Humans and Automation: Use, Misuse, Disuse, Abuse

Parasuraman, Raja; Riley, Victor

doi:10.1518/001872097778543886

Cited by 2,959 publications

(2,054 citation statements)

References 52 publications

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“…For example, Payre, Cestac, and Delhomme (2016) found a higher take-over time with increasing trust in an emergency situation if training was insufficient. Consistent across different levels of automation, inappropriate levels of trust lead to extended reaction times or poorer reaction quality in hazardous situations (Abe, Itoh, & Tanaka, 2002;McGuirl & Sarter, 2006;Parasuraman & Riley, 1997). The causal mechanism could lie in participants' monitoring strategy: Muir and Moray (1996) as well as Bagheri and Jamieson (2004) found a decrease in monitoring with increasing trust.…”

Section: The Role Of Trust In Automated Drivingmentioning

confidence: 99%

“…This act of reliance is only performed if the driver trusts driving automation enough to completely hand over the driving task. However, trust predicts not only whether an automated system is used but also how it is used: Parasuraman and Riley (1997) categorized the interaction with automation into four styles which can be linked to operator's trust in automation. Among Use, Disuse, Abuse, the authors highlight the negative effects of Misuse, inappropriate overtrust when the operator's trust exceeds the automated system's capabilities.…”

Section: The Role Of Trust In Automated Drivingmentioning

confidence: 99%

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Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and takeover performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

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Section: The Role Of Trust In Automated Drivingmentioning

confidence: 99%

Section: The Role Of Trust In Automated Drivingmentioning

confidence: 99%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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“…These problems have been associated with various deficiencies in human operator states, including vigilance decrements, complacency and loss of situation awareness (SA), which have also been discussed at length in numerous studies (cf. Endsley 1987, Wiener 1988, Endsley and Kiris 1995, Parasuraman and Riley 1997. In general, a key underlying factor that has emerged as a contributor to human performance problems in complex, automated systems control is human out-of-the-loop (OOTL) performance (see Young 1969, Kessel andWickens 1982).…”

Section: Introductionmentioning

confidence: 99%

The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task

Kaber

Endsley

2004

Theoretical Issues in Ergonomics Science

594

387

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This paper extends previous research on two approaches to human-centred automation: (1) intermediate levels of automation (LOAs) for maintaining operator involvement in complex systems control and facilitating situation awareness; and (2) adaptive automation (AA) for managing operator workload through dynamic control allocations between the human and machine over time. Some empirical research has been conducted to examine LOA and AA independently, with the objective of detailing a theory of human-centred automation. Unfortunately, no previous work has studied the interaction of these two approaches, nor has any research attempted to systematically determine which LOAs should be used in adaptive systems and how certain types of dynamic function allocations should be scheduled over time. The present research briefly reviews the theory of humancentred automation and LOA and AA approaches. Building on this background, an initial study was presented that attempts to address the conjuncture of these two approaches to human-centred automation. An experiment was conducted in which a dual-task scenario was used to assess the performance, SA and workload effects of low, intermediate and high LOAs, which were dynamically allocated (as part of an AA strategy) during manual system control for various cycle times comprising 20, 40 and 60% of task time. The LOA and automation allocation cycle time (AACT) combinations were compared to completely manual control and fully automated control of a dynamic control task performed in conjunction with an embedded secondary monitoring task. Results revealed LOA to be the driving factor in determining primary task performance and SA. Low-level automation produced superior performance and intermediate LOAs facilitated higher SA, but this was not associated with improved performance or reduced workload. The AACT was the driving factor in perceptions of primary task workload and secondary task performance. When a greater percentage of primary task time was automated, operator perceptual resources were freed-up and monitoring performance on the secondary task improved. Longer automation cycle times than have previously been studied may have benefits for overall human-machine system performance. The combined effect of LOA and AA on all measures did not appear to be 'additive' in nature. That is, the LOA producing the best performance (low level automation) did not do so at the AACT, which produced superior performance (maximum cycle time). In general, the results are supportive of intermediate LOAs and AA as approaches to human-centred automation, but each appears to provide different benefits to human-machine system performance. This work provides additional information for a developing theory of human-centred automation.

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“…Automation, defined as the performance of tasks by machines (often computers) rather than human operators (Parasuraman & Riley, 1997), continues to be deployed in various industrial settings in order to increase efficiency and reduce variability. Cited benefits include the reduction of operator workload and error coupled with a reduction in labour costs (Dekker, 2004;Hollnagel, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…These benefits make automation very attractive to businesses wishing to increase efficiency while reducing costs. Numerous lab-based studies in the field of human factors have been undertaken to investigate the effects of automation and these have often found the benefits to be less clear-cut than might be expected (Parasuraman & Riley, 1997). For example, situation awareness may be reduced under high levels of automation (Kaber & Endsley, 2004) and workload may be increased under abnormal circumstances (Kantowitz, 1994).…”

Section: Introductionmentioning

confidence: 99%

Impact of automation: Measurement of performance, workload and behaviour in a complex control environment

2015

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This paper describes an experiment that was undertaken to compare three levels of automation in rail signalling; a high level in which an automated agent set routes for trains using timetable information, a medium level in which trains were routed along pre-defined paths, and a low level where the operator (signaller) was responsible for the movement of all trains. These levels are described in terms of a rail automation model based on previous automation theory (Parasuraman, Sheridan, & Wickens, 2000). Performance, subjective workload, and signaller activity were measured for each level of automation running under both normal operating conditions and abnormal, or disrupted, conditions. The results indicate that perceived workload, during both normal and disrupted phases of the experiment, decreased as the level of automation increased and performance was most consistent (i.e. showed the least variation between participants) with the highest level of automation. The results give a strong case in favour of automation, particularly in terms of demonstrating the potential for automation to reduce workload, but also suggest much benefit can achieved from a mid-level of automation potentially at a lower cost and complexity. Impact StatementResearch in the area of automation, and in particular in the examination of human interaction with different levels of automation, has normally been undertaken in Balfe et al, Impact of Automation: Measurement of Performance, Workload and Behaviour in a Complex Control Environment 2 laboratory settings using simple tasks and naïve participants where the level of automation can be easily manipulated. This research was undertaken with expert participants using complex simulation of three ecologically valid levels of automation and provides empirical field validation of some of the results found in laboratory studies.

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Humans and Automation: Use, Misuse, Disuse, Abuse

Cited by 2,959 publications

References 52 publications

Introduction matters: Manipulating trust in automation and reliance in automated driving

Introduction matters: Manipulating trust in automation and reliance in automated driving

The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task

Impact of automation: Measurement of performance, workload and behaviour in a complex control environment

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