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This study investigates how the perception of persuasive behaviors (polite, logical, displeased, angry) of an android robot is affected by situations regarding the context of violation (affecting oneself or others), and by subject traits, such as compliance awareness (CA) and agreeableness (AG). We conducted a video-based experiment based on a mixed-subjects design with 98 participants from the US and conducted a three-way mixed analysis of variance to investigate the impact of persuasive types and situation types (as within-subject factors), and the subject trait groups (CA or AG, as between-subject factors), on the subjective impressions of the persuasive behaviors by the android robot. Results showed that more negative behaviors (anger and displeasure) are appraised as being more appropriate and effective to persuade a violator in situations where the violation affects others, while no clear preference was found in a situation where the violation affects only oneself. Regarding the subject traits, participants with higher CA and lower AG would be willing to adhere to any persuasive behaviors, while their counterparts would dislike being persuaded through negative behaviors by the robot. These findings can be considered in future studies to develop cognitive models for generating situation-aware behaviors in social robots. [ABSTRACT FROM AUTHOR]

Fire services have a responsibility to record information regarding known hazards for sites posing unique and substantial risks. Such information is important, both for informing service planning, and for supporting the development of situational awareness and decision-making during emergency response. However, public inquiries and service inspections have raised concerns regarding the effectiveness of systems in place for recording, monitoring, and using site-specific risk information (SSRI). In contrast, a lack of research focus has been directed toward the use of SSRI, limiting the evidence base available for understanding the causes of these difficulties and how they may be addressed. The following study seeks to contribute to developing knowledge by examines what factors affect the use of SSRI across organisational levels and why. Interviews were conducted with 23 firefighters from operational and tactical roles in one region of the UK. Thematic analysis highlighted six key themes: (i) Differences in the use of SSRI across organisational levels, (ii) System accessibility, (iii) Content, (iv) Structure, (v) Quality, and (vi) Training. Findings indicate that SSRI use is affected by the relevance, structure, and quality of information, which are influenced by workload, training, guidance, and system accessibility. During emergency response, information requirements, time and cognitive capacity differ across organisational levels, influencing the extent to which barriers affect ability to use SSRI. The implications of findings are discussed in relation to informing future areas of applied cognition research, and fire sector decisions regarding the gathering and storage of SSRI. [ABSTRACT FROM AUTHOR]

Situation awareness (SA) is a key indicator of operator task performance and behavioral safety in Human-Machine Interaction (HMI), and task difficulty is an important influencing factors in establishing and maintaining SA, especially in safety-critical scenarios such as flying and nuclear. However, the complexity and diversity of multitasking make it difficult to quantify task difficulty, which to a certain extent limits the research on cognitive research. To solve this problem, we proposed an approach to quantifying task difficulty in multitasking and analyzed the effect of task difficulty on different levels of SA through experiments in this paper. First, a task difficulty measure for a single meta-task was developed based on Shannon's information theory, including both discrete and continuous meta-tasks. Next, a three-dimensional attribute model of meta-tasks was proposed to measure the difficulty value added by concurrent tasks based on multiple resource theory. Finally, an experiment was conducted to measure SA at different levels based on SAGAT, and SA under different levels of task difficulty was analyzed. The results showed a strong negative correlation between SA and task difficulty. Specifically, compared to SA at the comprehension and prediction levels, SA at the perceptual level was more easily influenced by task difficulty. This study can provide some reference for the quantify of task difficulty in human factors experiments, the optimization of tasks in HMI, and the selection of operator attention allocation strategies. [ABSTRACT FROM AUTHOR]

This study investigated the influence of using optical see-through head-mounted displays (OST-HMDs) as a replacement for conventional monitors for an extended duration, as required for laparoscopic surgery. We developed the experiment scenario including two displays (surgical field and patient vitals) in three different combinations of the HMD and conventional monitors. An experiment was conducted to compare the task performance, situation awareness, physical discomfort, and perceived workload in the three different display settings. From this study, it was shown that even though the OST-HMD resulted in higher physical discomfort and lower self-perceived performance rating, the surgical task performance, and situation awareness were not worse, and in some conditions even better than conventional monitors. The main obstacle to using OST-HMDs to replace conventional monitors in laparoscopic surgery would be the weight of the HMDs and the head discomfort caused by wearing them for a long time. [ABSTRACT FROM AUTHOR]

