Situation Awareness Framework Research Articles

Integrating situation-aware knowledge maps and dynamic window approach for safe path planning by maritime autonomous surface ships

This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important for MASS navigation, offering a framework for situational awareness, including contextual information fusion and decision-making evidence. This research enriches the KM model with collision avoidance rules from the International Regulations for Preventing Collisions at Sea (COLREGs), building upon our previous work on MASS's efficient and COLREGs-compliant navigation in encounter scenarios. The model provides navigational context, covers COLREGs rules and environmental factors, and recommends MASS actions for various scenarios as suggested by COLREGs. Moreover, an adapted DWA, tailored to maritime navigation, accounts for specific constraints and safety measures for MASS, utilising KM-derived situational awareness as constraints in its cost function for path planning. A significant innovation introduced here is a tiered safety distance model featuring proactive, defensive, and collision buffers to ensure rule-compliant and effective collision avoidance. This scheme enables MASS to take timely collision avoidance actions at both proactive and defensive distances, in line with COLREGs recommendations. The effectiveness of the KM-DWA algorithm is validated by comparing it with the basic DWA algorithm in single- and multi-vessel encounter scenarios. The experiment outcomes illustrate the integrated approach's superiority in terms of COLREGs compliance and collision avoidance rate, emphasising its ability to support COLREGs-compliant decision-making and enhance situational awareness in autonomous maritime operations.

Time and Distance Perception in Highly Dynamic Environments

Abstract Space- and time-based tasks, like flying airplanes, driving cars, or the manual controlled rendezvous and docking of spacecrafts can be very demanding for pilots, drivers, or astronauts, depending on selected trajectories, approaching speeds, or distracting information. One characteristic of highly dynamic environments is that persons have to consider their spatial position in relation to surrounding elements based on passing time, detected speed, and trajectories to avoid collisions with other objects. An inadequate perception of the own position in relation to other objects and passing time can lead to impaired decision-making processes, resulting in errors or collisions. A framework for the explanation of cognitive processes involved in perception, anticipation, and decision-making in dynamic environments is provided by the concept of situation awareness. Based on this framework persons must perceive relevant elements, integrate them in a coherent mental representation and anticipate the development of the current situation in the near future, to plan safe upcoming maneuvers. Thus, for safe maneuvers anticipation processes are playing a major role in decision-making and action selection. In this paper it is proposed that time and distance perceptions are central features of anticipation processes in dynamic situations. It is suggested to combine theories like the attentional-gate model and time-to-contact with the framework of situation awareness to explain anticipation processes in dynamic environments. The major aim is to develop precise cognitive models of persons acting in dynamic situations. Such cognitive models can be the basis for the development and parameterization of assistance systems, supporting anticipation in dynamic environments.

Enhancing human–robot collaborative transportation through obstacle-aware vibrotactile warning and virtual fixtures

Transporting large and heavy objects can benefit from Human–Robot Collaboration (HRC), increasing the contribution of robots to our daily tasks and addressing challenges arising from labor shortages. This strategy typically positions the human collaborator as the leader, with the robot assuming the follower role. However, when transporting large objects, the operator’s situational awareness can be compromised as the objects may occlude different parts of the environment, weakening the human leader’s decision-making capacity and leading to failure due to collision. This paper proposes a situational awareness framework for collaborative transportation to face this challenge. The framework integrates a multi-modal haptic-based Obstacle Feedback Module with two units. The first unit consists of a warning module that alerts the operator through a haptic belt with four vibrotactile devices that provide feedback about the location and proximity of the obstacles. The second unit implements virtual fixtures as hard constraints for mobility. The warning feedback and the virtual fixtures act online based on the information given by two Lidars mounted on a mobile manipulator to detect the obstacles in the surroundings. By enhancing the operator’s awareness of the environment, the proposed module improves the safety of the human–robot team in collaborative transportation scenarios by preventing collisions. Experiments with 16 non-expert subjects in four feedback modalities during four scenarios report an objective evaluation thanks to quantitative metrics and subjective evaluations based on user-level experiences. The results reveal the strengths and weaknesses of the implemented feedback modalities while providing solid evidence of the increased situational awareness of the operator when the two haptic units are employed.

