A firefighter's situation awareness (SA) is considered crucial to making effective tactical decisions and actions at the scene. Despite the importance of the firefighter's SA, there have been limited ...research efforts to understand what cues and information firefighters use to assess ongoing situations and predict future conditions. In addition to fire events, contemporary firefighters respond to an increasing volume of non-fire incidents. Thus, this study aims to identify firefighters' SA during three fire incidents (single house fire, vehicle fire, and passenger aircraft fire) and three non-fire incidents (medical emergency, hazardous materials, and urban search and rescue). A goal-directed task analysis was conducted via focus group discussions with eight career firefighters. Findings indicate that firefighters build their SA by processing various cues from hazards (e.g., fire, ignition source), humans (e.g., occupants, bystanders, drivers, passengers), spatial elements (e.g., building structure, location of hazards), and surrounding conditions (e.g., traffic, weather). Our findings provide insights into SA measurement, SA-oriented work processes, training for SA, and designing technologies to support firefighters' SA during all-hazard responses.
•Modern firefighters increasingly respond to non-fire incidents.•Firefighters' situation awareness (SA) plays a critical role for their decisions.•Firefighters perceive cues of hazards, people, space, and surrounding conditions.•Efforts are needed to support firefighter's SA for various future hazardous events.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study aimed to identify professional firefighters’ situation awareness (SA) requirements for six high-risk emergencies: i) a single-house fire, ii) an unconscious person as a medical emergency ...scenario, iii) a vehicle fire, iv) a gas leak incident as a hazardous material (HazMat) case, v) a passenger aircraft fire, vi) and a search and rescue operation for a collapsed building. A Goal-Directed Task Analysis (GDTA) was employed to elicit major goals, sub-goals, decisions, and three-level SA information requirements for firefighters in six different types of an incident. Findings indicate that firefighters have common goals of ensuring life safety and incident stabilization. Sub-goals were concerned with the safety of residents as well as responders’ safety. Incident stabilization meant extinguishing fire for fire events and providing medical treatment in case of a medical emergency. To build SA for different situations, firefighters indicated that they use characteristics of fire and smoke, the location and condition of residents, a person’s appearance and behavior, and building structure. These findings can inform the design of work processes and emergency response technologies for fire and non-fire operations.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Situation awareness (SA) has become a widely used construct within the human factors community, the focus of considerable research over the past 25 years. This research has been used to drive the ...development of advanced information displays, the design of automated systems, information fusion algorithms, and new training approaches for improving SA in individuals and teams. In recent years, a number of papers criticized the Endsley model of SA on various grounds. I review those criticisms here and show them to be based on misunderstandings of the model. I also review several new models of SA, including situated SA, distributed SA, and sensemaking, in light of this discussion and show how they compare to existing models of SA in individuals and teams.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
This paper presents a model of situation awareness (SA) that emphasises that SA is necessarily built using a subset of available information. A technique (Quantitative Analysis of Situation Awareness ...- QASA), based around signal detection theory, has been developed from this model that provides separate measures of actual SA (ASA) and perceived SA (PSA), together with a feature unique to QASA, a measure of bias (information acceptance). These measures allow the exploration of the relationship between actual SA, perceived SA and information acceptance. QASA can also be used for the measurement of dynamic ASA, PSA and bias. Example studies are presented and full details of the implementation of the QASA technique are provided. Practitioner Summary: This paper presents a new model of situation awareness (SA) together with an associated tool (Quantitative Analysis of Situation Awareness - QASA) that employs signal detection theory to measure several aspects of SA, including actual and perceived SA and information acceptance. Full details are given of the implementation of the tool.
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
Driving has become a collaborative activity and a form of human-autonomy teaming (HAT) with the addition of autonomy to the advanced driver assistance system (ADAS), which makes situational decisions ...and sensible actions (e.g., autopilot and collision avoidance). However, it has been identified that in many fatal road accidents involving collaborative driving, over-reliance on the ADAS becomes the primary factor. To overcome this issue, the underlying situation awareness (SA) concept is investigated to identify an appropriate SA model for collaborative driving that could impact the intelligent agent's design in an HAT context. The formalization of existing SA model characteristics is defined and compared with those in collaborative driving. As a result, existing SA models are inadequate for explaining collaborative driving. Therefore, a new supportive SA (SSA) model is proposed. Based on the nature of this new model, applying transparency during SA development of the ADAS is suggested as a mechanism to comprehend ADAS behaviours. The proposed SA model is a significant expansion of multiple-agent SA models, and a transparent-based system can be a future direction of ADAS development to calibrate drivers' trust.
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
•The indicators were collected based on three levels of situation awareness (SA).•The drivers’ SA assessment system was built using grey correlation analysis.•The constructed backpropagation neural ...network model can effectively predict SA.