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The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that fairness, accountability, transparency, and explainability (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment. This allows for unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an adaptability, fairness, transparency, explainability, and accountability (AFTEA) framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper explores the convergence of Human-Centered AI (HCAI) and Cyber-Physical Social Systems (CPSS) in pursuing advanced Cognitive Situation Awareness (CSA). Integrating HCAI principles within CPSS fosters systems prioritizing human needs, values, and experiences, improving perception, understanding, and responsiveness to complex environments. By incorporating transparency, interpretability, and usability into Artificial Intelligence (AI) systems, the human-centered approach enhances user interaction and cooperation with intelligent systems, leading to more adaptive and efficient CPSS. The study employs a comprehensive approach to explore the intersection of HCAI and CPSS. Moreover, the paper presents case studies to illustrate real-world applications of HCAI and CPSS, such as self-driving cars and smart homes, transportation, healthcare, energy management, social media, and emergency response systems. Nevertheless, technical complexities, privacy concerns, and regulatory considerations must be addressed. The paper demonstrates the practical implications of integrating HCAI into CPSS through case studies in various domains. Furthermore, It highlights the positive impact of CSA systems such as self-driving cars, showcasing improvements in transportation. This paper contributes to advancing CSA and designing intelligent systems, promoting human–machine collaboration and societal well-being. By examining the intersection of HCAI and CPSS, this study advances research in CSA and designing intelligent systems prioritizing human needs, values, and experiences. [ABSTRACT FROM AUTHOR]

Overconfidence in situation awareness (SA) can lead to various detrimental consequences, including risky behaviors. This study investigates the influence of stress and attention allocation on SA in manufacturing. Specifically, this study aims to (1) examine the effects of stress on objective SA, (2) explore the relationship between SA levels and SA overconfidence, and (3) investigate the potential alleviation of SA overconfidence through attention allocation to more challenging SA levels. These findings demonstrate that stress impairs the comprehension aspect of SA. Moreover, SA overconfidence increases with the SA level. In exploring strategies to mitigate SA overconfidence, allocating attention specifically to the most challenging SA level--SA projection--proved effective in alleviating overconfidence. These findings contribute to future SA research by clarifying the relationship between stress and SA, uncovering the link between SA levels and SA overconfidence, and exploring methods to alleviate overconfidence and improve decision accuracy in manufacturing systems. [ABSTRACT FROM AUTHOR]

Objective: This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving. Background: Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems. Method: Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions. Results: Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model. Conclusion: The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time. Application: The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving. [ABSTRACT FROM AUTHOR]

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the 'weakest link' and are vital to prevention. This research investigates what cognitive and internal factors influence information security awareness (ISA) among employees, through quantitative empirical research using a survey conducted at a Dutch financial insurance firm. The research question of "How and to what extent do cognitive and internal factors contribute to information security awareness (ISA)?" has been answered, using the theory of situation awareness as the theoretical lens. The constructs of Security Complexity, Information Security Goals (InfoSec Goals), and SETA Programs (security education, training, and awareness) significantly contribute to ISA. The most important research recommendations are to seek novel explaining variables for ISA, further investigate the roots of Security Complexity and what influences InfoSec Goals, and venture into qualitative and experimental research methodologies to seek more depth. The practical recommendations are to minimize the complexity of (1) information security topics (e.g., by contextualizing it more for specific employee groups) and (2) integrate these simplifications in various SETA methods (e.g., gamification and online training). [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) could improve maritime collaboration by facilitating human factors like situation awareness and decision-making. However, it is not clear in which ways AR can be beneficial and for which maritime operations. This paper addresses the need for a systematic literature review of state-of-the-art ways AR can facilitate maritime collaboration. Following the PRISMA statement, we searched for the constructs "AR," "maritime operation," and "collaboration" in the Web of Science and IEEE Xplore databases. Out of 691 search results, we retained 32 publications for analysis. Ten publications from other sources were included. Our results provide a review of AR applications for the maritime operations of ship navigation, personal navigation, maritime construction, maritime maintenance and inspection, and other. We suggest that research focuses on bringing promising AR applications to higher technology readiness levels, learning from other industries, applying robust evaluation methods, and grounding more on human factors like decision-making, situation awareness, and communication. [ABSTRACT FROM AUTHOR]