A novel industrial process situation awareness model based on multi‐time scale dynamic feature fusion with applications to float glass manufacturing

AbstractModern industrial manufacturing processes often focus on the changes of performance indicators and the evolution of operation conditions. However, the dynamics of variables are vague within processes and even more difficult to be characterised across different processes. The time‐series dynamic characteristics of the different scales among variables, processes, and production cycles develop and transfer with changes in parameters, equipment, and processes. It is difficult to accurately show the quality index and operating condition trend at the same time. To solve these problems, a situation awareness (SA) framework integrating multi‐time scale dynamic features for manufacturing processes is proposed. First, data are condensed and reconstructed through the denoising long short term memory autoencoder to reveal the time series dynamic characteristics to get neighbourhood features which contain features among neighbourhood samples. Second, the neighbourhood features divided into window blocks are fused into the stage features by statistical analysis. Finally, a multi‐scale isometric convolution network is designed to extract the local and global features, which can effectively show the development of dynamic features on a long time scale, and profoundly describe the influence of full cycle features on variables and operating conditions. The proposed model is verified on the real data set of a float glass manufacturing process, and the SA model can well predict the future trend of long time series.

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

AbstractIn order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system.

Constructing a spatiotemporal situational awareness framework to sense the dynamic evolution of online public opinion on social media

PurposeThe paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant departments in formulating public opinion control measures for specific time and space contexts.Design/methodology/approachThe spatiotemporal situational awareness framework comprises situational element extraction, situational understanding and situational projection. In situational element extraction, the data on the COVID-19 vaccine, including spatiotemporal tags and text contents, is extracted. In situational understanding, the bidirectional encoder representation from transformers – latent dirichlet allocation (BERT-LDA) and bidirectional encoder representation from transformers – bidirectional long short-term memory (BERT-BiLSTM) are used to discover the topics and emotional labels hidden in opinion texts. In situational projection, the situational evolution characteristics and patterns of online public opinion are uncovered from the perspective of time and space through multiple visualisation techniques.FindingsFrom the temporal perspective, the evolution of online public opinion is closely related to the developmental dynamics of offline events. In comparison, public views and attitudes are more complex and diversified during the outbreak and diffusion periods. From the spatial perspective, the netizens in hotspot areas with higher discussion volume are more rational and prefer to track the whole process of event development, while the ones in coldspot areas with less discussion volume pay more attention to the expression of personal emotions. From the perspective of intertwined spatiotemporal, there are differences in the focus of attention and emotional state of netizens in different regions and time stages, caused by the specific situations they are in.Originality/valueThe situational awareness framework can shed light on the dynamic evolution of online public opinion from a multidimensional perspective, including temporal, spatial and spatiotemporal perspectives. It enables decision-makers to grasp the psychology and behavioural patterns of the public in different regions and time stages and provide targeted public opinion guidance measures and offline event governance strategies.

Artificial Situational Awareness Assessment of a Novel ATC Support System

This paper presents the application of an existing situational awareness framework to a newly developed artificial intelligence system to determine its awareness level. The system incorporates diverse automation techniques - knowledge graph, expert rules, machine learning - for gaining situational awareness and applying it in the field of air traffic control. Since the system was developed to serve as a foundation for exploring automation and artificial situational awareness, the primary result of this work is the system’s overall awareness level assessment and the identification of sub-systems that may be improved for additional awareness. The framework used was chosen in the fundamental project documents and its use proved beneficial as it enabled the demonstration of how general guidelines can be interpreted for a specific system. It also informed possible routes for improvement of the process. Highest priority awareness-related improvements are those dealing with robustness, whose implementation would substantiate the current awareness assessment. The system is shown to be on the highest awareness level conditionally, considering its proof-of-concept level. The high level reached by the system is contingent on awareness concept and condition interpretations. With the appropriate assessment of the system, implementation in an operational environment is more feasible.

Reduction of Very Rapid Emergency Transfers to the Pediatric Intensive Care Unit.

Emergency transfers are associated with increased inpatient pediatric mortality. Therefore, interventions to improve system-level situational awareness were utilized to decrease a subset of emergency transfers that occurred within four hours of admission to an inpatient medical-surgical unit called very rapid emergency transfers (VRET). Specifically, we aimed to increase the days between VRET from non-ICU inpatient units from every 10 days to every 25 days over 1 year. Using the Model for Improvement, we developed an interdisciplinary team to reduce VRET. The key drivers targeted were the admission process from the emergency department and ambulatory clinics, sepsis recognition and communication, and expansion of our situational awareness framework. Days between VRET defined the primary outcome metric for this improvement project. After six months of interventions, our baseline improved from a VRET every 10 days to every 79 days, followed by another shift to 177 days, which we sustained for 3 years peaking at 468 days between events. Interventions targeting multiple admission sources to improve early recognition and communication of potential clinical deterioration effectively reduced and nearly eliminated VRET at our organization.

