Showing papers on "Situation awareness published in 2016"

Power system restoration: a literature review from 2006 to 2016

Abstract: Power system restoration has attracted more attention and made great progress recently. Research progress of the power system restoration from 2006 to 2016 is reviewed in this paper, including black-start, network reconfiguration and load restoration. Some emerging methods and key techniques are also discussed in the context of the integration of variable renewable energy and development of the smart grid. There is a long way to go to achieve automatic self-healing in bulk power systems because of its extreme complexity. However, rapidly developing artificial intelligence technology will eventually enable the step-by-step dynamic decision-making based on the situation awareness of supervisory control and data acquisition systems (SCADA) and wide area measurement systems (WAMS) in the near future.

Incremental deployment and migration of geo-distributed situation awareness applications in the fog

Abstract: Geo-distributed Situation Awareness applications are large in scale and are characterized by 24/7 data generation from mobile and stationary sensors (such as cameras and GPS devices); latency-sensitivity for converting sensed data to actionable knowledge; and elastic and bursty needs for computational resources. Fog computing [7] envisions providing computational resources close to the edge of the network, consequently reducing the latency for the sense-process-actuate cycle that exists in these applications. We propose Foglets, a programming infrastructure for the geo-distributed computational continuum represented by fog nodes and the cloud. Foglets provides APIs for a spatio-temporal data abstraction for storing and retrieving application generated data on the local nodes, and primitives for communication among the resources in the computational continuum. Foglets manages the application components on the Fog nodes. Algorithms are presented for launching application components and handling the migration of these components between Fog nodes, based on the mobility pattern of the sensors and the dynamic computational needs of the application. Evaluation results are presented for a Fog network consisting of 16 nodes using a simulated vehicular network as the workload. We show that the discovery and deployment protocol can be executed in 0.93 secs, and joining an already deployed application can be as quick as 65 ms. Also, QoS-sensitive proactive migration can be accomplished in 6 ms.

Model Selection Management Systems: The Next Frontier of Advanced Analytics

Abstract: John Boyd recognized in the 1960's the importance of situation awareness for military operations and introduced the notion of the OODA loop (Observe, Orient, Decide, and Act). Today we realize that many applications have to deal with situation awareness: Customer Relationship Management, Human Capital Management, Supply Chain Management, patient care, power grid management, and cloud services management, as well as any IoT (Internet of Things) related application; the list seems to be endless. Situation awareness requires applications to support the management of data, knowledge, processes, and other services such as social networking in an integrated way. These applications additionally require high personalization as well as rapid and continuous evolution. They must provide a wide variety of operational and functional requirements, including real time processing.Handcrafting these applications is an almost impossible task requiring exhaustive resources for development and maintenance. Due to the resources and time involved in their development, these applications typically fall way short of the desired functionality, operational characteristics, and ease and speed of evolution. We - the authors - have developed a model enabling the development and maintenance of situation-aware applications in a declarative and therefore economical manner; we call this model KIDS - Knowledge Intensive Data-processing System.

Context-based detection of pedestrian crossing intention for autonomous driving in urban environments

Abstract: This paper focuses on the detection of pedestrian crossing intention to improve the situation awareness for autonomous driving in urban environments. A new definition of pedestrian crossing intention is discussed, which allows self-driving vehicles to identify pedestrians, whose intended actions are relevant for the own behavior planning, at an early stage. We propose a context-based feature descriptor in combination with a SVM classifier for detecting this. The descriptor captures the movement of a pedestrian relative to the road and the spatial layout of other scene elements in a generic manner. The performance of the feature descriptor is evaluated in relation to various SVM setups. Feasibility of the approach is demonstrated with data captured on-board of a vehicle in real inner-city traffic. The evaluation of the classification results confirms, that context-based data is a promising indicator for pedestrian crossing intention and that the proposed feature descriptor is capable of representing this. It is further shown that a lack of information about the pedestrian's posture and body movement results in a delayed detection of the pedestrians changing their crossing intention when compared to a human observer.

Forest Monitoring and Wildland Early Fire Detection by a Hierarchical Wireless Sensor Network

Abstract: A wildland fire is an uncontrolled fire that occurs mainly in forest areas, although it can also invade urban or agricultural areas. Among the main causes of wildfires, human factors, either intentional or accidental, are the most usual ones. The number and impact of forest fires are expected to grow as a consequence of the global warming. In order to fight against these disasters, it is necessary to adopt a comprehensive, multifaceted approach that enables a continuous situational awareness and instant responsiveness. This paper describes a hierarchical wireless sensor network aimed at early fire detection in risky areas, integrated with the fire fighting command centres, geographical information systems, and fire simulators. This configuration has been successfully tested in two fire simulations involving all the key players in fire fighting operations: fire brigades, communication systems, and aerial, coordination, and land means.

Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance

Abstract: Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles (AV) are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about the driving environment. Studies have proved that automated driving have the potential to decrease traffic congestion by reducing the time headway (THW), enhancing the traffic capacity and improving the safety margins in car following. Despite different encouraging factors, automated driving raise some concerns such as possible loss of situation awareness, overreliance on automation and system failure. This paper aims to investigate the effects of AV on driver’s behavior and traffic performance. A literature review was conducted to examine the AV effects on driver’s behavior. Findings from the literature survey reveal that conventional vehicles (CV), i.e. human driven, which are driving close to a platoon of AV with short THW, tend to reduce their THW and spend more time under their critical THW. Additionally, driving highly AV reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of AV on traffic performance, a simulation case study consisting of a 100% AV scenario and a 100% CV scenario was performed using microscopic traffic simulation. Outputs of this simulation study reveal that the positive effects of AV on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably improved by 8.09% during p.m. peak hours in the AV scenario, while the average travel speed enhanced relatively by 8.48%. As a consequent, the average travel time improved by 9.00% in the AV scenario. The outcome of this study jointly with the previous driving simulator studies illustrates a successful practice of microscopic traffic simulation to investigate the effects of AV. However, further development of the microscopic traffic simulation models are required and further investigations of mixed traffic situation with AV and CV need to be conducted.

Pilot error versus sociotechnical systems failure: a distributed situation awareness analysis of Air France 447

Abstract: The Air France 447 crash occurred in 2009 when an Airbus A330 stalled and fell into the Atlantic Ocean, killing all on board. Following a major investigation, it was concluded that the incident resulted from a series of events that began when the autopilot disconnected after the aircraft's Pitot tubes froze in an adverse weather system. The findings place scrutiny on the aircrew's subsequent lack of awareness of what was going on and of what procedure was required, and their failure to control the aircraft. This article argues that this is inappropriate, instead offering a systems level view that can be used to demonstrate how systems, not individuals, lose situation awareness. This is demonstrated via a distributed situation awareness-based description of the events preceding the crash. The findings demonstrate that it was the sociotechnical system comprising aircrew, cockpit and aeroplane systems that lost situation awareness, rather than the aircrew alone.

Enforcing situation awareness with granular computing: a systematic overview and new perspectives

Abstract: Situation Awareness is defined by Endsley as “the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future” and it deals with the continuous extraction of environmental information and its integration with prior knowledge for directing further perception and anticipating future events. To realize systems for Situation Awareness, individual pieces of raw information (e.g. sensor data) should be interpreted into a higher, domain-relevant concept called “situation”, which is an abstract state of affairs interesting to specific applications. The power of using “situations” lies in their ability to provide a simple, human-understandable representation of, for instance, sensor data. The aim of this work is to propose an overview of the applications of Computational Intelligence and Granular Computing for the implementation of systems supporting Situation Awareness. In this scenario, several and heterogeneous Computational Intelligence models and techniques (e.g. Fuzzy Cognitive Maps, Fuzzy Formal Concept Analysis, Dempster–Shafer Theory of Evidence, Ontologies, Knowledge Reasoning, Evolutionary Computing, Intelligent Agents) can be employed to implement such systems. Moreover, in a Situation Identification process, huge volumes of heterogeneous data need processing (e.g. fusion). With respect to this issue, Granular Computing is an information processing theory for using “granules” (e.g. subsets, intervals, fuzzy sets) effectively to build an efficient computational model for dealing with the above-mentioned data. The overview is proposed coherently to both methodological and architectural viewpoints for Situation Awareness.

Analyzing the applicability of Internet of Things to the battlefield environment

Abstract: As the Internet of Things (IoT) matures in commercial sectors, the promise of diverse new technologies such as data-driven applications, intelligent adaptive systems, and embedded optimized automation will be realized in every environment. An immediate research question is whether contemporary IoT concepts can be applied also to military battlefield environments and can realize benefits similar to those in industry. Military environments, especially those that depend on tactical communications, are much more challenging than commercial environments. Thus it is likely many commercial IoT architectures and technologies may not translate into the military domain and others will require additional research to enable deployment and efficient implementation. This paper investigates these issues and describes potential military operational activities that could benefit from commercial IoT technologies, including logistics, sensing/surveillance, and situation awareness. In addition, the paper lays out a roadmap for future research necessary to leverage IoT and apply it to the tactical battlefield environment.

Introspective perception: Learning to predict failures in vision systems

Abstract: As robots aspire for long-term autonomous operations in complex dynamic environments, the ability to reliably take mission-critical decisions in ambiguous situations becomes critical. This motivates the need to build systems that have situational awareness to assess how quali ed they are at that moment to make a decision. We call this self-evaluating capability as introspection. In this paper, we take a small step in this direction and propose a generic framework for introspective behavior in perception systems. Our goal is to learn a model to reliably predict failures in a given system, with respect to a task, directly from input sensor data. We present this in the context of vision-based autonomous MAV flight in outdoor natural environments, and show that it effectively handles uncertain situations.

