Man's Role in Control Systems

Bibby, K.S.; Margulies, F.; Rijnsdorp, J.E.; Withers, R.M.J.; Makarov, I. M.

doi:10.1016/s1474-6670(17)67612-2

Cited by 27 publications

(7 citation statements)

References 6 publications

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“…Evolving technical systems and an increasing spiral of automation ensues, which ultimately reflects complex processes of procedural substitution (Wahlström 1995). Bibby et al (1975) write:…”

Section: Alternative Strategies To Shipboard Proceduresmentioning

confidence: 99%

Shipboard operating and maintenance procedures and the knowledge gap

Doherty

2016

Journal of Marine Engineering & Technology

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This paper advocates the use of shipboard procedures as a strategy for bridging the knowledge gap. It also recognises that an over-reliance reflects a failure to equip seafarers with the appropriate prior knowledge. Maritime accident reports frequently cite poor procedures as contributing factors towards maritime accidents. This may suggest a nexus among technical systems, seafarer competence, shipboard procedures and accidents at sea. It is suggested in this paper that a holistic approach is needed to ensure that the approval of technical systems, seafarer training and written procedures are inextricably linked. Shipboard written procedures are described in terms of a negotiation of meaning between writer and seafarer, where successful execution of a shipboard task requires an accurate interpretation, by the seafarer, of the writer's intentions. It is suggested that this may only be achieved by ensuring that seafarer prior knowledge is sufficient, writer's anticipation of this knowledge is accurate and communication of new knowledge is explicit. This paper does not offer specific advice on the design of shipboard procedures; however, the process of acquiring a mental action plan is discussed. The factors that support or impede this acquisition are described and supported by a number of accident reports and academic studies.

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Section: Alternative Strategies To Shipboard Proceduresmentioning

confidence: 99%

Shipboard operating and maintenance procedures and the knowledge gap

Doherty

2016

Journal of Marine Engineering & Technology

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“…Previous human factors research indicates that automation resolves the imprecision and variability of human task performance, but also yields new types of safety concerns. It has been found that a high level of automation can cause out-of-the-loop problems such as complacency, skill degradation, mental underload (when the automation functions reliably), mental overload (when the operator suddenly needs to solve an automation-induced problem), and loss of situation awareness (Bainbridge, 1983;Bibby, Margulies, Rijnsdorp, & Withers, 1975;Endsley & Kiris, 1995;Hancock et al, 2013;Kaber & Endsley, 1997;Parasuraman & Riley, 1997;Vlakveld, 2015), which are issues that have also been implicated in the domain of automated driving (De Winter, Happee, Martens, & Stanton, 2014;Seppelt & Victor, 2016;Young & Stanton, 2002). Recently, a meta-analysis of 18 experiments on human-automation interaction found statistical support for the so-called lumberjack hypothesis, which postulates that as the degree of automation increases, the side effects of automation (e.g., performance impairment if the automation fails) increase as well (Onnasch, Wickens, Li, & Manzey, 2014).…”

Section: Introductionmentioning

confidence: 99%

Human factors of transitions in automated driving: A general framework and literature survey

Happee

Cabrall

et al. 2016

Transportation Research Part F: Traffic Psychology and Behaviou

159

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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.

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“…However, it has been argued that the coproduction of services entails several paradoxical relations (Blair et al 2020) that create conflictual conditions and significant tensions for providers of sea ice predictions: Scientists have to balance simultaneously expertise-and userdriven innovation without losing grasp of production-oriented, high-impact research; they have to assess and make transparent to users their own limitations; and they also must communicate the uncertainties and skill underlying their products, all the while confidently meeting ever-evolving user needs. In addition, one can argue that the automation paradox (Bibby et al 1975;Bainbridge 1983) will become increasingly pervasive in sea ice prediction, as services evolve from mainly manual processes to increasing automation. Ironically, the automation of forecasting services will likely require everincreasing levels of human input in order to become usable information (Jeuring et al 2020).…”

Section: B Simulation and Foresight In Climate Services Coproductionmentioning

confidence: 99%

Coproducing Sea Ice Predictions with Stakeholders Using Simulation

Blair

Müller

Palerme

et al. 2022

Weather, Climate, and Society

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Forecasts of sea-ice evolution in the Arctic region for several months ahead can be of considerable socio-economic value for a diverse range of marine sectors and for local community supply logistics. However, subseasonal-to-seasonal (S2S) forecasts represent a significant technical challenge, while translating user needs into scientifically manageable procedures and robust user confidence requires collaboration among a range of stakeholders. We developed and tested a novel, transdisciplinary co-production approach that combined socio-economic scenarios and participatory, research-driven simulation-gaming to test a new S2S sea-ice forecast system with experienced mariners in the cruise tourism sector. Our custom-developed computerized simulation-game ICEWISE integrated sea-ice parameters, forecast technology and human factors, as a participatory environment for stakeholder engagement. We explored the value of applications-relevant S2S sea-ice prediction and linked uncertainty information. Results suggest that the usefulness of S2S services is currently most evident in schedule-dependent sectors but expected to increase due to anticipated changes in the physical environment and continued growth in Arctic operations. Reliable communication of uncertainty information in sea-ice forecasts must be demonstrated and trialed before users gain confidence in emerging services and technologies. Mariners’ own intuition, experience, and familiarity with forecast service provider reputation impact the extent to which sea-ice information may reduce uncertainties and risks for Arctic mariners. Our insights into the performance of the combined foresight/simulation co-production model in brokering knowledge across a range of domains demonstrates promise. We conclude with an overview of the potential contributions from S2S sea-ice predictions and from experiential co-production models to the development of decision-driven and science-informed climate services.

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Man's Role in Control Systems

Cited by 27 publications

References 6 publications

Shipboard operating and maintenance procedures and the knowledge gap

Shipboard operating and maintenance procedures and the knowledge gap

Human factors of transitions in automated driving: A general framework and literature survey

Coproducing Sea Ice Predictions with Stakeholders Using Simulation

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