Study on Cyber Common Operational Picture Framework for Cyber Situational Awareness

The remarkable development of the Internet has made our lives very convenient, such as through the ability to instantaneously transmit individual pictures. As a result, cyber-attacks are also being developed and increasing, and the computer/mobile devices we use can become infected with viruses in an instant. Rapid cyber situational awareness is essential to prepare for such cyber-attacks. Accelerating cyber situational awareness requires Cyber Common Operational Pictures, which integrate and contextualize numerous data streams and data points. Therefore, we propose a Cyber Common Operational Pictures framework and criteria for rapid cyber situation awareness. First, the system reaction speed based on the user’s request and the standard for easily recognizing the object shown on the screen are presented. Second, standards and frameworks for five types of visualization screens that can directly recognize and respond to cyber-attacks are presented. Third, we show how a system was constructed based on the proposed framework, as well as the results of an experiment on the response time of each visualization screen. As a result of the experiment, the response speed of the 5 visualization screens was about 0.11 s on average for inquiry (simple) and 1.07 s on average for inquiry (complex). This is consistent with the typical response times of the studies investigated in this paper. If CyCOP is developed in compliance with the framework items (UI, object symbol, object size, response speed) presented in this paper, rapid situational awareness is possible. This research can be used in cyber-attack and defense training in the military field. In the private sector, it can be used in cyber and network control.

A Network Security Situation Awareness Method Based on GRU in Big Data Environment

Aiming at the “bottleneck” problems of the traditional network security situation awareness model, such as large equipment limitations, single data source and poor integration ability, weak level of autonomous learning and data mining, a network security situation awareness framework suitable for big data is constructed. A gate recurrent unit (GRU) model is established to effectively extract features from the situation data set through the deep learning algorithm of big data. It is a method to automatically mine and analyze the hidden relationship and change trend of network security situation, realize the high-speed acquisition and fusion of massive multi-source heterogeneous data, and perceive the network security situation from an all-round perspective. The experimental results show that this method has a good awareness effect on network threats, and has strong representation ability in the face of network threats. It can effectively perceive the network threat situation without relying on data labels, which verifies that this method can effectively improve the efficiency and accuracy of security situation awareness.

Maritime information sharing environment deployment using the advanced multilayered Data Lake capabilities

Establishing an efficient information sharing network among national agencies in maritime domain is of essential importance in enhancing the operational performance, increasing the situational awareness and enabling interoperability among all involved maritime surveillance assets. Based on various data-driven technologies and sources, the EU initiative of Common Information Sharing Environment (CISE), enables the networked participants to timely exchange information concerning vessel traffic, joint SAR & operational missions, emergency situations and other events at sea. In order to host and process vast amounts of vessels and related maritime data consumed from heterogeneous sources (e.g. SAT-AIS, UAV, radar, METOC), the deployment of big data repositories in the form of Data Lakes is of great added value. The different layers in the Data Lakes with capabilities for aggregating, fusing, routing and harmonizing data are assisted by decision support tools with combined reasoning modules with semantics aiming at providing a more accurate Common Operational Picture (COP) among maritime agencies. Based on these technologies, the aim of this paper is to present an end-to-end interoperability framework for maritime situational awareness in strategic and tactical operations at sea, developed in EFFECTOR EU-funded project, focusing on the multilayered Data Lake capabilities. Specifically, a case study presents the important sources and processing blocks, such as the SAT-AIS, CMEMS, UAV components, enabling maritime information exchange in CISE format and communication patterns. Finally, the technical solution is validated in the project’s recently implemented maritime operational trials and the respective results are documented.

Increasing situation awareness in healthcare through real-time simulation

Research into real-time simulation applications outside of manufacturing environments has extended to sociotechnical systems such as healthcare over the past decade, where a number of published studies have demonstrated proof-of-concept models for near-future resource planning. Using real-time decision-support systems, people take decisions supported by the output of simulations. However real-time simulation frameworks abstract human intervention to an “external decision-maker,” with little regard to the complexities of underlying decision-making constructs, and how design and development decisions can impact the quality of decision-support. One such construct is situation awareness (SA), which is a precursor to decision-making. It is a dynamic state of knowledge about how a situation is unfolding; one approach to enhancing situation awareness is the provision of appropriate real-time information. We argue that design, development and implementation decisions should be focused at the interface between decision-making and decision-support. This integrative literature review proposes a SA framework integrating models of SA with a technical perspective for real-time simulation, to support an understanding of the cognitive needs of users alongside technical details during the development process. The implications for the usefulness and usability of real-time decision-support tools are discussed with application to Emergency Departments.