Based on combining the relevant studies on situation awareness (SA), this paper integrated multiple indicators, including eye movement, electroencephalogram (EEG), and driving behavior, to evaluate SA. SA is typically divided into three stages: perception, understanding and prediction. This paper used eye movement indicators to represent perception, EEG indicators to represent understanding, and driving behavior indicators to represent prediction. After identifying indicators for evaluating SA, a driving simulation experiment was designed to collect data on the indicators. 41 subjects were recruited to participate in the investigation, and the experimenter collected data from each subject in a total of 9 groups. After removing 4 groups of invalid data, 365 groups of valid data were finally obtained. The grey correlation analysis was used to optimize the SA indicators, and 10 SA evaluation indicators were finally determined. There were the average fixation duration, the nearest neighbor index, pupil area, the percentage power spectral density values of the 3 rhythmic waves (θ, α, β), rhythmic wave energy combination parameters (α / θ), mean speed, SD of speed and acceleration. Taking the optimized 10 indicators as input and the SA scores as output, a backpropagation neural network model with a topological structure of 10-8-1 was constructed. 75% of the data were randomly selected for model training, and the final network training’s mean square error was 0.0025. Using the remaining 25% of data for verification, the average absolute error and average relative error of the predicted results are 0.248 and 0.046, respectively. This showed that the model was effective, and it was feasible to evaluate the SA by using the data of eye movement, EEG and driving behavior parameters.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
There is an increasing interest in how to organize operations carried out by multiteam systems (MTS). Large MTS typically operate with a dedicated integration team, responsible for coordinating the ...operation. We report a study of a military multiteam system that prosecute time-sensitive targets. We asked whether and how the integration team’s efficiency depends on its communication setting. Specifically, we studied how a co-located vs. a distributed communications setting influenced the shared situation awareness and whether the shared situation awareness again influenced the outcome of the decision processes. We found that performance fell when the integration team shifted from a co-located to a distributed setting. The fall in performance seemed to be mediated by a corresponding fall in situation awareness. Moreover, while the performance improved for each run in the co-located setting, we did not see such learning in the distributed setting. Qualitative observations revealed that misunderstandings lasted longer in a distributed configuration than in a co-located setting. We found that situation awareness at level 3 was the only level of situation awareness significant for predicting all dimensions of performance. Implications for theory, research, and practice are discussed.
Situation awareness (SA) is directly related to the operating level of dynamic system operators, and electroencephalography (EEG) is frequently employed as the gold standard for SA recognition. ...Several deep learning models performed well in SA recognition based on EEG features. However, it has limitations such as a limited size of datasets, restricted model interpretability, and low capability of extracting beneficial features. In this work, an adaptive spatial-channel attention mechanism (ASCAM) was introduced in the architectures of a convolutional neural network (CNN). Specifically, ASCAM allows the layers of CNN architectures to fuse various sizes of received information and selectively focus on effective interpretable features. Regarding the problem of the limited size of datasets, combining frequency noise with multivariate variational mode decomposition (MVMD) enhances the generalization capability of models. Experiment results showed that EEGNet embedded in the framework exhibited a relative improvement of 6.02% over the baseline method. The ASCAM contributes to feature extraction and significantly enhances considerable performance. Ablation studies were further implemented to confirm the efficacy of the proposed ASCAM and the MVMD-based data augmentation. Interpretation results indicated that neural network models with embedded attention mechanisms have discovered neurobiological mechanisms related to SA loss. Meanwhile, the proposed lightweight framework is plug-and-play, which can be embedded into any CNN architecture and utilized for various EEG decoding tasks.
System autonomy and AI are being developed for a wide variety of applications where they will likely work in tandem with people, forming human-AI teams (HAT). Situation awareness (SA) of autonomous ...systems and AI has been established as critical for effective interaction and oversight of these systems. As AI capabilities grow, and more effective teaming behaviors are expected of AI systems, there will also be an increased need for shared SA between the human and AI teammates. Methods for supporting team SA within HAT are discussed in terms of team SA requirements, team SA mechanisms, team SA displays and team SA processes. A framework for understanding the types of information that needs to be shared within HAT is provided, including a focus on taskwork SA, agent SA, and teamwork SA. AI based on learning systems creates new challenges for the development of good SA and mental models. AI transparency and explainability are discussed in terms of their separate roles for supporting SA and mental models in HAT. The SA Oriented Design (SAOD) process is described as a systematic methodology for developing transparent AI displays for HAT and an example of its application to automated driving in a Tesla is provided.
•Situation awareness (SA) is critical for effective interaction with AI systems.•Human-AI team performance requires taskwork SA, agent SA, and teamwork SA.•SA is best supported by AI display transparency that is current and prospective.•Explainable AI is primarily retrospective and directed at building mental models.•SA Oriented Design (SAOD) is a systematic process for developing transparent AI.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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