L3 condition anticipated significantly fewer hazards than those in the L0 condition, corroborating previous research (Samuels et al., 2020). However, those in the L3 condition were also significantly poorer at anticipating latent hazards than those in the L2 condition, suggesting the importance of instruction on a drivers’ attentional allocation policy. A tradeoff was found between latent hazard anticipation and auditory recognition scores indicating the allocation of limited attentional resources as predicted by the Yamani and Horrey (2018) model. Interestingness of auditory stimuli had little to no effect on latent hazard anticipation. In general, automation may improve the multitasking ability of a young driver piloting L2 automation, but this benefit is lost for drivers of L3 automation. Instead, young drivers piloting L3 automation may anticipate latent hazards at rates as low as those observed in newly licensed drivers, and may be completely unaware of their failure to anticipate such hazards. The current research illustrates the criticality of user guidance when handling automated driving systems and serves as one step towards understanding the complex relationship between human drivers and automated systems

Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions

Previous research has indicated that high levels of vehicle automation can result in reduced driver situation awareness, but has also highlighted potential benefits of such future vehicle designs through enhanced safety and reduced driver workload. Well-designed automation allows drivers’ visual attention to be focused away from the roadway and toward secondary, in-vehicle tasks. Such tasks may be pleasant distractions from the monotony of system monitoring. This study was undertaken to investigate the impact of voluntary secondary task uptake on the system supervisory responsibilities of drivers experiencing highly-automated vehicle control. Independent factors of Automation Level (manual control, highly-automated) and Traffic Density (light, heavy) were manipulated in a repeated-measures experimental design. 49 drivers participated using a high-fidelity driving simulator that allowed drivers to see, hear and, crucially, feel the impact of their automated vehicle handling. Drivers experiencing automation tended to refrain from behaviours that required them to temporarily retake manual control, such as overtaking, resulting in an increased journey time. Automation improved safety margins in car following, however this was restricted to conditions of light surrounding traffic. Participants did indeed become more heavily involved with the in-vehicle entertainment tasks than they were in manual driving, affording less visual attention to the road ahead. This might suggest that drivers are happy to forgo their supervisory responsibilities in preference of a more entertaining highly-automated drive. However, they did demonstrate additional attention to the roadway in heavy traffic, implying that these responsibilities are taken more seriously as the supervisory demand of vehicle automation increases. These results may dampen some concerns over driver underload with vehicle automation, assuming vehicle manufacturers embrace the need for positive system feedback and drivers also fully appreciate their supervisory obligations in such future vehicle designs

Systematic Review of Driver Distraction in the Context of Advanced Driver Assistance Systems (ADAS) & Automated Driving Systems (ADS)

Advanced Vehicle Systems promise improved safety and comfort for drivers. Steady advancements in technology are resulting in increasing levels of vehicle automation capabilities, furthering safety benefits. In fact, some of these vehicle automation systems are already deployed and available, but with promised benefits, such systems can potentially change driving behaviors. There is evidence that drivers have increased secondary task engagements while driving with automated vehicle systems, but there is a need for a clearer scientific understanding of any potential correlations between the use of automated vehicle systems and potentially negative driver behaviors. Therefore, this thesis aims to understand the state of knowledge on automated vehicle systems and their possible impact on drivers’ distraction behaviors. I have conducted two systematic literature reviews to examine this question. This thesis reports these reviews and examines the effects of secondary task engagement on driving behaviors such as take-over times, visual attention, trust, and workload, and discusses the implications on driver safety

An Examination of Drivers’ Responses to Take-over Requests with Different Warning Systems During Conditional Automated Driving

Today, the autonomous vehicle industry is growing at a fast pace towards Level-5 autonomous cars, based on the Society of Automotive Engineers (SAE) definition, for customers. It is expected that there will soon be SAELevel-3 automated cars in the market–which corresponds to a plethora of research works in this sector and one of them is the study of the design of takeover request warning system because failure to respond a takeover request warning may lead to fatal accidents. The objective of this study is to examine the effects of different warning types on drivers’ takeover responses while they are engaging in different non-driving tasks during conditional automated driving. This study is a simulator-based with a mixed-subjects design while participants interacting with a simulated Level-3 automation system under different conditions. A total of 24 participants were recruited and participated in the study. Each participant experienced two types of takeover request (TOR )warning systems (Auditory TOR and Multimodal TOR) under four types of non-driving task conditions with two levels of non-driving task duration. One baseline drive without any secondary task was also designed for comparison with those conditions with non-driving tasks. Three research questions are addressed in this thesis: •Will a Multimodal TOR lead to better driver responses in reaction to takeover requests than Auditory TOR? •Will the different type of non-driving tasks lead to different cognitive engagement of drivers, therefore resulting in different reactions to takeover requests? Will different duration of engagement in non-driving tasks impact on responses of drivers’ re-engagement in driving tasks? In this study, data was collected for both objective driver measures through simulator run log files and subjective driver measures through questionnaires. For analysis purposes, a Mixed-Effects Model was conducted to test the response variables, followed by the Fisher LSD Pairwise Comparison test for significant factors with more than two levels and Two-Sample t-tests for subjective measures were used. Results showed that Multimodal TOR leads to shorter brake time and steer touching time comparatively and the difference of these dependent variables between the TORs is significant as p-value<0.05. The findings also suggest that the Multimodal TOR warning system leads to a better reaction of drivers. Moreover, it was also found that the type of non-driving tasks leads to different driver responses, more specifically, drivers have a significantly slower reaction towards the takeover request if they are engaging in visual-manual non-driving tasks when compared to if they are engaging in other types of non-driving tasks (e.g., cognitive or visual tasks). However, there are no significant gender-based effects observed for Brake Time and Steer Touch Time.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/152430/1/Kanishk Bakshi Final Thesis.pdfDescription of Kanishk Bakshi Final Thesis.pdf : Thesi

