Showing papers on "Situation awareness published in 2011"

Designing for Situation Awareness: An Approach to User-Centered Design, Second Edition

Abstract: The barrage of data overload is threatening the ability of people to effectively operate in a wide range of systems including aircraft cockpits & ground control stations, military command and control centers, intelligence operations, emergency management, medical systems, air traffic control centers, automobiles, financial and business management systems, space exploration, and power and process control rooms All of these systems need user interfaces that allow people to effectively manage the information available to gain a high level of understanding of what is currently happening and projections on what will happen next They need systems designed to support Situation Awareness Addressing the information gap between the plethora of disorganized, low-level data and what decision makers really need to know, Designing for Situation Awareness: An Approach to User-Centered Design, Second Edition provides a successful, systematic methodology and 50 design principles for engineers and designers seeking to improve the situation awareness of their systems users based on leading research on a wide range of relevant issues So, whats new in the Second Edition: Significantly expanded and updated examples throughout to a wider range of domains New Chapters: Situation Awareness Oriented Training and Supporting SA in Unmanned and Remotely Operated Vehicles Updated research findings and expanded discussion of the SA design principles and guidelines to cover new areas of development Mica R Endsley is a pioneer and world leader in the study and application of situation awareness in advanced systems Debra G Jones work is focused on designing large-scale and complex systems to support situation awareness and dynamic decision making Completely revised and updated, liberally illustrated with actual design examples, this second edition demonstrates how people acquire and interpret information and examines the factors that undermine this process Endsley and Jones distill their expertise and translate current research into usable, applicable methods and guidelines

SensePlace2: GeoTwitter analytics support for situational awareness

Abstract: Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.

Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency.

Abstract: In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies. We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand-annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.

WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones

Abstract: Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.

Supervisory Control of Multiple Robots: Human-Performance Issues and User-Interface Design

Abstract: The purpose of this paper is to review research pertaining to the limitations and advantages of supervisory control for unmanned systems. We identify and discuss results showing technologies that mitigate the observed problems such as specialized interfaces, and adaptive systems. In the report, we first present an overview of definitions and important terms of supervisory control and human-agent teaming. We then discuss human performance issues in supervisory control of multiple robots with regard to operator multitasking performance, trust in automation, situation awareness, and operator workload. In the following sections, we review research findings for specific areas of supervisory control of multiple ground robots, aerial robots, and heterogeneous robots (using different types of robots in the same mission). In the last section, we review innovative techniques and technologies designed to enhance operator performance and reduce potential performance degradations identified in the literature.

Specification-Based Intrusion Detection for Advanced Metering Infrastructures

Abstract: It is critical to develop an effective way to monitor advanced metering infrastructures (AMI). To ensure the security and reliability of a modernized power grid, the current deployment of millions of smart meters requires the development of innovative situational awareness solutions to prevent compromised devices from impacting the stability of the grid and the reliability of the energy distribution infrastructure. To address this issue, we introduce a specification-based intrusion detection sensor that can be deployed in the field to identify security threats in real time. This sensor monitors the traffic among meters and access points at the network, transport, and application layers to ensure that devices are running in a secure state and their operations respect a specified security policy. It does this by implementing a set of constraints on transmissions made using the C12.22 standard protocol that ensure that all violations of the specified security policy will be detected. The soundness of these constraints was verified using a formal framework, and a prototype implementation of the sensor was evaluated with realistic AMI network traffic.

Cauldron mission-centric cyber situational awareness with defense in depth

Abstract: The cyber situational awareness of an organization determines its effectiveness in responding to attacks. Mission success is highly dependent on the availability and correct operation of complex computer networks, which are vulnerable to various types of attacks. Today, situational awareness capabilities are limited in many ways, such as inaccurate and incomplete vulnerability analysis, failure to adapt to evolving networks and attacks, inability to transform raw data into cyber intelligence, and inability for handling uncertainty. We describe advanced capabilities for mission-centric cyber situational awareness, based on defense in depth, provided by the Cauldron tool. Cauldron automatically maps all paths of vulnerability through networks, by correlating, aggregating, normalizing, and fusing data from a variety of sources. It provides sophisticated visualization of attack paths, with automatically generated mitigation recommendations. Flexible modeling supports multi-step analysis of firewall rules as well as host-to-host vulnerability, with attack vectors inside the network as well as from the outside. We describe alert correlation based on Caldron attack graphs, along with analysis of mission impact from attacks.