• The level of urgency affects situation awareness(SA), trust, workload, and perceived urgency. • In high-level urgency, level 3 SA significantly improves situation awareness. • The displays with SA cues enhance system transparency, increasing drivers' trust. • Information that adequately describes the situation reduces the drivers' workload. • Level 1 SA is not significantly different from a display without SA factors. Semi-autonomous vehicles still require drivers to take over in an unexpected situation. In this situation, the increased cognitive load on the driver can lead to distraction, which in turn reduces situational awareness (SA) and prevents appropriate responses, increasing the risk of accidents. For this reason, providing interfaces that enhance SA is essential to ensuring safety and optimal performance. However, existing SA-based display research often overlooks the levels of SA and effectiveness of modalities, especially in urgent situations where a driver's SA might be compromised. The present study aims to design SA-based display that considers the urgency of the situation and the level of SA in line with a specific design framework and modality effectiveness. We conducted an experiment using simulated videos to evaluate the effectiveness of the SA-based display. This experiment assessed the effects of three urgent situations and three levels of situational awareness-based displays on drivers' SA, situational trust, mental workload, and perceived urgency. We employed a 3x4 mixed-factorial design for the experiment. The between-subject factors were the SA levels (perception, comprehension, and projection) and a baseline. The within-subject factors were urgency scenarios (low, medium, and high). The results showed that as urgency increased, displays reflecting Level 3 SA, which requires prediction, significantly improved SA compared to other displays. We expect our findings to contribute to the practical design of automotive displays by providing useful considerations for SA-based display design. [ABSTRACT FROM AUTHOR]

• Eye movement and driving behavior were used to evaluate situation awareness (SA). • The variable message sign gives drivers a higher SA level. • The variable message signs (VMS) have a speed-limiting effect on vehicles. • Experienced drivers have better SA than inexperienced drivers. • Eye movement is a sensitive SA indicator. The driving environment in the freeway exit areas is complex, and the installation of relevant signs can enhance the creation of a safe and smooth exit area environment. This study investigated the situation awareness (SA) scores of two types of drivers using different signs in the freeway exit areas and the correlation between drivers' eye movement, driving behavior, and SA scores. The driving tasks were divided into two situations: continuing on the mainline and entering the exit ramp. The independent variables included scene type (static speed limit signs, static speed limit signs plus variable message signs, variable speed limit signs, variable speed limit signs plus variable message signs), and participant type (experienced and inexperienced participants). The dependent variables were SA, eye movement, and driving behavior. Each participant completed a driving simulation experiment with the two driving tasks, totaling eight scenes. The SA scores were measured using the Situation Awareness Global Assessment Technique (SAGAT). The driving behavior and eye movement were collected during the experimental data analysis segment, and the sensitivity indicators for evaluating drivers' SA were determined by calculating the correlation with the SAGAT scores. This paper focuses on applying variable message signs (VMS) to speed limits in freeway exit areas from the driver's perspective to enhance driving safety. The findings showed that drivers have better SA in scenes with VMS, and experienced drivers have better SA than inexperienced drivers in the same scene. The VMS affects speed control, and eye movement is a sensitive SA indicator. [ABSTRACT FROM AUTHOR]

• 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. [ABSTRACT FROM AUTHOR]

In unstructured agricultural fields where autonomous navigation is challenging and demands additional safety, the operator’s experience and knowledge are essential for supervising operations and making decisions beyond the robot’s autonomous capabilities. Local networks with long-range wireless communication combined with digital twin concepts are promising solutions that can be used for robot teleoperation. The purpose of this study was to demonstrate the feasibility of supervising a mobile robot inside berry orchards using a digital shadow from a long-range distance (between 300 and 3000 m), with the primary objective of assisting the robot in navigating in complex situations such as row-end turning. This involved creating a virtual representation of the robot that mirrors its state and actions, allowing the remote operator to monitor and guide the robot effectively. The system comprised a GPS-based navigation controller with collision avoidance sensors, two sets of LoRa transmitters and repeaters, a simulation environment with a digital shadow of the robot, and a graphical user interface for the remote operator. Information about the digital shadow’s state, including location, orientation, and distances to obstacles, was received as a message by the LoRa gateway and was used to update the path for the actual robot that interfaced with the Robot Operating System (ROS). The main research hypothesis aimed to test the quality of the LoRa communication link between the robot and the operator, as well as the robustness of the robot’s control system, with an emphasis on the architecture, communication link, and situation awareness creation. Preliminary results showed that depending on the environment, the average packet loss was 12% at distances of approximately 2300 m. Our results highlight some of the core technical challenges that need to be addressed for an effective teleoperation system, including latency, stability, and the limited range of wireless communication. Future works involves evaluating the performance and reliability of the proposed method under different field conditions and scenarios, as well as considering the use of the 5G network for a significant improvement in data transmission speed, navigation efficiency, and visual feedback. Upon successful implementation, this study has the potential to enhance the efficiency and safety of robot navigation, providing a practical solution for remote supervision in challenging environments.Article highlights: Development of a digital shadow mobile robot: Designed and implemented a simulated mobile robot with that can follow a user-defined reference trajectory and generate a path. Enhanced Teleoperation: LoRa-based Internet of Robotic Things for efficient teleoperation of an agricultural mobile robot in complex environments for remote agricultural supervision. Evaluation of LoRa Connectivity: The study reveals issues like latency, stability, and limited range in LoRa communication that need addressing for effective teleoperation systems. [ABSTRACT FROM AUTHOR]