Information Security Situation in Blockchain for Secure SDN Based on Big Data in Smart Communities

In the internet of things (IoT) and big data, the global information society is entering a new phase where consumers, networks, and perception devices work together more intimately. Software-defined networks (SDN) offer lower latency and massive connectivity for intelligent devices (IoT) for the internet of things. Smart communities are one of the most important applications of the blockchain. SDN technology is used to provide residents with smart services. Situational awareness for information security offers a distinct, high-level security perspective based on security alarm occurrences. However, contemporary information security warning data has become too complicated and varied than a simple analysis is almost impossible. In addition to enhancing IoT security's monitoring, emergency response, and forecasting capabilities, this article presents an IoT-assisted information security situation awareness framework (IoT-ISSAF). SDN model has been validated through simulation as being able to accurately assess the current state of network security in blockchain.

A multi-level analytic framework for disaster situational awareness using Twitter data

During a natural disaster, mining messages from social media platforms can facilitate local agencies, rescue teams, humanitarian aid organizations, etc., to track the situational awareness of the public. However, for different stakeholders, the concerns about people’s situational awareness in a natural disaster event are different. Therefore, I developed a Twitter-based analytic framework to take perception-level situational awareness, humanitarian-level situational awareness, and action-level situational awareness into consideration. Specifically, perception-level situational awareness mainly reflects people’s perception of the ongoing natural disaster event (i.e., if people are discussing the disaster event). Decision-makers can rapidly have a big picture of severely impacted regions. Humanitarian-level situational awareness represents tweets that are associated with the humanitarian categories based on the definition from the United Nations Office for the Coordination of Humanitarian Affairs. The detection of humanitarian-level situational awareness can help response teams understand the specific situations and needs of local communities. In terms of the action-level situational awareness, I extracted noun-verb pairs in each tweet to explicitly represent the specific event described in a given tweet, so that the response teams can quickly act on the situation case by case. Moreover, to shed light on disaster resilience and social vulnerability, I further examined the demographic characteristics of three levels of situational awareness. I empirically demonstrated the analytic framework using geo-tagged tweets during 2018 Hurricane Michael.

Reasoning Graph: A Situation-aware framework for cooperating unprotected turns under mixed connected and autonomous traffic environments

Cooperating unprotected turns in mixed connected and autonomous traffic environments is beneficial to improving the systemic utility of intersections. However, interactions among connected and automated vehicles (CAVs) and manually driving vehicles (MVs) are varied and interrelated. These factors make the cooperation a multimodal problem and bring many difficulties to interactions analyzing and the optimal plan solving. This paper proposes a situation-aware Reasoning Graph (RG) framework that consists of the Reasoning Process and the Solution Process. Complex interactions are first described by a graph, where the mapping from situations to the graph is constructed. Then the Reasoning Process divides future motion space into numerous situations and reasons potential situations using the graph search-based algorithm. Some rules for maneuver compatibility and social interaction customs are integrated into the graph search to ensure the maneuver compatibility and social compliance of these reasoned situations. The Solution Process develops a heuristic algorithm to quickly search for speed profiles that follow the reasoned situations. After comparative tests of the reservation-based and the optimization-based methods, the results show that the RG-based method helped decrease the average systemic cost by approximately 10%. However, it did not cause losses in individual performances. The statistical analysis of individual performance shows that the RG even increased the individual performances regarding safety, efficiency, and energy-saving.

A data-driven situation-aware framework for predictive analysis in smart environments

In the era of Internet of Things (IoT), it is vital for smart environments to be able to efficiently provide effective predictions of user’s situations and take actions in a proactive manner to achieve the highest performance. However, there are two main challenges. First, the sensor environment is equipped with a heterogeneous set of data sources including hardware and software sensors, and oftentimes humans as complex sensors, too. These sensors generate a huge amount of raw data. In order to extract knowledge and do predictive analysis, it is necessary that the raw sensor data be cleaned, understood, analyzed, and interpreted. Second challenge refers to predictive modeling. Traditional predictive models predict situations that are likely to happen in the near future by keeping and analyzing the history of past user’s situations. Traditional predictive analysis approaches have become less effective because of the massive amount of data continuously streamed in that both affects data processing efficiency and complicates the data semantics. To address the above challenges, we propose a data-driven, situation-aware framework for predictive analysis in smart environments. First, to effectively analyze the sensor data, we employ the Situ-Morphism method to transfer sensor-enabled situation information to vector information. Then we introduce new similarity metrics and implement similarity prediction based on Locality Sensitive Hashing to improve data processing efficiency and effectively handle the data semantics. Experiment results show that the predictive analysis method proposed in this paper can be effective.