Semantic Knowledge-Based Framework to Improve the Situation Awareness of Autonomous Underwater Vehicles

Abstract: This paper proposes a semantic world model framework for hierarchical distributed representation of knowledge in autonomous underwater systems. This framework aims to provide a more capable and holistic system, involving semantic interoperability among all involved information sources. This will enhance interoperability, independence of operation, and situation awareness of the embedded service-oriented agents for autonomous platforms. The results obtained specifically affect the mission flexibility, robustness, and autonomy. The presented framework makes use of the idea that heterogeneous real-world data of very different type must be processed by (and run through) several different layers, to be finally available in a suited format and at the right place to be accessible by high-level decision-making agents. In this sense, the presented approach shows how to abstract away from the raw real-world data step by step by means of semantic technologies. The paper concludes by demonstrating the benefits of the framework in a real scenario. A hardware fault is simulated in a REMUS 100 AUV while performing a mission. This triggers a knowledge exchange between the status monitoring agent and the adaptive mission planner embedded agent. By using the proposed framework, both services can interchange information while remaining domain independent during their interaction with the platform. The results of this paper are readily applicable to land and air robotics.

Optimizing Operator–Agent Interaction in Intelligent Adaptive Interface Design: A Conceptual Framework

Abstract: Intelligent adaptive interfaces (IAIs) are emerging technologies that promise opportunities for enhancing performance in complex sociotechnical environments, such as multiple uninhabited aerial vehicle (UAV) control. However, a lack of established design guidelines for such advanced interfaces makes many designs costly and ineffective. In this paper, a generic conceptual framework for developing IAIs is proposed to guide interface design. The framework integrates a user-centered design approach with the concept of proactive use of adaptive intelligent agents (AIAs), aiming at maximizing overall system performance. Based on existing design approaches, identified challenges, and IAI design needs, the framework uses a multiple-agent hierarchical structure to allocate tasks between operators and agents for optimizing operator-agent interaction. These AIAs provide interface aids as a means of reducing operator workload, and increasing situation awareness and operational effectiveness. The framework and associated IAI models provide guidance to design a knowledge-based system, such as a UAV control station interface.

Towards Contextualized Information Delivery: A Rule-based Architecture for the Domain of Mobile Police Work

Abstract: An increasing number of mobile users demand adaptive services tailored to their specific requirements in a particular situation. Typically, when carrying out the task at hand, police officers need to have up-to-date information contextualized to their current situation in order to support their decision making. In contrast to the traditional work environments in which workers are involved in standard office work, the situations in which mobile workers perform their tasks are characterized by various types of context. This feature requires the designers of a system serving those mobile users to understand which context dimensions might influence the users’ information needs; thus, developers must find solutions that enable applications to adapt their behaviour to the current context without consuming too much of users’ attentions. In this thesis we designed a generic architecture supporting delivery of contextualized information that can assist mobile users to take decisions during their activities. Within the MOSAIC project, aiming to enhance situation awareness of emergency responders, we developed a rule-based system which can assess the relevance of information items by taking into account the contextual situations police officers are involved in. Following our quantitative evaluation method, we evaluated the effectiveness and adaptability our system based on realistic scenarios in cooperation with end-users. The competitive results have shown that the system can select adequately relevant information for end-users given their contexts and thus demonstrated the feasibility of the architecture we designed.

Information Fusion Measures of Effectiveness (MOE) for Decision Support

Abstract: For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

Antecedents of trust in human-robot collaborations

Abstract: Robotic systems are being introduced into military echelons to extend warfighter capabilities in complex, dynamic environments. While these systems are designed to complement human capabilities (e.g., aiding in battlefield situation awareness and decision making, etc), they are often misused or disused because the user does not have an appropriate level of trust in his or her robotic counterpart(s). We describe a continuing body of research that identifies factors impacting a human's level of trust in a robotic teammate. The factors identified to date can be categorized as human influences (e.g., individual differences in terms of personality, experience, culture), machine influences (e.g., robotic platform, robot performance in terms of levels of automation, failure rates, false alarms), and environmental influences (e.g. task type, operational environment, shared mental models). A framework for human-robot team trust was constructed, which is evolving into a working model contingent upon the results of an on-going meta-analysis.