Security operation centers (SOCs) are increasingly established to meet the growing threat against cyber security. The operators of SOCs respond to complex incidents under time constraints. Within critical infrastructure, the consequences of human error or low performance in SOCs may be detrimental. In other domains, situation awareness (SA) has proven useful to understand and measure how operators use information and decide the correct actions. Until now, SA research in SOCs has been restricted by a lack of in-depth studies of SA mechanisms. Therefore, this study is the first to conduct a goal-directed task analysis in a SOC for critical infrastructure. The study was conducted through a targeted series of unstructured and semi-structured interviews with SOC operators and their leaders complemented by a review of documents, incident reports, and in situ observation of work within the SOC and real incidents. Among the presented findings is a goal hierarchy alongside a complete overview of the decisions the operators make during escalated incidents. How the operators gain and use SA in these decisions is presented as a complete set of SA requirements. The findings are accompanied by an analysis of contextual differences in how the operators prioritize goals and use information in network incidents and security incidents. This enables a discussion of what SA processes might be automated and which would benefit from different SA models. The study provides a unique insight into the SA of SOC operators and is thus a steppingstone for bridging the knowledge gap of Cyber SA. [ABSTRACT FROM AUTHOR]

Aiming at the lack of effective analysis and identification of operational area in the traditional battlefield situation awareness process, a method of aerial battlefield target trajectory analysis method for operational area extraction is proposed. Firstly, based on the improved Douglas-Peucker algorithm, the featured trajectory is extracted based on the original target trajectory, thus reducing the trajectory redundant information. Secondly, the clustering by fast search and find of density peaks (CFSFDP) algorithm is used to cluster the featured trajectory, which obtains multiple featured points clusters. Finally, each featured points cluster is scanned based on Graham's convex hull algorithm to obtain closed polygons to characterize the combat areas. The feasibility and effectiveness of the algorithm model are verified by several case simulations.

Aimming at the multitudinous battlefield situation information and the difficulty in recognizing the changing trends, based on large models, a situation awareness agent and an intelligent situation awareness inductive method are proposed. Starting from cognitive concepts and combining the abstractness and embodiment characteristics of agents, three key components in the construction of agents have been clarified: learning environment, memory mode, and knowledge generation mechanism. The architecture of the battlefield situation awareness agent is designed, including memory component, planning component, execution component, evaluation component, and key points for agent training. In the long-term memory component, based on the modeling characteristics of complex battlefield states, the paper discusses the application of large language models, multimodal large models and large sequence models.

