Air Traffic Control Tools Assessment

Noskievič, Tomáš; Kraus, Jakub

doi:10.14311/mad.2017.02.01

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…In safety–critical industries such as defence, aviation, and health care, people increasingly work with automated decision aids. For example, decision aids can advise operators how to manage the deployment of multiple uninhabited vehicles for activities such as military operations, delivering cargo and rescuing civilians (Calhoun et al, 2018; Loft et al, 2021), and in air traffic control (ATC), decision aids can advise controllers of potential future conflicts (i.e., loss of minimum separation standards) between aircraft (Musialek et al, 2010; Noskievič & Kraus, 2017). Providing operators with decision aids can improve performance and reduce workload (see meta-analysis by Onnasch et al, 2014).…”

Section: An Evidence Accumulation Model Of Automation Usementioning

confidence: 99%

Automated decision aids: When are they advisors and when do they take control of human decision making?

Strickland¹,

Boag²,

Heathcote³

et al. 2023

Journal of Experimental Psychology: Applied

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We applied a computational model to examine the extent to which participants used an automated decision aid as an advisor, as compared to a more autonomous trigger of responding, at varying levels of decision aid reliability. In an air traffic control conflict detection task, we found higher accuracy when the decision aid was correct, and more errors when the decision aid was incorrect, as compared to a manual condition (no decision aid). Responses that were correct despite incorrect automated advice were slower than matched manual responses. Decision aids set at lower reliability (75%) had smaller effects on choices and response times, and were subjectively trusted less, than decision aids set at higher reliability (95%). We fitted an evidence accumulation model to choices and response times to measure how information processing was affected by decision aid inputs. Participants primarily treated low-reliability decision aids as an advisor rather than directly accumulating evidence based on its advice. Participants directly accumulated evidence based upon the advice of high-reliability decision aids, consistent with granting decision aids more autonomous influence over decisions. Individual differences in the level of direct accumulation correlated with subjective trust, suggesting a cognitive mechanism by which trust impacts human decisions.

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Section: An Evidence Accumulation Model Of Automation Usementioning

confidence: 99%

Automated decision aids: When are they advisors and when do they take control of human decision making?

Strickland¹,

Boag²,

Heathcote³

et al. 2023

Journal of Experimental Psychology: Applied

View full text Add to dashboard Cite

show abstract

“…Automated decision aids are increasingly prevalent in modern workplaces. For example, in air traffic control, decision aids advise controllers of potential future conflicts (i.e., loss of minimum separation standards) between aircraft (Noskievič & Kraus, 2017), and decision aids are used in Defense to provide advice regarding the management of uninhabited vehicles (UVs; U.S. Air Force, 2015). Automated decision aid advice in such work contexts is often highly reliable, but not perfect, potentially leading to disuse of automated advice (rejecting correct advice) or misuse of automated advice (accepting incorrect advice) by the human operator (Lee & See, 2004;Parasuraman & Riley, 1997).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transparent Automated Advice to Mitigate the Impact of Variation in Automation Reliability

Gegoff,

Tatasciore,

Bowden

et al. 2023

Hum Factors

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Objective To examine the extent to which increased automation transparency can mitigate the potential negative effects of low and high automation reliability on disuse and misuse of automated advice, and perceived trust in automation. Background Automated decision aids that vary in the reliability of their advice are increasingly used in workplaces. Low-reliability automation can increase disuse of automated advice, while high-reliability automation can increase misuse. These effects could be reduced if the rationale underlying automated advice is made more transparent. Methods Participants selected the optimal UV to complete missions. The Recommender (automated decision aid) assisted participants by providing advice; however, it was not always reliable. Participants determined whether the Recommender provided accurate information and whether to accept or reject advice. The level of automation transparency (medium, high) and reliability (low: 65%, high: 90%) were manipulated between-subjects. Results With high- compared to low-reliability automation, participants made more accurate (correctly accepted advice and identified whether information was accurate/inaccurate) and faster decisions, and reported increased trust in automation. Increased transparency led to more accurate and faster decisions, lower subjective workload, and higher usability ratings. It also eliminated the increased automation disuse associated with low-reliability automation. However, transparency did not mitigate the misuse associated with high-reliability automation. Conclusion Transparency protected against low-reliability automation disuse, but not against the increased misuse potentially associated with the reduced monitoring and verification of high-reliability automation. Application These outcomes can inform the design of transparent automation to improve human-automation teaming under conditions of varied automation reliability.

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“…In order to improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS) [4] and Decision Support Tools (DST) for ATCos (e.g., Arrival and Departure managers) [38]. These new technologies present advantages in many aspects, such as safety, efficiency and airspace capacity [2], [27].…”

Section: Introductionmentioning

confidence: 99%

A Mindset-Based Evolution of Unmanned Aircraft System (UAS) Acceptance Into the National Airspace System (NAS)

et al. 2020

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The main goal of this research is to evaluate the Air Traffic Controller (ATCo) workload considering the Unmanned Aircraft System (UAS) integration into the National Airspace System (NAS) through fast-time simulations, and including futuristic scenarios in which the ATCo is familiar with manned and unmanned aircraft, i.e., different ATCo mindsets are considered. As these professionals play an essential role in optimizing the airspace operation, maintaining their workload at an acceptable level is essential. However, the integration of new technologies, such as UAS, may present an impact on safety levels from the workload perspective. In this context, the Technology Maturity Level (TML), which is a systematic metric/measurement system that supports assessments of the familiarity of a particular aircraft with ATCos, is proposed. The experiments showed that the integration of UAS into the NAS should be conducted gradually. INDEX TERMS Unmanned aircraft system (UAS), National Airspace System (NAS), Air Traffic Controller (ATCo), safety, workload.

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Air Traffic Control Tools Assessment

Cited by 8 publications

References 1 publication

Automated decision aids: When are they advisors and when do they take control of human decision making?

Automated decision aids: When are they advisors and when do they take control of human decision making?

Transparent Automated Advice to Mitigate the Impact of Variation in Automation Reliability

A Mindset-Based Evolution of Unmanned Aircraft System (UAS) Acceptance Into the National Airspace System (NAS)

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