Driver’s perceptions about the effects of speed regulation systems in the driving task

Speed regulation systems like the cruise control (CC) and the speed limiter (SL) are becoming a standard feature in vehicles nowadays. However, these systems add a certain level of automation to the driving task and so they have the potential to change the way people drive. In order to understand the potential that these systems have in terms of road safety, it is crucial to understand how drivers percept the effects of the systems during the driving task. Then, the aim of the present research was to identify driver’s perceptions about the effects of speed regulation systems, more specifically the cruise control and the speed limiter, in the driving task and, to accomplish this goal, a questionnaire was applied. The main findings were that females are more prone to keep speeds equal to the road speed limit, and that when using both, cruise control and speed limiter, drivers are more available to comply with road speed limits. It was also found that the CC has a bigger impact than the SL when it comes to engaging into secondary tasks while driving

Were they in the loop during automated driving? Links between visual attention and crash potential

Background: A proposed advantage of vehicle automation is that it relieves drivers from the moment-to-moment demands of driving, to engage in other, non-driving related, tasks. However, it is important to gain an understanding of drivers’ capacity to resume manual control, should such a need arise. As automation removes vehicle control-based measures as a performance indicator, other metrics must be explored. Methods: This driving simulator study, conducted under the European Commission (EC) funded AdaptIVe project, assessed drivers’ gaze fixations during partially-automated (SAE Level 2) driving, on approach to critical and non-critical events. Using a between-participant design, 75 drivers experienced automation with one of five out-of-the-loop (OOTL) manipulations, which used different levels of screen visibility and secondary tasks to induce varying levels of engagement with the driving task: 1) no manipulation, 2) manipulation by light fog, 3) manipulation by heavy fog, 4) manipulation by heavy fog plus a visual task, 5) no manipulation plus an n-back task. Results: The OOTL manipulations influenced drivers’ first point of gaze fixation after they were asked to attend to an evolving event. Differences resolved within one second and visual attention allocation adapted with repeated events, yet crash outcome was not different between OOTL manipulation groups. Drivers who crashed in the first critical event showed an erratic pattern of eye fixations towards the road centre on approach to the event, while those who did not demonstrated a more stable pattern. Conclusions: Automated driving systems should be able to direct drivers’ attention to hazards no less than 6 seconds in advance of an adverse outcome

The comparison of auditory, tactile, and multimodal warnings for the effective communication of unexpected events during an automated driving scenario

In an automated car, users can fully engage in a distractor task, making it a primary task. Compared to manual driving, drivers can engage in tasks that are difficult to interrupt and of higher demand, the consequences can be a reduced perception of, and an impaired reaction to, warnings. In this study we compared three in-vehicle warnings (auditory, tactile, and auditory-tactile) which were presented during three highly attention capturing tasks (visual, auditory, and tactile) while the user was engaged in a self-driving car scenario, culminating in an emergency brake event where the warning was presented. The novel addition for this paper was that three set paced, attention capturing tasks, as well the three warnings were all designed in a pilot study to have comparable workload and noticeability. This enabled a direct comparison of human performance to be made between each of the attention capturing tasks, which are designed to occupy only one specific modality (auditory, visual or haptic), but remain similar in overall task demand. Results from the study showed reaction times to the tactile warning (for the emergency braking event) were significantly slower compared to the auditory and auditory-tactile (aka multimodal or multisensory) warning. Despite the similar reaction times between the in-vehicle auditory warning and the multimodal warning, the multimodal warning led to a reduced number of missed warnings and fewer false responses. However, the auditory and auditory-tactile warnings were rated significantly more startling than the tactile alone. Our results extend the literature regarding the performance benefits of multimodal warnings by comparing them with in-vehicle auditory warnings in an autonomous driving context. The set-pace attention capturing tasks in this study would be of interest to other researchers to evaluate the interaction in an automated driving context, particularly with hard to interrupt and attention capturing tasks