Introduction Material and Methods Results Conclusions Managing obstetric shoulder dystocia requires swift action using correct maneuvers. However, knowledge of obstetric teams' performance during management of real‐life shoulder dystocia is limited, and the impact of non‐technical skills has not been adequately evaluated. We aimed to analyze videos of teams managing real‐life shoulder dystocia to identify clinical challenges associated with correct management and particular non‐technical skills correlated with high technical performance.We included 17 videos depicting teams managing shoulder dystocia in two Danish delivery wards, where deliveries were initially handled by midwives, and consultants were available for complications. Delivery rooms contained two or three cameras activated by Bluetooth upon obstetrician entry. Videos were captured 5 min before and after activation. Two obstetricians assessed the videos; technical performances were scored as low (0–59), average (60–84), or high (85–100). Two other assessors evaluated non‐technical skills using the Global Assessment of Team Performance checklist, scoring 6 (poor) to 30 (excellent). We used a spline regression model to explore associations between these two score sets. Inter‐rater agreement was assessed using interclass correlation coefficients.Interclass correlation coefficients were 0.71 (95% confidence interval 0.23–0.89) and 0.82 (95% confidence interval 0.52–0.94) for clinical and non‐technical performances, respectively. Two teams had low technical performance scores; four teams achieved high scores. Teams adhered well to guidelines, demonstrating limited head traction, McRoberts maneuver, and internal rotation maneuvers. Several clinical skills posed challenges, notably recognizing shoulder impaction, applying suprapubic pressure, and discouraging women from pushing.Two non‐technical skills were associated with high technical performance: effective patient communication, with teams calming the mother and guiding her collaboration during internal rotational maneuvers, and situation awareness, where teams promptly mobilized all essential personnel (senior midwife, consultant, pediatric team). Team communication, stress management, and task management skills were not associated with high technical performance.Videos capturing teams managing real‐life shoulder dystocia are an effective tool to reveal challenges with certain technical and non‐technical skills. Teams with high technical performance are associated with effective patient communication and situational awareness. Future training should include technical skills and non‐technical skills, patient communication, and situation awareness. [ABSTRACT FROM AUTHOR]

AbstractThis study examines the impact of visual assistance and cognitive load on situation awareness (SA) during takeover events in Level 3 (L3) autonomous driving, where drivers are permitted to engage in Non-Driving Related Tasks (NDRTs). Utilizing a driving simulator, the research explores how different NDRTs, occupying various sensory channels, influence drivers’ SA under two visual assistance conditions: full marking and key marking. Results from the Situation Awareness Global Assessment Technique (SAGAT) and NASA Task Load Index (NASA-TLX) scales, along with eye-tracking data, demonstrate significant interactions between visual assistance conditions and tasks, highlighting key marking’s superior effectiveness in enhancing SA across all NDRTs. The study reveals that cognitive workload, largely arising from NDRTs rather than road attention, remains unaffected by visual assistance conditions. Findings suggest that drivers’ strategies for allocating attention are consistent, regardless of the visual task presence. This research contributes to understanding SA in autonomous driving takeovers, offering valuable insights for developing Human-Machine Interface (HMI) designs to improve SA and takeover performance in autonomous driving systems. [ABSTRACT FROM AUTHOR]

This work focuses on the opportunity to use the Digital Twin of a complex system, as a Decision Support System. In studying the phenomenon of human Decision Making, the concept of Situation Awareness appears to be of primary importance when dealing with these complex systems. Given the complexity of the system to be represented in the DT, its own complexity, and the need to integrate the user's abilities to allow the acquisition of SA, the concept of reality anchor is proposed to identify the elements of the studied situation necessary for users to perceive, understand and project the situation they face. A methodology, called the Reality Anchor Methodology, has been defined to ensure the elicitation and implementation of these elements in a DT. This methodology is composed of three steps that aim (1) to elicit the reality anchors through a study of the operators' tasks and activities, (2) to design a prototype to carry out human-in-the-loop tests and (3) to validate the definition of Reality Anchors by analysing the SA, experience feedback and the activities performed during the tests. This method was applied to a case study in the oil-and-gas industry and showed the importance of the defined reality anchors. [ABSTRACT FROM AUTHOR]

Aiming at the lack of effective analysis and identification of operational area in the traditional battlefield situation awareness process, a method of aerial battlefield target trajectory analysis method for operational area extraction is proposed. Firstly, based on the improved Douglas-Peucker algorithm, the featured trajectory is extracted based on the original target trajectory, thus reducing the trajectory redundant information. Secondly, the clustering by fast search and find of density peaks (CFSFDP) algorithm is used to cluster the featured trajectory, which obtains multiple featured points clusters. Finally, each featured points cluster is scanned based on Graham's convex hull algorithm to obtain closed polygons to characterize the combat areas. The feasibility and effectiveness of the algorithm model are verified by several case simulations. [ABSTRACT FROM AUTHOR]

In response to the increasing number and forms of network attacks, different types and names of network security situational awareness models have received widespread attention and research from the academic community. In the context of the rapid popularization of information technology, hardware system, software vulnerabilities, and security vulnerabilities in daily application operations have led to an increasing number of ways and means of network attacks. However, a single type of network security monitoring and analysis tool is no longer suitable for the current development of network technology. By reviewing the research history and current status of network security situational awareness technology, this article summarizes and analyzes the theoretical development and engineering applications of situational awareness models, and discusses the shortcomings and deficiencies in relevant technical solutions. It also looks forward to the future research directions of network security situational awareness models. [ABSTRACT FROM AUTHOR]