Situational Awareness in Driving

Abstract: This chapter focuses on three questions. First, what are the attentional component processes that drivers use to maintain situation awareness (SA)? Second, how can drivers’ ability to maintain SA, especially using driving simulators be measured? Third, what is the empirical evidence, especially using simulators, that the particular component processes described here are key parts of maintaining SA and that these components affect driving performance? SA is defined here as the updated, meaningful knowledge of a changing, multifaceted situation that drivers use to guide choice and action. Regarding the first question, it is proposed in this chapter that SA involves component processes of focal vision (including attention allocation within tasks, event comprehension, and task management across concurrent tasks) as well as ambient vision processes (including attention capture by sudden peripheral events). SA is a complex process that requires assessment by a variety of online (during driving) and offline (post-driving) measures. Driving simulators are used in many SA measures. Research using these measures shows that most of the above components of SA can be trained, improve with driving experience, and correlate positively with safe driving.

ENHANCED SITUATIONAL AWARENESS AND TARGETING (eSAT) SYSTEM

Abstract: eSAT pushes 3D scene awareness and targeting to forward positioned mobile operators and their handheld devices in environments such as found in military theaters of operation, border control and enforcement, police operations, search & rescue and large commercial industrial operations. A host computer hosts a 3D model of a scene and dynamically captures and transmits a windowed portion of the visual representation of that 3D model over a wireless network to the mobile operators' handheld devices. The windowed portion of the visual representation is streamed directly to the operators' handheld device displays. The mobile operators may interact with and control the 3D model via the wireless network. The host computer may synthesize the visual representation of the 3D model with live feeds from one or more of the handheld devices or other assets to improve situational awareness. Either the mobile operators or host operator can make point selections on the visual representation to extract geo-coordinates from the 3D model as a set of target coordinates.

Human Performance on the Flight Deck

Abstract: Contents: Preface A systems approach to human factors in aviation Part 1 The Science Base: Human information processing Workload Situation awareness Decision making Error Individual differences. Part 2 The (Hu)Man: Pilot selection Training and simulation Stress, fatigue and alcohol Environmental stressors. Part 3 The Machine: Display design Aircraft control Automation Human-computer interaction (HCI) on the flight deck. Part 4 The Management: Flight deck safety management: crew resource management and line operations safety audits Airline safety management Incident and accident investigation Concluding thoughts: human factors in aviation as a route to increased operational efficiency References Index.

Driver's various information process and multi-ruled decision-making mechanism: a fundamental of intelligent driving shaping model

Abstract: The most difficult but important problem in advance driver assistance system development is how to measure and model the behavioral response of drivers with focusing on the cognition process. This paper describes driver's deceleration and acceleration behavior based on driving situation awareness in the car–following process, and then presents several driving models for analysis of driver's safety approaching behavior in traffic operation. The emphasis of our work is placed on the research of driver's various information process and multi-ruled decision-making mechanism by considering the complicated control process of driving; the results will be able to provide a theoretical basis for intelligent driving shaping model.

Modeling a probabilistic ontology for Maritime Domain Awareness

Abstract: Situational awareness and prediction are essential elements of information fusion. Both involve various types of uncertainty and require a sound automated inferential process. Probabilistic ontologies support uncertainty management in se-mantically aware systems, and facilitate modular, interoperable systems. This paper describes the process of developing a probabilistic ontology for a Maritime Domain Awareness application. The ontology was created to support identification of ships behaving suspiciously enough to be declared ships of interest. The original model was expanded in two ways: to provide reasons for declaring a ship as being of interest, and to include individual crew member associations. The latter is achieved by supporting inferences about a person's close relations, group associations, communications, and background influences to assess his likelihood of having terrorist links.

Optical Brain Imaging to Enhance UAV Operator Training, Evaluation, and Interface Development

Abstract: As the use of unmanned aerial vehicles expands to near earth applications and force multiplying scenarios, current methods of operating UAVs and evaluating pilot performance need to expand as well. Many human factors studies on UAV operations rely on self reporting surveys to assess the situational awareness and cognitive workload of an operator during a particular task, which can make objective evaluations difficult. Functional Near-Infrared Spectroscopy (fNIR) is an emerging optical brain imaging technology that monitors brain activity in response to sensory, motor, or cognitive activation. fNIR systems developed during the last decade allow for a rapid, non-invasive method of measuring the brain activity of a subject while conducting tasks in realistic environments. This paper investigates deployment of fNIR for monitoring UAV operator's cognitive workload and situational awareness during simulated missions. The experimental setup and procedures are presented with some early results supporting the use of fNIR for enhancing UAV operator training, evaluation and interface development.

