Insufficient Situation Awareness Research Articles

The Role of ATC’s Situational Awareness in Wildlife Hazard Handling at the Aerodrome Control Unit Tower

The handling of wildlife hazard is an important aspect in ensuring flight safety at the Aerodrome Control Tower Unit of AirNav Indonesia, Denpasar’s Branch. In this context, situational awareness becomes a key factor that influences decision-making and actions of ATCs regarding wildlife hazard. This research aims to analyze the role of ATC's situational awareness in handling wildlife hazard at the mentioned unit. Data collection methods include field observations, interviews, and document analysis. The research findings indicate that wildlife hazard incidents at the I Gusti Ngurah Rai International Airport are quite high, but the lack of detection by ATCs poses a problem that can disrupt the smooth flow of air traffic services. This is primarily due to high workload and insufficient situational awareness of the dangers posed by wildlife hazard. Recommended solutions include conducting regular briefings, launching campaigns on wildlife hazard awareness, and enhancing situational awareness among ATCs. The research results are expected to provide insights and recommendations for improving flight safety concerning wildlife hazard at the Aerodrome Control Tower Unit of AirNav Indonesia, Denpasar’s Branch.

Application of HFACS and grounded theory for identifying risk factors of air traffic controllers’ unsafe acts

Unsafe acts of air traffic controllers (ATCers) are caused by various factors. Based on interview data and case reports, human factors analysis and classification system (HFACS) and the grounded theory were adopted to identify the risk factors of ATCers’ unsafe acts comprehensively. The interview data and the case data issued by the authority were first collected. Then, the above data were encoded to obtain the relevant concepts and categories based on the grounded theory, and the HFACS model is used to classify the concepts and categories. Finally, the relationship between the core category and the secondary category was sorted out in the way of storyline. The results show that the risk factors include environmental factors, organizational influences, unsafe supervision and controllers’ states, and the unsafe acts manifest as errors and violations. Among them, the controllers’ states are intermediate variable, and other factors indirectly affect the controllers’ unsafe acts. The first three risk factors with high frequency in unsafe incidents are technical environment, mental states and business ability. The three most common unsafe acts are giving the wrong order, insufficient situational awareness, and poor work order on-site. Through combining HFACS framework and grounded theory to analyze data, a more clear and comprehensive conceptual model of risk factors of ATCers’ unsafe acts can be obtained.

Human-Centered Intervention Based on Tactical-Level Input in Unscheduled Takeover Scenarios for Highly-Automated Vehicles

Due to functional limitations in certain situations, the driver receives a request to intervene from automated vehicles operating level 3. Unscheduled intervention of control authority would lead to insufficient situational awareness, then this will make dangerous situations. The purpose of this study is thus to propose tactical-level input (TLI) method with a multimodal driver-vehicle interface (DVI) for the human-centered intervention. The proposed DVI system includes touchscreen, hand-gesture, and haptic interfaces that enable interaction between driver and vehicle, and TLI along with such DVI system can enhance situational awareness. We performed unscheduled takeover experiments using a driving simulator to evaluate the proposed intervention system. The experimental results indicate that TLI can reduce reaction time and driver workload, and moreover, most drivers preferred the use of TLI than manual takeover.

HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection

Cyberattacks, which consist of exploiting security vulnerabilities of computer networks and systems for any kind of malicious purpose (e.g., extortion, data steal, assets hijacking), have been continuously increasing worldwide in recent years. Cyberspace appears today as a new battlefield, along with physical world scenarios (land, sea, air, and space), for the organizations defence and security. Besides, by the fact that attacks from the physical world may have significant implications in the cyber world and vice versa, these dimensions cannot be understood independently. However, the most common intelligence systems offer an insufficient situational awareness exclusively focused on one of these decision spaces. This article introduces HYBINT, an enhanced intelligence system that provides the necessary decision-making support for an efficient critical infrastructures protection by combining the real-time situation of the physical and cyber domains in a single visualization space. HYBINT is a real cross-platform solution which supplies, through Big Data analytical methods and advanced representation techniques, hybrid intelligence information from significant data of both physical and cyber data sources in order to bring an adequate hybrid situational awareness (HSA) of the cyber-physical environment. The proposal will be validated in a detailed scenario in which HYBINT system will be evaluated.