Previous research indicated a need to improve pilot training with regard to understanding of autopilot logic and behavior, especially in non-routine situations. Therefore, we tested the effect of problem-based exploratory training on pilots' understanding of autopilot functions. Using a moving-base flight simulator, general aviation pilots (n = 45) were trained to diagnose failures either without foreknowledge and guidance (exploratory group), without foreknowledge but with some guidance (exploratory-guidance group) or with foreknowledge and full guidance (control group). They subsequently performed six test scenarios in which their understanding of the effects of failures was tested by requiring them to deduce the failures and select autopilot modes that were still functioning. Those who received exploratory training with guidance were significantly more likely than the other groups to diagnose failures correctly. The exploratory training group also selected the most appropriate functioning autopilot modes significantly faster than the control group. The results suggest that exploratory training with an appropriate level of guidance is useful for gaining a practical understanding of autopilot logic and behavior. Exploratory training may help to improve transfer of training to operational practice, and prevent automation surprises and accidents. [ABSTRACT FROM AUTHOR]

Patrolling represents a potential application area for multi-robot systems, as it can enable efficient surveillance. A key aspect in facilitating the real-world applications of such missions is the enhancement of situation awareness of the base station (BS), in addition to ensuring well-coordinated patrol behavior. This paper addresses this requirement by proposing a layered patrol algorithm designed to maintain network connectivity with the BS. The novelty of this research lies in the distributed nature of the algorithm, despite the presence of the BS. Each robot independently determines its behavior based on local information while concurrently preserving connectivity to the BS. Additionally, this study introduces a novel performance metric to assess the situation awareness of the BS, focusing on the algorithm's ability to provide prompt information about mission progress. Simulated missions revealed that the proposed algorithm outperformed existing algorithms, visited locations of interest more frequently and comprehensively, and provided the BS with improved situation awareness. Enhancing situation awareness may enable human operators to quickly gain insights into the system's behavior based on mission progress, allowing for timely interventions if necessary. This capability contributes to improving human trust in autonomous systems. [ABSTRACT FROM AUTHOR]

The situation awareness, especially for collaborative robots, plays a crucial role when humans and machines work together in a human-centered, dynamic environment. Only when the humans understands how well the robot is aware of its environment can they build trust and delegate tasks that the robot can complete successfully. However, the state of situation awareness has not yet been described for collaborative robots. Furthermore, the improvement of situation awareness is now only described for humans but not for robots. In this paper, the authors propose a metric to measure the state of situation awareness. Furthermore, the models are adapted to the collaborative robot domain to systematically improve the situation awareness. The proposed metric and the improvement process of the situation awareness are evaluated using the mobile robot platform Robotino. The authors conduct extensive experiments and present the results in this paper to evaluate the effectiveness of the proposed approach. The results are compared with the existing research on the situation awareness, highlighting the advantages of our approach. Therefore, the approach is expected to significantly improve the performance of cobots in human–robot collaboration and enhance the communication and understanding between humans and machines. [ABSTRACT FROM AUTHOR]

INTRODUCTION: A gricultural aircraft operations are associated with unique challenges. In particular, these include maintaining awareness of obstacles associated with flight at very low altitudes. Wire strikes are a common cause of accidents in these operations. METHODS: Focus groups were completed during the 2022 Ag Aviation Expo hosted by the National Agricultural Aviation Association with pilots who had experienced wire-strike events (N = 22). The researchers coded the transcripts using a human factors framework. RESULTS: Notably, unplanned "trim passes" were a key stage of flight during wire-strike events. Cognitive risk factors that may have affected their performance included situation awareness, decision-making choices, and pressure to perform. Over half of subjects reported being aware of the wire before collision. Possible prevention strategies include not spraying the field due to safety risks, paying better attention to where they were in the field, and avoiding deviation from the planned route. DISCUSSION: Wire-strike events often occur due to momentary lapses in attention, even when the pilot is already aware of the wire. This study shows that targeted approaches to prevent wire strikes in agricultural aviation operations require addressing a number of cognitive risks and human factors, rather than implementing increased preflight surveillance. These results have implications for preventing future wire-strike accidents based directly on pilot perceptions, both within agricultural operations and general aviation more broadly. [ABSTRACT FROM AUTHOR]