Toward situation awareness for the semantic sensor web: complex event processing with dynamic linked data enrichment

Abstract: Over the past few years there has been a proliferation in the use of sensors within different applications. The increase in the quantity of sensor data makes it difficult for end users to understand situations within the environments where the sensors are deployed. Thus, there is a need for situation assessment mechanisms upon the sensor networks to assist users to interpret sensor data when making decisions. However, one of the challenges to realize such a mechanism is the need to integrate real-time sensor readings with contextual data sources from legacy systems. This paper tackles the data enrichment problem for sensor data. It builds upon Linked Data principles as a valid basis for a unified enrichment infrastructure and proposes a dynamic enrichment approach that sees enrichment as a process driven by situations of interest. The approach is demonstrated through examples and a proof-of-concept prototype based on an energy management use case.

Traffic Incident Management: A Common Operational Picture to Support Situational Awareness of Sustainable Mobility

Abstract: Successful traffic incident management presupposes a multi-disciplinary approach. To meet appropriately the safety and mobility needs of all affected parties, traffic incidents call for a high level of collaboration and coordination of involved agencies. Effective traffic incident management activities rely in particular on flexible communications and information systems.Based on experiences from the military domain it is possible to develop strategic concepts that are related to the improvement of information sharing and collaboration. Such concepts can also be applied to enhanced traffic incident management information systems. The present paper aims to offer a review of the state of the art in this field and to illustrate the empirical usefulness and benefits of traffic incident management.

Automated personal assistance system

Abstract: An automated personal assistance system employing artificial intelligence technology that includes speech recognition and synthesis, situational awareness, pattern and behavioral recognition, and the ability to learn from the environment. Embodiments of the system include environmental and occupant sensors and environmental actuators interfaced to an assistance controller having the artificial intelligence technology incorporated therein to control the environment of the system. An embodiment of the invention is implemented as a vehicle which reacts to voice command for movement and operation of the vehicle and detects objects, obstructions, and distances. This invention provides the ability to monitor for the safety of operation and modify dangerous maneuvers as well as to learn locations in the environment and to automatically find its way to them. The system may also incorporate communication capability to convey patterns of environmental and occupant parameters and to a monitoring center.

New technologies for information retrieval to achieve situational awareness and higher patient safety in the surgical operating room: the MRI institutional approach and review of the literature.

Abstract: Background Technical progress in the operating room (OR) increases constantly, but advanced techniques for error prevention are lacking. It has been the vision to create intelligent OR systems ("autopilot") that not only collect intraoperative data but also interpret whether the course of the operation is normal or deviating from the schedule ("situation awareness"), to recommend the adequate next steps of the intervention, and to identify imminent risky situations. Methods Recently introduced technologies in health care for real-time data acquisition (bar code, radiofrequency identification [RFID], voice and emotion recognition) may have the potential to meet these demands. This report aims to identify, based on the authors' institutional experience and a review of the literature (MEDLINE search 2000-2010), which technologies are currently most promising for providing the required data and to describe their fields of application and potential limitations. Results Retrieval of information on the functional state of the peripheral devices in the OR is technically feasible by continuous sensor-based data acquisition and online analysis. Using bar code technologies, automatic instrument identification seems conceivable, with information given about the actual part of the procedure and indication of any change in the routine workflow. The dynamics of human activities also comprise key information. A promising technology for continuous personnel tracking is data acquisition with RFID. Emotional data capture and analysis in the OR are difficult. Although technically feasible, nonverbal emotion recognition is difficult to assess. In contrast, emotion recognition by speech seems to be a promising technology for further workflow prediction. Conclusion The presented technologies are a first step to achieving an increased situational awareness in the OR. However, workflow definition in surgery is feasible only if the procedure is standardized, the peculiarities of the individual patient are taken into account, the level of the surgeon's expertise is regarded, and a comprehensive data capture can be obtained.