MONSTERS, ALIENS, AND THE END OF THE WORLD

Consistent flaws are revealed in post-incident reviews of security failures and ongoing reviews of security processes. These flaws include the math and methods used, insufficient situational awareness, overlooked systems mechanics, and missed root causes. Yet, many security teams are composed of bright, diligent, and dedicated professionals. How could they make such big mistakes? Simple—by being vulnerable to their own brains. It’s about brain science. Cognitive bias and structural blindness is the human condition. Brains must be trained to overcome our own vulnerabilities—like a martial artist building speed. This training is entirely different from checklists, intrusion prevention systems or mandatory security awareness videos. Yes, errors in typical security methods have wasted untold hours of human labor and mountains of money. Good news is that we security pros are not alone. Professionals in other areas have been exploring ways to help our brains see bigger and better for decades. Our opportunity is to bring this into security. Decades of research and proven practice are adapted to tech-related risk in a “dual cycle” approach. The first cycle—the “5 + 2 Step Cycle for Managing Risk to Performance”—is about substantive improvements in the management of risk. The second cycle is about “making change stick” by integrating the substantive change with an organizational change/transformation method.

TeMoto: Intuitive Multi-Range Telerobotic System with Natural Gestural and Verbal Instruction Interface

Teleoperated mobile robots, equipped with object manipulation capabilities, provide safe means for executing dangerous tasks in hazardous environments without putting humans at risk. However, mainly due to a communication delay, complex operator interfaces and insufficient Situational Awareness (SA), the task productivity of telerobots remains inferior to human workers. This paper addresses the shortcomings of telerobots by proposing a combined approach of (i) a scalable and intuitive operator interface with gestural and verbal input, (ii) improved Situational Awareness (SA) through sensor fusion according to documented best practices, (iii) integrated virtual fixtures for task simplification and minimizing the operator’s cognitive burden and (iv) integrated semiautonomous behaviors that further reduce cognitive burden and negate the impact of communication delays, execution latency and/or failures. The proposed teleoperation system, TeMoto, is implemented using ROS (Robot Operating System) to ensure hardware agnosticism, extensibility and community access. The operator’s command interface consists of a Leap Motion Controller for hand tracking, Griffin PowerMate USB as turn knob for scaling and a microphone for speech input. TeMoto is evaluated on multiple robots including two mobile manipulator platforms. In addition to standard, task-specific evaluation techniques (completion time, user studies, number of steps, etc.)—which are platform and task dependent and thus difficult to scale—this paper presents additional metrics for evaluating the user interface including task-independent criteria for measuring generalized (i) task completion efficiency and (ii) operator context switching.

Assessing the Impact of Insufficient Situation Awareness on Power System Operation

Situation awareness is a key factor in preserving power system security, as it enables effective and timely decision-making and reactions by the operators to an incident. Insufficient situation awareness results in a delayed, incorrect or deficient response, endangering power system stability. This factor was actually identified as one of the main causes of several electrical disturbances in the last decade. This paper identifies numerous factors that govern the formation of situation awareness in a power system control center. A multi-state model based on Markov modeling is proposed for assessing the impact of insufficient situation awareness on the probability of power system blackouts. The proposed model considers the level of situation awareness and the state of the information infrastructure. The workings of this model are illustrated using the IEEE 24-bus Reliability Test System.

Development of network restructuring models for improved air traffic forecasts

Current air traffic forecast methods employed by the United States Federal Aviation Administration function under the assumption that the structure of the network of routes operated by airlines will not change; that is, no new routes will be added nor existing ones removed. However, in reality the competitive nature of the airline industry is such that new routes are routinely added between cities possessing significant passenger demand; city-pairs are also removed. Such phenomena generates a gap between the forecasted and actual state of the US Air Transportation System in the long term, providing insufficient situational awareness to major stakeholders and decision-makers in their consideration of major policy and technology changes. To address this gap, we have developed and compared three algorithms that forecast the likelihood of un-connected city-pairs being connected by service in the future, primarily based on the nodal characteristics of airports in the US network. Validation is performed by feeding historical data to each algorithm and then comparing the accuracy and precision of new city-pairs forecasted using knowledge of actual new city-pairs that developed. While an Artificial Neural Network produces superior precision, fitness function and logistic regression algorithms provide good representation of the distribution of new route types as well as greater flexibility for modeling future scenarios. However, these latter two algorithms face difficulty in resolving differences among the large number of ‘spoke’ airports in the network – additional parameters that may be able to differentiate them are currently under review. These insights gained are valuable stepping stones for exploiting knowledge of restructuring in the service route network to improve overall forecasts that drive policy and technology decision-making.