38,149 research outputs found
Empirical exploration of air traffic and human dynamics in terminal airspaces
Air traffic is widely known as a complex, task-critical techno-social system,
with numerous interactions between airspace, procedures, aircraft and air
traffic controllers. In order to develop and deploy high-level operational
concepts and automation systems scientifically and effectively, it is essential
to conduct an in-depth investigation on the intrinsic traffic-human dynamics
and characteristics, which is not widely seen in the literature. To fill this
gap, we propose a multi-layer network to model and analyze air traffic systems.
A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN)
encapsulate critical physical and operational characteristics; an Integrated
Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network
(ICCN) are formulated to represent air traffic flow transmissions and
intervention from air traffic controllers, respectively. Furthermore, a set of
analytical metrics including network variables, complex network attributes,
controllers' cognitive complexity, and chaotic metrics are introduced and
applied in a case study of Guangzhou terminal airspace. Empirical results show
the existence of fundamental diagram and macroscopic fundamental diagram at the
route, sector and terminal levels. Moreover, the dynamics and underlying
mechanisms of "ATCOs-flow" interactions are revealed and interpreted by
adaptive meta-cognition strategies based on network analysis of the ICCN.
Finally, at the system level, chaos is identified in conflict system and human
behavioral system when traffic switch to the semi-stable or congested phase.
This study offers analytical tools for understanding the complex human-flow
interactions at potentially a broad range of air traffic systems, and underpins
future developments and automation of intelligent air traffic management
systems.Comment: 30 pages, 28 figures, currently under revie
A proposed psychological model of driving automation
This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation
Formulating the cognitive design problem of air traffic management
Evolutionary approaches to cognitive design in the air traffic management (ATM) system can be attributed with a history of delayed developments. This issue is well illustrated in the case of the flight progress strip where attempts to design a computer-based system to replace the paper strip have consistently been met with rejection. An alternative approach to cognitive design of air traffic management is needed and this paper proposes an approach centred on the formulation of cognitive design problems. The paper gives an account of how a cognitive design problem was formulated for a simulated ATM task performed by controller subjects in the laboratory. The problem is formulated in terms of two complimentary models. First, a model of the ATM domain describes the cognitive task environment of managing the simulated air traffic. Second, a model of the ATM worksystem describes the abstracted cognitive behaviours of the controllers and their tools in performing the traffic management task. Taken together, the models provide a statement of worksystem performance, and express the cognitive design problem for the simulated system. The use of the problem formulation in supporting cognitive design, including the design of computer-based flight strips, is discussed
Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges
Human-swarm interaction (HSI) involves a number of human factors impacting
human behaviour throughout the interaction. As the technologies used within HSI
advance, it is more tempting to increase the level of swarm autonomy within the
interaction to reduce the workload on humans. Yet, the prospective negative
effects of high levels of autonomy on human situational awareness can hinder
this process. Flexible autonomy aims at trading-off these effects by changing
the level of autonomy within the interaction when required; with
mixed-initiatives combining human preferences and automation's recommendations
to select an appropriate level of autonomy at a certain point of time. However,
the effective implementation of mixed-initiative systems raises fundamental
questions on how to combine human preferences and automation recommendations,
how to realise the selected level of autonomy, and what the future impacts on
the cognitive states of a human are. We explore open challenges that hamper the
process of developing effective flexible autonomy. We then highlight the
potential benefits of using system modelling techniques in HSI by illustrating
how they provide HSI designers with an opportunity to evaluate different
strategies for assessing the state of the mission and for adapting the level of
autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling
Conference, Canberra, Australi
EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers
Several models defining different types of cognitive human behaviour are available. For this work, we
have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model
is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools
able to assess at which level of cognitive control the operator is dealing with the considered task, that
is if he/she is performing the task as an automated routine (skill level), as procedures-based activity
(rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK
behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such
behaviours have been evaluated from a neurophysiological point of view, for example, by considering
brain activity variations across the different SRK levels. Therefore, the proposed study aimed to
investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly
to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers,
demonstrated that specific brain features could characterize and discriminate the different SRK levels,
therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic
setting
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges
With the rapid development of marine activities, there has been an increasing
number of maritime mobile terminals, as well as a growing demand for high-speed
and ultra-reliable maritime communications to keep them connected.
Traditionally, the maritime Internet of Things (IoT) is enabled by maritime
satellites. However, satellites are seriously restricted by their high latency
and relatively low data rate. As an alternative, shore & island-based base
stations (BSs) can be built to extend the coverage of terrestrial networks
using fourth-generation (4G), fifth-generation (5G), and beyond 5G services.
Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs.
Despite of all these approaches, there are still open issues for an efficient
maritime communication network (MCN). For example, due to the complicated
electromagnetic propagation environment, the limited geometrically available BS
sites, and rigorous service demands from mission-critical applications,
conventional communication and networking theories and methods should be
tailored for maritime scenarios. Towards this end, we provide a survey on the
demand for maritime communications, the state-of-the-art MCNs, and key
technologies for enhancing transmission efficiency, extending network coverage,
and provisioning maritime-specific services. Future challenges in developing an
environment-aware, service-driven, and integrated satellite-air-ground MCN to
be smart enough to utilize external auxiliary information, e.g., sea state and
atmosphere conditions, are also discussed
An account of cognitive flexibility and inflexibility for a complex dynamic task
Problem solving involves adapting known problem solving methods and strategies to the task at hand (Schunn & Reder, 2001) and cognitive flexibility is considered to be “the human ability to adapt the cognitive processing strategies to face new and unexpected conditions of the environment” (Cañas et al., 2005, p. 95). This work presents an ACT-R 6.0 model of complex problem solving behavior for the dynamic microworld game FireChief (Omodei & Wearing, 1995) that models the performance of participants predisposed to behave either more or less flexibly based on the nature of previous training on the task (Cañas et al., 2005). The model exhibits a greater or lesser degree of cognitive inflexibility in problem solving strategy choice reflecting variations in task training. The model provides an explanation of dynamic task performance compatible with the Competing Strategies paradigm (Taatgen et al., 2006) by creating a second layer of strategy competition that renders it more flexible with respect to strategy learning, and provides an explanation of cognitive inflexibility based on reward mechanism
A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation
Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni
- …