A Harmonized ENC Database as a Foundation of Electronic Navigation

Enhancements in shipboard automation offer the prospect of crew size reductions for navigation tasks. This work was concerned with comparing the structure of navigation tasks using paper charts with the same tasks accomplished using an electronic chart display information system (ECDIS). Voyage planning with paper charts is based on drawing specific voyage segments, measuring distances between waypoints, and annotating the chart with voyage specific information. These tasks change substantially with electronic charts, particularly in terms of how the task is accomplished. Similarly, the manual activities of track keeping are reduced, but the need for a continuous record maintains the use of the paper chart. For both navigation tasks, there is less ability to visualize geographic features continuously with electronic charts because of the keyhole effect created by a CRT display. It is concluded that the design of automated navigation aids should be based not only on the informational aspects of task performance, but also the functional means by which navigators carry out their tasks with conventional technologies such as paper charts.

Situation awareness (SA) has been widely studied in human operator scenarios as it can have broad effects on operator performance. However, SA often develops and changes rapidly, especially during highly-demanding operational tasks such as piloting aircraft or responding to emergencies. In order to enable just-in-time interventions, systems must have access to the current state of operator SA. Gold-standard objective measures of situation awareness require freeze-probes, a metric that cannot be implemented in operational settings. In order to access this dynamic cognitive state, this work seeks to develop unobtrusive and real-time methods for estimating SA. To do so, this study collected psychophysiological, neurophysiological, behavioral, and performance data while subjects complete the MATB-II, a complex task-battery designed to imitate high-workload scenarios. The task was frozen at regular intervals and subjects completed objective SA assessments and workload questionnaires. Between these freeze-probes, task difficulty and priority were manipulated in order to induce a variety of SA outcomes. Data streams including fNIRS, EEG, ECG, eye-tracking, heart rate, EDA, respiration, and in-task behavioral measures were collected. In correlational analysis, features such as heart rate and skin conductance response were found to be potentially useful data streams for estimating situation awareness. In future work, these and other data streams will be incorporated into models to predict scores on objective freeze-probe assessments in real-time.

The aim of this study is to find out whether the provision of situation awareness (SA) information is effective in reducing the transition time that the control authority is transited from automated driving to manual driving. In the level 3 automobile, the driver must remain in a fallback ready state in which he/she can drive at any time, so a human factor study of the driver's SA is required. Thirty-six drivers licensed performed three experiment scenarios with an automated driving feature activated. Participants were asked to enter some sentence with his/her smartphone in the automated driving section. Situation information was provided differently for each stage of the experiment using the iPad installed in the vehicle. We measured the driver's response time to the takeover request (TOR), the time to start the manual driving, and the stabilization time. We found that the time required to recognize and respond to TOR was reduced in the case of providing SA information. We recommend that level 3 automobile manufacturers design their vehicles to provide SA information to ensure the driver's safety.

Visual signals that integrate situation awareness (SA) information can improve drivers’ performance when assuming control of a partially automated vehicle from a non-driving task. By incorporating information that follows the levels of model of SA, these signals enable drivers to more quickly attain higher levels of SA, potentially bypassing traditional sequential processing stages. We evaluated the effectiveness of such SA-enhanced signals (displayed in the instrument cluster) in a simulated driving study by measuring takeover times and collision occurrences. The results indicate that incorporating SA information about the environment significantly reduces the time needed for drivers to take control of the vehicle and results in fewer collisions. Considering these performance differences suggests insights on optimizing visual cues to quickly restore SA among drivers as they transition from non-driving activities to manual control in partially automated vehicles.

The effect of adaptive display on pilot performance was assessed using objective and subjective measures from a sequence of 2 experiments. Combat aircraft advances have led to dramatic increases in the operational tempo facing the pilot, increasing the likelihood for situation awareness (SA) failures, biases, and information overload. Prior research suggests that adaptive interface might help to address this issue. We designed and evaluated 2 interfaces targeted at problem areas for pilots: a weapons employment zone (WEZ) display designed to support awareness of combat geometry, and an adaptive border display (ABD) designed to warn pilots of impending border infractions breaking rules of engagement. Two experiments were conducted under various levels of scenario complexities to test the ability of these displays to improve SA, reduce workload, and improve objective performance in a population of licensed civilian pilots. Study 1 results showed that the WEZ display significantly improved both performance and SA, and reduced workload. However, there were no significant effects of the ABD. In the second study, we used substantially more complex scenarios to investigate the effect of the ABD. Results showed that in these situations, the ABD had a significant effect on improving pilot performance. The results demonstrate the value of adaptive visualizations that highlight key aspects of the environment that impact performance. Although the results have verified the benefits of adaptive displays, more sensitive performance measures and diverse test pilots are suggested in the future to provide more confidence in applying these findings. Potential application of this research includes modern aircraft cockpit interface design under complex combat scenarios.

