Cognitive Task Analysis for Unmanned Aerial System Design

Adams, Julie A.

doi:10.1007/978-90-481-9707-1_26

Cited by 6 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That it is not conformable with people's lifestyle may become one of the reasons for an innovation to be resisted [18]. Moreover, the interaction of pedestrians, vehicles, and infrastructure may also be changed due to the joining of AVs [45,118]. Compatibility could be valued in future research.…”

Section: Compatibilitymentioning

confidence: 99%

The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

et al. 2020

View full text Add to dashboard Cite

Excessive dependence on autonomous vehicles (AVs) may exacerbate traffic congestion and increase exhaust emissions in the future. The diffusion of AVs may be significantly affected by the public’s acceptance. A few factors that may affect people’s acceptance of AVs have been researched in the existing studies, one-third of which cited behavioral theories, while the rest did not. A total of seven factors with behavior theories are screened out that significantly affect the acceptance intention, including perceived ease of use, attitude, social norm, trust, perceived usefulness, perceived risk, and compatibility. Six factors without behavior theories are summed up that affect AV acceptance, namely safety, performance-to-price value, mobility, value of travel time, symbolic value, and environmentally friendly. We found that people in Europe and Asia have substantial differences in attitudes toward AVs and that safety is one of the most concerned factors of AVs by scholars and respondents. Public acceptance of the different types of AVs and consumers’ dynamic preferences for AVs are highlighted in the review too. The quality of literature is systematically assessed based on previously established instruments and tailored for the current review. The results of the assessment show potential opportunities for future research, such as the citation of behavior theories and access to longitudinal data. Additionally, the experimental methods and the utilization of mathematical and theoretical methods could be optimized.

show abstract

Section: Compatibilitymentioning

confidence: 99%

The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Answering Adams’ (2015) call to consider the cognitive aspects of UAV operation in the development of systems, and Shneiderman’s (2020) call to build the AI around the human, we provide here a demonstration of an AI-based intelligent filter, its implementation and an empirical evaluation of how this implementation affects operators’ workloads in real time. Specifically, we are addressing the problem of information overload in C2 maps and how it affects the work of micro-UAV operators.…”

Section: Discussionmentioning

confidence: 99%

“…Clutter, irrelevant information and the need to filter layers manually increase operators’ workloads, and can lead overloaded operators to neglect the C2 map and rely solely on the payload’s feed for gaining SA ( Back et al, 2021 ). Developers of UAV map systems must address cognitive load in their system design, as Adams (2015) emphasized the importance of human cognitive capabilities in the development of UAV systems. To avoid fatal results and support operators, techniques that reduce cognitive workloads without impacting mission performance must be considered.…”

Section: Introductionmentioning

confidence: 99%

Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload

2022

View full text Add to dashboard Cite

Objective Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators. Background Military UAV operators use the command-and-control (C2) map for developing mission-relevant situation awareness (SA). Yet C2 maps are overloaded with information, mostly irrelevant to the mission, causing operators to neglect the map altogether. To reduce irrelevant information, an intelligent filtering algorithm was developed. Here we evaluate its effectiveness in reducing operators’ cognitive workloads. Method Two-stage operational scenarios were conducted with professional ex-military UAV operators, using two filter protocols and a no-filter control. High-end real-time techniques were used to continuously assess workload from muscle behavior and machine learning models. Results Lower cognitive workload was found when applying the algorithm’s protocols, especially when fatigue started to accumulate (Stage II). However, concerns about the quality of SA arose. Conclusion The algorithm was positively evaluated for its ability to reduce operators’ cognitive workloads. More evaluations of operators’ SA are required. Application The algorithm demonstrates the possibility of integrating AI to improve human performance in complex systems, and can be applied to other domains where spatial-temporal information needs to be contextually filtered in real time.

show abstract

“…They developed a scale to measure the acceptance of fully automated vehicles by pedestrians, and proposed three elements of that scale: safety, interaction, and compatibility. Adams [20] points out that drivers of traditional vehicles, pedestrians, and other road users may expect automated vehicles to give way to them in any case, which poses new challenges to the existing traffic facilities, laws, and regulations.…”

Section: Introductionmentioning

confidence: 99%

Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

2021

View full text Add to dashboard Cite

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

show abstract

Cognitive Task Analysis for Unmanned Aerial System Design

Cited by 6 publications

References 40 publications

The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload

Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

Contact Info

Product

Resources

About