Modeling situation awareness in human-like agents using mental models

Abstract: In order for agents to be able to act intelligently in an environment, a first necessary step is to become aware of the current situation in the environment. Forming such awareness is not a trivial matter. Appropriate observations should be selected by the agent, and the observation results should be interpreted and combined into one coherent picture. Humans use dedicated mental models which represent the relationships between various observations and the formation of beliefs about the environment, which then again direct the further observations to be performed. In this paper, a generic agent model for situation awareness is proposed that is able to take a mental model as input, and utilize this model to create a picture of the current situation. In order to show the suitability of the approach, it has been applied within the domain of F-16 fighter pilot training for which a dedicated mental model has been specified, and simulations experiments have been conducted.

User interface design for military AR applications

Abstract: Designing a user interface for military situation awareness presents challenges for managing information in a useful and usable manner. We present an integrated set of functions for the presentation of and interaction with information for a mobile augmented reality application for military applications. Our research has concentrated on four areas. We filter information based on relevance to the user (in turn based on location), evaluate methods for presenting information that represents entities occluded from the user’s view, enable interaction through a top-down map view metaphor akin to current techniques used in the military, and facilitate collaboration with other mobile users and/or a command center. In addition, we refined the user interface architecture to conform to requirements from subject matter experts. We discuss the lessons learned in our work and directions for future research.

A Coherent Impression of the Pilots' Situation Awareness: Studying Relevant Human Factors Tools

Abstract: A flight simulator experiment was set up to study relevant human factors tools for situation awareness assessment of pilots. A specific scenario was designed in which a malfunction of the aircraft was introduced during flight: an indicated air speed discrepancy. Pilot behavior was studied while pilots tried to figure out the correct speed. Eye movement metrics alone provided an insufficient picture of pilot situation awareness, but when purposefully combined with subjective, self-rating metrics, they offered a more comprehensive look at situation awareness, covering all 3 levels of Endsley's situation awareness definition.

Going Beyond Citizen Data Collection with Mapster: A Mobile+Cloud Real-Time Citizen Science Experiment

Abstract: Citizens have always played an important role in emergency management such as urban flooding response. New information and communication technologies such as smart phones and computer-based social networks have great potential to transform the roles of citizens in emergency management. However, current digital citizen science projects are usually limited in three areas: 1) limited one-way citizen participation, 2) no processing and integration of citizens' reports with other existing infrastructure sensing data, 3) no personalized near-real-time spatiotemporal visualization tools for citizens to instantly view aggregated data to gain updated situational awareness. We developed a Mapster application that specifically addresses these issues. First, we leveraged Twitter's geo-referenced tweets functionality to design a customized smart phone application for citizens to report a set of events that have been identified in past urban flooding situations such as "basement flooding" and "powerline down" etc. Second, a Cloud-based semantic streaming data harvesting and processing tool was developed to fetch and process both the Twitter feeds and other infrastructure sensing data such as US National Weather Service's radar data. Third, a user can instantly explore the heterogeneous data processed and provided by the Cloud service through a map-based spatiotemporal animation tool on the smart phone to see how all the events evolve before, during, and after a storm. Such a two-way information flow significantly improves citizen participation and their sense of situational awareness. We present our architecture, implementation, and discussion of issues on citizen science data collection platforms, integration of heterogeneous data sources and future work plan.

Cyber situation awareness: modeling the security analyst in a cyber-attack scenario through instance-based learning

Abstract: In a corporate network, the situation awareness (SA) of a security analyst is of particular interest. A security analyst is in charge of observing the online operations of a corporate network (e.g., an online retail company with an external webserver and an internal fileserver) from threats of random or organized cyber-attacks. The current work describes a cognitive Instance-based Learning (IBL) model of the recognition and comprehension processes of a security analyst in a simple cyber-attack scenario. The IBL model first recognizes cyber-events (e.g., execution of a file on a server) in the network based upon events' situation attributes and the similarity of events' attributes to past experiences (instances) stored in analyst's memory. Then, the model reasons about a sequence of observed events being a cyber-attack or not, based upon instances retrieved from memory and the risk-tolerance of a simulated analyst. The execution of the IBL model generates predictions of the recognition and comprehension processes of security analyst in a cyber-attack. An analyst's decisions are evaluated in the model based upon two cyber SA metrics of accuracy and timeliness of analyst's decision actions. Future work in this area will focus on collecting human data to validate the predictions made by the model.