Exchanging of Situation Awareness information is extremely important for organizations in order to survive as part of the cyber domain. The situation Awareness is required for decision making and for an early warning of upcoming threats. Situation Awareness and the security information in the cyber domain differ from the kinetic domain. Because of that, Situation Awareness has different requirements and use cases, for example when considering time or geographical distances. There is always a risk when sharing security information due to the classified nature of the information. It might contain information of weaknesses or vulnerabilities of the organization, and if used wrongly it jeopardizes the continuity of the business or mission. The model introduced in this paper for creating information sharing topologies enables sharing of classified security related information between multiple organizations with the lowest possible risks levels.

BackgroundInformation from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation.ObjectiveThis study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak.MethodsWe developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation.ResultsThe tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak.ConclusionsAIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

Electronic navigation, although still relatively new, is becoming increasingly more common, particularly on commercial vessels. This handbook offers a wealth of detailed information about how different charting systems operate and answers the most commonly asked questions regarding electronic charts (ENC, RNC, DNC) and electronic chart systems (ECD

Situation awareness is knowing what is going on in the situation. Clinicians working in the emergency medical services (EMS) encounter numerous situations in various conditions, and to be able to provide efficient and patient safe care they need to understand what is going on and possible projections of the current situation. The design of this study encompassed a Goal-Directed Task analysis where situation awareness information requirements were mapped in relation to goals related to various aspects of the EMS mission. A group of 30 EMS subject matter experts were recruited and answered a web-based survey in three rounds related to what they though themselves or a colleague might need to achieve situation awareness related to the specific goals of various situations. The answers were analysed using content analysis and descriptive statistics. Answers reached consensus at a predetermined level of 75%. Those who reached consensus were entered into the final goal-directed task analysis protocol. The findings presented that EMS clinicians must rely on their own, or their colleagues prior experience or knowledge to achieve situation awareness. This suggests that individual expertise plays a crucial role in developing situation awareness. There also seems to be limited support for situation awareness from organizational guidelines. Furthermore, achieving situation awareness also involves collaborative efforts from the individuals involved in the situation. These findings could add to the foundation for further investigation in this area which could contribute to the development of strategies and tools to enhance situation awareness among EMS clinicians, ultimately improving patient care and safety.