Designing for Situation Awareness of Future Power Grids: An Indicator System Based on Linear Eigenvalue Statistics of Large Random Matrices

Abstract: Future power grids are fundamentally different from current ones, both in size and in complexity. This trend imposes challenges for situation awareness (SA) based on classical indicators, which are usually model-based and deterministic. As an alternative, this paper proposes a statistical indicator system based on linear eigenvalue statistics (LESs) of large random matrices: 1) from a data modeling viewpoint, we build, starting from power flows equations, the random matrix models (RMMs) only using the real-time data flow in a statistical manner; 2) for a data analysis that is fully driven from RMMs, we put forward the high-dimensional indicators, called LESs that have some unique statistical features such as Gaussian properties; and 3) we develop a 3-D power map to visualize the system, respectively, from a high-dimensional viewpoint and a low-dimensional one. Therefore, a statistical methodology of SA is employed; it conducts SA with a model-free and data-driven procedure, requiring no knowledge of system topologies, units operation/control models, causal relationship, and so on. This methodology has numerous advantages, such as sensitivity, universality, speed, and flexibility. In particular, its robustness against bad data is highlighted, with potential advantages in cyber security. The theory of big data-based stability for online operations may prove feasible along with this line of work, although this critical development will be reported elsewhere.

Minimum Time to Situation Awareness in Scenarios Involving Transfer of Control from an Automated Driving Suite

Abstract: This research assessed the impact of vehicle automation on a driver’s ability to anticipate latent threats and to detect materialized hazards on the forward roadway. In particular, the minimum alert time before transfer of control was determined. This was the minimum time required after an autonomous driving suite (ADS) had been in full control of a vehicle for the driver to reacquire the same level of situation awareness that he or she had when in full control of the vehicle. This simulator study included five treatment conditions during which drivers either were always in complete control of their own vehicle (control) or were required to resume control at 4 s, 6 s, 8 s, or 12 s before the appearance of a latent hazard (transfer). While the vehicle was in autonomous mode, the drivers performed an in-vehicle task for more than a minute and were told not to glance at the forward roadway. Analysis of eye movements showed that drivers in the control condition detected nearly 40% more hazards compared with d...

Towards A Taxonomy of Autonomous Vehicle Handover Situations

Abstract: This paper proposes a taxonomy of autonomous vehicle handover situations with a particular emphasis on situational awareness. It focuses on a number of research challenges such as: legal responsibility, the situational awareness level of the driver and the vehicle, the knowledge the vehicle must have of the driver's driving skills as well as the in-vehicle context. The taxonomy acts as a starting point for researchers and practitioners to frame the discussion on this complex problem.

Towards trustworthy automation: User interfaces that convey internal and external awareness

Abstract: Suppose we are given an autonomous vehicle that has limitations, meaning that it may need to transfer control back to the human driver to guarantee safety in certain situations. This paper presents work on designing a user interface to assist this hand off by considering the effects of the expression of internal and external awareness. Internal awareness is the concept of knowing whether or not the system is confident in its ability to handle the current situation. External awareness is the concept of being able to identify the limitations as the car is driving in terms of situational anomalies. We conduct a user study to examine what information should be presented to the driver, as well as the effects of expressing these levels of awareness on the driver's situational awareness and trust in the automation. We found that expressing uncertainty about the autonomous system (internal awareness) had an adverse effect on driver experience and performance. However, by effectively conveying the automation's external awareness on the anomaly, improvements were found in the driver's situational awareness, increased trust in the system, and performance after the transfer of control.

Method and system for situational awareness for emergency response

Abstract: Disclosed are systems and methods for sending emergency alerts. In some embodiments, sensors and wearable devices may trigger and send the emergency alerts and/or warning signals via available communication devices. Multi-media emergency alerts are also disclosed that include situational awareness information for effective and efficient emergency response.

The Need for Safety and Cyber-Security Co-engineering and Standardization for Highly Automated Automotive Vehicles

Abstract: A key long-term trend is towards highly automated vehicles and autonomous driving. This has a huge impact, besides comfort and enabling people not able or allowed to drive, on sustainability of environmental-friendly urban road transport because the number of vehicles and parking space could considerably be reduced if called on command and left behind after use for the next call. This requires a considerable amount of functionality, sensors, actuators and control, situation awareness etc., and the integration into a new type of critical infrastructure based on communication between vehicles and vehicles and infrastructure for regional traffic management. Both, safety and security aspects have to be handled in a coordinated manner, affecting co-engineering, co-certification and standardization.