By conducting a mixed-design experiment using simplified accident handling tasks performed by two-person teams, this study examined the effects of automation function and condition (before, during, and after malfunction) on human performance. Five different and non-overlapping functions related to human information processing model were considered and their malfunctions were set in a first-failure way. The results showed that while the automation malfunction impaired task performance, the performance degradation for information analysis was more severe than response planning. Contrary to other functions, the situation awareness for response planning and response implementation tended to increase during malfunctioning and decrease after. In addition, decreased task performance reduced trust in automation, and malfunctions in earlier stages of information processing resulted in lower trust. Suggestions provided for the design and training related to automation emphasise the importance of high-level cognitive support and the benefit of involving automation error handling in training. The effects of automation function and malfunction on human performance are important for design and training. The experimental results in this study revealed the significance of high-level cognitive support. Also, introducing automation error handling in training can be helpful in improving situation awareness of the teams. [ABSTRACT FROM AUTHOR]

This paper examines the feasibility of incorporating visual cueing systems within vehicles to mitigate the risk of experiencing motion sickness. The objective is to enhance passenger awareness and the ability to anticipate the forces associated with car travel motion. Through a comprehensive literature review, the findings demonstrate that visual cues can mitigate motion sickness for particular in-vehicle configurations, whereas their influence on situational awareness is not clear yet. Each type of visual cue proved more effective when presented in the peripheral field of view rather than solely in the central vision. Promising applications can be found within interactive screens and ambient lighting, while the use of extended reality shows potential for future investigations. In addition, integrating such systems into highly automated vehicles shows potential to improve their overall user acceptance. This paper focuses on the integration of visual cues in vehicles to alleviate motion sickness. The work provides an overview of visual cueing strategies and different types of systems for display. The findings suggest that incorporating visual cues in highly automated vehicles can reduce motion sickness. Recommendations are provided for the design and integration of these displays in vehicle interiors. [ABSTRACT FROM AUTHOR]

With the evolution of traditional production towards smart manufacturing, humans and machines interact dynamically to handle complex production systems in semi-automated environments when full automation is not possible. To avoid undesirable side effects, and to exploit the full performance potential of experts, it is crucial to consider the human perspective when developing new technologies. Specifically, human sub-tasks during machine operation must be described to gain insights into cognitive processes. This research proposes a cognition-based framework by integrating a number of known psychological concepts. The focus is on the description of cognitive (team) processes in the resolution of anomalies within a manufacturing process with interdisciplinary experts working together. An observational eye tracking study with retrospective think-aloud interviews (N = 3) provides empirical evidence for all cognitive processes proposed in the framework, such as regular process monitoring and—in case of a detected anomaly—diagnosis, problem solving, and resolution. Moreover, the role of situation awareness, individual expertise and (cognitive) team processes is analyzed and described. Further, implications regarding a human-centered development of future production systems are discussed. The present research provides a starting point for understanding and supporting cognitive (team) processes during intelligent manufacturing that will dominate the production landscape within Industry 5.0. [ABSTRACT FROM AUTHOR]

• Explored the use of multimodal signals in designing vehicle takeover requests. • Found that informative signals enhance processing times and situation awareness compared to instructional signals. • Multimodal displays, especially tactile-visual, outperform unimodal displays. • Observed no significant age-related differences in situation awareness levels. • Drivers showed a preference for signals conveying obstacle locations over purely instructional ones. Research has revealed that conditionally automated vehicles can adversely affect situation awareness, a crucial factor in ensuring a safe transition of driving control during takeover, particularly for older adults. The objective of this study was to design and test more complex multimodal interfaces capable of delivering critical real-time road information, including obstacle locations, statuses, and lane availability, to effectively assist drivers who may experience age-related cognitive and physical declines, when navigating complex automated systems. This study investigated the effects of displays (single tactile, and visual and tactile combined), information presentation type (instructional, informative, baseline), and age (older and younger adults) on participants' takeover performance (i.e., information processing time and situation awareness). In general, the utilization of informative information resulted in an enhancement of drivers' situation awareness and information processing time, compared to when using the instructional information type. Moreover, multimodal displays were associated with faster processing speeds compared to unimodal displays. However, no significant main effect of the display was observed on the level of situation awareness. Likewise, there was no discernible age-related disparity in situation awareness levels. Yet, younger adults exhibited shorter information processing times than older adults. This research aims to improve situation awareness and processing time to help drivers, especially older adults, prevent time-critical accidents during the takeover process in automated driving. The findings from this study may inform the design of next-generation in-vehicle human–machine interfaces. [ABSTRACT FROM AUTHOR]