Advanced robots are desired to improve their reasoning ability in increasingly practical situations to quickly respond to urgent challenges such as search and rescue, public security, and autonomous driving. Advances in sensor technology and intelligent algorithms are accelerating the development of autonomous systems, allowing them to interact with dynamic environments more independently and intelligently, completing relatively complicated tasks. Nevertheless, the safety and security problems are yet to be comprehensively addressed. Currently, many efforts have been made to solve this problem by improving the unimodal perception ability of intelligent systems. Methods using surveillance cameras to collect large amounts of visual data and process them using deep networks. Various types of range scanner can sense and reconstruct the surrounding environment through precise distance measurements. However, in the above cases, there may be a lack of training data, fixed cameras cannot adequately and dynamically model the surroundings, unimodal analysis is hard to capture both appearance and depth information. Therefore, higher levels of perception and reasoning abilities are required to handle unexpected conditions and scenarios efficiently. In such cases, mobile robots should be fully aware of the dynamic environment, prioritize, and continuously analyze critical situations, share information, and collaboration under limited computing power and resources. Furthermore, considering the robots are operating alongside humans, reliable and interpretable robot behavior is highly preferred for more natural interactions.
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\nThis study aims to develop reasoning strategies for autonomous robots to improve robotic intelligence under different assumptions and conditions. The framework allows one or several robots to address safety or security problems in a targeted manner in partially known environments, where time, dynamics, and interactions play a significant role. The objective is to identify the unique individual whose activity, in space and time, appear different from the other people in the scene. The activities considered not only the motion pattern, but also the object associated with this person. Unlike traditional abnormal detection, the uniqueness reasoning in this work does not require a predefined expected pattern. A modular analysis is also applied to decompose the entire reasoning process into submodules such as perception, analysis, and inference, to achieve algorithmic flexibility and interpretability. Probabilistic modeling is used to associate spatial relationships between semantic labels and their temporal changes. The relationships are described in either instant spatial spaces (semantic-interaction feature) or spatiotemporal spaces (spatiotemporal feature), and it provides essential information for robot navigation, situation awareness, and task planning. Unlike existing research, this is the first time that such a mission is implemented on mobile robot platforms by using the probabilistic fusion of multimodal information.
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\nConsidering how evidence information is obtained and linked in the process of reasoning, we study the problem of uniqueness reasoning at three levels. The first level is called knowledge-based single robot uniqueness reasoning and runs on a single robot platform. At this level, we focus on a knowledge-based approach, where knowledge is provided in terms of how motion and an object associated with a person can be used to infer his/her uniqueness, as determined by human expertise. Because such a method has advantages in dealing with structured missions and with limited training data. Qualitative rules are applied to extract human activities from observations, and then the uniqueness of each person is judged by associating the prior knowledge with the performed activities. Experiments were conducted in indoor environments with various degrees of occlusion, and the experimental results showed the rationality and accuracy of the reasoning.
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\nHowever, though the knowledge-based method can successfully address specific tasks, it lacks flexibility and generality to adapt to different situations. The assumption of prior knowledge should be replaced by general analytical modeling. Thus, the second level is analytics-based single robot uniqueness reasoning. For one thing, it applies quantitative analysis to model human activities from observations rather than using qualitative rules. For another, instead of leveraging prior knowledge, the distribution of the features characterizing a human activity is constructed and the activity of an individual is analyzed within the distribution to determine if it is unique. Experiments were conducted in both indoor and urban outdoor environments. The experimental results demonstrated the rationality, accuracy, and efficiency of the reasoning.
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\nMoreover, since many applications require to work in an unstructured and large-scale environment as well as a long-term operation, the sensing and reasoning capabilities of a single robot are limited by occlusion, sensor failure, and narrow field of view. Robots are desired to share information and perform collaborative reasoning. Therefore, if say the first two levels are mainly using sensory data to implement the algorithm, the third level is more focused on applying the algorithm to the robot platform. The third level is analytics-based multi-robot uniqueness reasoning, in which a multi-robot system is adopted as the platform. By obtaining the relative pose between robots, local observations can be transformed and transmitted among the multi-robot system. Since it is a distributed system, each robot still first performs its local uniqueness reasoning in the case of long-distance and poor communication. Meanwhile, the system performs a re-judgment based on the shared global observation and then shares the judgment results back to each robot. Experiments were carried out in various unstructured environments and contained day and night situations. The experimental results showed the rationality, accuracy, and robustness of the reasoning. This is the first time that reasoning is categorized by considering the way of obtaining and linking evidence information. This study has potential applications in a variety of situations, and it can serve as a guide for further robotic motion planning and human-robot interaction.

Versions of this paper were first presented at the Royal Institute of Navigation GNSS Vulnerabilities and Solutions Conference held at Baska, Croatia in September 2008 and the Royal Institute of Navigation NAV 08 Conference held at Church House, Westminster, London in October 2008.The US Global Positioning System (GPS) is currently the primary source of Position, Navigation and Timing (PNT) information in maritime applications, whether stand-alone or augmented with additional systems. This situation will continue in the future with GPS, possibly together with other Global Navigation Satellite Systems (GNSS) e.g. Galileo, being the core PNT technology for e-Navigation – the future digital maritime architecture. GPS signals, measured at the surface on the Earth, are very weak. As such, the system is vulnerable to unintentional interference and jamming, resulting in possible denial of service over large geographical areas. The result of such interference could be the complete failure of the mariner's GPS receiver or, possibly worse, the presentation to the mariner of hazardously misleading information (HMI) for navigation and situational awareness, depending on how the GPS receiver reacts to the jamming incident. Recognising this, the General Lighthouse Authorities of the United Kingdom and Ireland (GLA), in collaboration with the UK Ministry of Defence (MOD) Defence Science and Technology Laboratory (DSTL), have conducted a series of sea-trials with the aim of identifying the full effects of GPS jamming on safe navigation at sea.This paper presents the key findings of these trials and provides important information on the effect of GPS denial. The GLA are playing a pivotal role in the establishment of eLoran as an independent source of PNT, taking advantage of eLoran's complementary nature, having dissimilar failure modes to GPS and the future GNSS. This paper provides information on the performance of an eLoran receiver in an area of GPS service denial. The paper presents the rationale for the work, details the system architecture employed, the data gathering efforts and finally the data analysis procedures, results and conclusions.