Towards "drone-borne" disaster management: future application scenarios

Abstract: . Information plays a key role in crisis management and relief efforts for natural disaster scenarios. Given their flight properties, UAVs (Unmanned Aerial Vehicles) provide new and interesting perspectives on the data gathering for disaster management. A new generation of UAVs may help to improve situational awareness and information assessment. Among the advantages UAVs may bring to the disaster management field, we can highlight the gain in terms of time and human resources, as they can free rescue teams from time-consuming data collection tasks and assist research operations with more insightful and precise guidance thanks to advanced sensing capabilities. However, in order to be useful, UAVs need to overcome two main challenges. The first one is to achieve a sufficient autonomy level, both in terms of navigation and interpretation of the data sensed. The second major challenge relates to the reliability of the UAV, with respect to accidental (safety) or malicious (security) risks. This paper first discusses the potential of UAV in assisting in different humanitarian relief scenarios, as well as possible issues in such situations. Based on recent experiments, we discuss the inherent advantages of autonomous flight operations, both lone flights and formation flights. The question of autonomy is then addressed and a secure embedded architecture and its specific hardware capabilities is sketched out. We finally present a typical use case based on the new detection and observation abilities that UAVs can bring to rescue teams. Although this approach still has limits that have to be addressed, technically speaking as well as operationally speaking, it seems to be a very promising one to enhance disaster management efforts activities.

Network safety situation awareness early-warning method and system based big data

Abstract: The invention discloses a network safety situation awareness early-warning method and system based big data, and the method comprises the steps: collecting intelligence information from the Internet, non-governmental organizations, governmental agencies and interiors of companies; obtaining log and network flow generated by safety equipment, network equipment, a host and other safety protection systems, carrying out the real-time preprocessing of the collected original data, carrying out the standardization of the data after preprocessing, and converting the data into safety data with the unified standard; carrying out scene modeling according to different attack behaviors, carrying out correlation analysis through combining with the intelligence information and the safety data, and generating early-warning information; carrying out the check processing of the early-warning information, and carrying out the visualized display of the early-warning information after check. The method can achieve the complete sensing of a safety situation, the early warning of safety threats and the capability of timely handling and responding of a safety event, and improves the overall safety protection capability of a power system.

Image classification to support emergency situation awareness

Abstract: Recent advances in image classification methods, along with the availability of associated tools, has seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of emergency situation awareness. We discuss image classification based on low level features as well as methods built on top of pre-trained classifiers. The performance of the classifiers are assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire related classes with an accuracy of 86%.

Experienced drivers are quicker to achieve situation awareness than inexperienced drivers in situations of transfer of control within a Level 3 autonomous environment

Abstract: Previous researchers examining transfers of control from semi-autonomous to manual driving have found that younger drivers engaged in a secondary task while in automated mode need at least 8 second...

A disaster response system based on human-agent collectives

Abstract: Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in the most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.

Improving situation awareness in crisis response teams

Abstract: In responding to an emergency, the actions of emergency response teams critically depend upon the situation awareness the team members have acquired Situation awareness, and the design of systems to support it, has been a focus in recent emergency management research In this paper, we introduce two interventions to the core processes of information processing and information sharing in emergency response teams to analyze their effect on the teams' situation awareness: (1) we enrich raw incoming information by adding a summary of the information received, and (2) we channel all incoming information to a central coordinator who then decides upon further distribution within the team The effect of both interventions is investigated through a controlled experiment with experienced professional responders Our results show distinctly different effects for information enrichment and centralization, both for the teams and for the coordinators within the team While the interaction effects of both conditions cannot be discerned, it is apparent that processing non-enriched information and non-centralized information sharing leads to a worse overall team situation awareness Our work suggests several implications for the design of emergency response management information systems Focuses on situation awareness in crisis response teamsProposes new approaches to information processing & sharing for situation awarenessReports on a controlled experiment conducted with professional emergency respondersShows the impact of enriched & centralized information on team situation awarenessSuggests design implications for emergency response information systems

FocusStack: Orchestrating Edge Clouds Using Location-Based Focus of Attention

Abstract: Allocating and managing resources in the Internet ofThings (IoT) presents many new challenges, including massivescale, new security issues, and new resource types that becomecritical in making orchestration decisions. In this paper, weinvestigate whether clouds of edge devices can be managed asInfrastructure-as-a-Service clouds. We describe our approach, FocusStack, that uses location based situational awareness, implemented over a multi-tier geographic addressing network, to solve the problems of inefficient awareness messaging andmixed initiative control that IoT device clouds raise for traditionalcloud management tools. We provide an extended casestudy of a shared video application as initial demonstrationand evaluation of the work and show that we effectively solvethe two key problems above.