Situation awareness (SA) describes an individual's understanding of their surroundings and actions in the near future based on the individual's comprehension and understanding of the surrounding inputs. SA measurements can be applied to improve system performance or human effectiveness in many fields of study, including driving. However, in some scenarios drivers might need to drive in unfamiliar traffic regulations (UFTRs), where the traffic rules and vehicle configurations are a bit different from what the drivers are used to under familiar traffic regulations. Such driving conditions require drivers to adapt their attention, knowledge, and reactions to safely reach the destination. This ability is influenced by the degree of handedness. In such tasks, mixed-/left-handed people show better performance than strong right-handed people. This paper aims to explore the influence of the degree of handedness on SA when driving under UFTRs. We analyzed the SA of two groups of drivers: strong right-handed drivers and mixed-/left-handed drivers. Both groups were not familiar with driving in keep-left traffic regulations. Using a driving simulator, all participants drove in a simulated keep-left traffic system. The participants' SA was measured using a subjective assessment, named the Participant Situation Awareness Questionnaire PSAQ, and performance-based assessment. The results of the study indicate that mixed-/left-handed participants had significantly higher SA than strong right-handed participants when measured by performance-based assessment. Also, in the subjective assessment, mixed-/left-handed participants had significantly higher PSAQ performance scores than strong right-handed participants. The findings of this study suggest that advanced driver assistance systems (ADAS), which show improvement in road safety, should adapt the system functionality based on the driver's degree of handedness when driving under UFTRs. [ABSTRACT FROM AUTHOR]

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system's ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks. [ABSTRACT FROM AUTHOR]

This study highlights the disproportionate number of fatal and non-fatal accidents in general aviation (GA) compared to airline carriers, emphasizing the need to investigate the contributing factors to these incidents. It identifies poor decision-making and a lack of situational awareness as key issues and presents a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to analyze preflight information used by GA pilots. The findings underscore the significance of operational factors in ensuring a successful flight and suggest modifications to pilot license renewal processes, with an emphasis on the adoption of digital preflight tools. A new theoretical framework based on the operational factors identified is also introduced, which could serve as a foundation for future studies and interventions aimed at enhancing safety in general aviation. [ABSTRACT FROM AUTHOR]

During SAE L3 automated driving, drivers engage in non-driving related tasks (NTRDs), but NTRDs impair situation awareness (SA) and takeover quality. This study aimed to find an NTRD that is interesting but has a minimal negative impact on SA. Given the strong impact of games on cognition, we studied how games affected SA and takeover performance. In the game survey, we investigated the impact of four type of games on driving SA. The results showed that there were some games positively affected SA (situational game). In the formal experiment, we used a realistic simulated driving environment to investigate the effect of games on SA and takeover performance. A 3 (task type: situational game/non-situational game/monitoring) × 4 (attention duration, time between monitoring requests and takeover requests: 4 s/6 s/8 s/10 s) mixed design was used. Results showed that drivers who engaged in the situational game had higher SA, better takeover performance, and required less attention duration than drivers who engaged in the non-situational game. Both groups showed faster takeover time than the monitoring group, indicating higher arousal. Our research results indicate that playing situational games can improve SA and takeover quality in automated driving. [ABSTRACT FROM AUTHOR]

Situation Awareness (SA) is a cornerstone for system verification and validation, particularly vital at the future Shore Control Centre (SCC). However, engine room monitoring (ERM) receives little attention regarding the SA. This research conducted two Likert-type surveys with 201 marine engineers and employed a descriptive statistic and the Mann–Whitney U test to investigate cue strengths in forming comprehension around the engine room and generality in cue strengths by ship types, experience and ranks of respondents. The importance varies to different extents, and their perspectives seem not to deviate much, with the max disagreement rate at 12% by ship types and the ranks of respondents. The extent of disagreement tends to reduce with the increment of experience thresholds. The outcomes offer a promising strategy for developing a human-centred monitoring system to mitigate the potential risk of information overload and incorrect schema instantiation at the future SCC. [ABSTRACT FROM AUTHOR]