Emergency response teams must coordinate their actions and goals in dynamic and demanding situations to ensure patient health and safety. It is a major challenge to measure and model such dynamic phenomenon under real world conditions. However, measuring coordination in real world conditions is necessary to better understand how teams function and to enhance their individual situation awareness, coordination, and learning. The use of sensor-based and video-based behavioral measurement of coordination within and between teams has been a suggested method for continuously recording dynamic, physical behavioral data streams related to proximity, movement or positioning to inform the team. However, very few studies have attempted to implement unobtrusive sensor-based data collection, especially in messy and challenging conditions. If we are to truly embrace the dynamic and complex nature of team-based behaviors and learning with new forms of data collection such as unobtrusive sensors, digital video and audio, we need to better understand the reality of the difficulties of collecting, processing and integrating these multiple digital data streams in real time to generate meaning in demanding, continually evolving, real world contexts such as emergency response. In the current paper, we strive to advance our understanding of the ground truth of leveraging sensor-based and other digital data streams to identify valid constructs, uncover meaningful indicators, and target relevant combinations of digital data streams to visualize and improve understanding of how teams coordinate and learn in team environments.

In recent years, oilfield companies have realized the value of real-time data and have set up real-time centers to monitor multiple wells in a given area remotely. With the advancement of rig communications, several wells can be streamed to these real-time centers where they are monitored by real-time engineers. In addition to the data streamed from both surface and downhole sensors to real-time centers, operational information is communicated electronically via emails, daily reports, and meetings; this information must be manually entered or interpreted by real-time engineers. We have observed that this takes precious time away from the real-time engineers’ core function of monitoring. However, as real-time centers mature, there must be a focus on merging operational information with real-time streaming data to reduce communication lag time and raise the real-time engineer’s situational awareness at all times. Coupling business systems with real-time monitoring software will have an immediate impact on the entire process, reducing communication time and instantaneously syncing the real-time center with rig operations. A first step towards this goal is to provide the real-time monitoring software with the ability to pull in and merge operational data with streamed data from the wellsite. Operational data can then be viewed on demand and updated automatically as well as visualized together with the streamed data through predefined dashboards. Up-to-date operations information changes will be synchronized to the real-time monitoring systems and brought to the attention of the real-time engineers. As a result of the implementation, a step change has been observed in the situational awareness of real-time engineers, improving their interpretation of the current situation and their influence on operational changes. Finally, there remains a need to move towards digital information exchange between these systems in both directions to provide a more collaborative environment.

FDA's Division of Cooperative Programs (DCP) has used non-computerized instrumentation extensively and proficiently for its specialized coastal water pollution studies that assist state management of shellfish harvest waters. This paper summarizes the instrumentation development portion of a September 1998 fluorometric study in coastal Connecticut. Fluorometry was used to study estuarine dispersion, travel time, and dilution of wastewater treatment plant discharge, yet the computerized hydrography system (CHS) can be used with any instrument that supports real-time ASCII (or NMEA 0183) output. Leading up to this instrumentation development were technology advancements for geographical positioning systems (GPS), fluorometers, portable PCs, electronic nautical charts, and navigation and hydrographic software. Prior to this study, only one off-the-shelf software application (Chartview Pro) was found that had a feature intended to computer log and display instrument data correlated to a plot of the vessel track on electronic nautical charts, both real-time and after the study. Yet this hydrographic feature had not been implemented by an end-user, until FDA-DCP staff developed a computerized hydrography system (CHS) that included Chartview Pro, and the software developer responded to feedback from FDA-DCP instrumentation trials. Stability as well as the responsiveness of the software company were some criteria for the CHS software, as was low cost, and therefore the off-the-shelf appeal. Versatility was also important. A purpose of developing the CHS was to update the hydrography instrumentation used by FDA-DCP and its cooperative program partners.