The Situation Awareness Framework for Explainable AI (SAFE-AI) and Human Factors Considerations for XAI Systems

Recent advances in artificial intelligence (AI) have drawn attention to the need for AI systems to be understandable to human users. The explainable AI (XAI) literature aims to enhance human understanding and human-AI team performance by providing users with necessary information about AI system behavior. Simultaneously, the human factors literature has long addressed important considerations that contribute to human performance, including how to determine human informational needs, human workload, and human trust in autonomous systems. Drawing from the human factors literature, we propose the Situation Awareness Framework for Explainable AI (SAFE-AI), a three-level framework for the development and evaluation of explanations about AI system behavior. Our proposed levels of XAI are based on the informational needs of human users, which can be determined using the levels of situation awareness (SA) framework from the human factors literature. Based on our levels of XAI framework, we also suggest a method for assessing the effectiveness of XAI systems. We further detail human workload considerations for determining the content and frequency of explanations as well as metrics that can be used to assess human workload. Finally, we discuss the importance of appropriately calibrating user trust in AI systems through explanations along with other trust-related considerations for XAI, and we detail metrics that can be used to evaluate user trust in these systems.

A Comprehensive Framework for the Assessment of Synchrophasor Communication Networks from the Perspective of Situational Awareness in a Smart Grid Cyber Physical System

The conventional power grid is modernizing into a cyber-physical smart grid. In this system, there are several applications, an important being the synchrophasor measurement system. Sensors called the phasor measurement units, measure the synchrophasors and communicate them to the phasor data concentrator using the synchrophasor communication networks. Situational awareness helps in the quick perception of the environment and in taking commensurate actions by the power system operator. Thus, the synchrophasor communication networks play an important role and must be assessed from the perspective of the operator’s situational awareness. In this context, a comprehensive situational awareness analysis metric is proposed. The proposed metric is based on hardware reliability, data reliability, and network delay. As a case study, a practical power grid of India (West Bengal Power Grid) is considered for assessing its situational awareness. The synchrophasor communication networks of the West Bengal power grid are simulated in the QualNet network simulator to validate the proposed situational awareness framework.

Context-Aware Service Model of a Mobile Library Based on Internet of Things

Appropriate technology needs to be applied in libraries to provide users with more humanized, intelligent, and convenient services to improve service quality. Using theories from library science, management, and modeling, this paper examines library personalized service in the intelligent Internet of Things (IoT) environment using a literature review, comparative analysis, and UML modeling to analyze the influencing factors of mobile library users’ acceptance of personalized recommendation services. Based on the situational awareness framework, the experimental results of the effect of these personalized service recommendations show that the load factor is greater than 0.6, which indicates that the dimensions of a scalar can explain situational awareness. The P-value > 0.01 shows that the mobile library is linear and related to the user, mobile, resource, and service scenarios. Overall, based on this investigation and analysis, countermeasures, and suggestions, a personalized service system framework may be suitable for mobile libraries in the IoT environment.

Autonomous Navigation in Confined Waters - A COLREGs Rule 9 Compliant Framework

Fully or partial autonomous marine vessels are actively being developed by many industry actors. In many cases, the autonomous vessels will be operating close to shore, and within range of a Remote Control Center (RCC). Close to shore operation requires that the autonomous vessel is able to navigate in close proximity to other autonomous or manned vessels, and possibly in confined waters, while obeying the COLREGs on equal terms as any other vessel at sea. In confined waters however, certain COLREGs rules apply, which might alter the expected actions (give-way or stand-on), depending on the manoeuvrability of the vessels. This paper presents a Situation Awareness (SAS) framework for autonomous navigation that complies with COLREGs rule 9 (Narrow Channels). The proposed solution comprises a method for evaluating the manoeuvrability of a vessel in confined waters, for assessing the applicability of COLREGs rule 9. This feature is then integrated into an already existing SAS framework for facilitating COLREGs-compliant navigation in restricted waters. The applicability of the proposed method is demonstrated in simulation using a case study of a small autonomous passenger ferry.