Situational Awareness Research Articles

Eye, Heart, the Brain: The Psychophysiology of Trust in AVs

Automation misuse and acceptance, influenced by trust, environmental conditions, and confidence, have hindered drivers from fully benefiting from partially automated vehicles. This study investigates how driver trust changes with AV reliance, differences in mental and physiological states, and continuous measures’ effectiveness. The takeover drivers reported lower trust than the non-takeover drivers in all scenarios. Nontakeover drivers’ elevated DLPFC activation aligns with trust networks and emotion regulation. The groups also differed in neural activation preand during scenarios with the takeover group showed more PFC, V2V3, and IFC engagement pre-scenario. Gaze revealed the takeover group fixated more on the AV button or dashboard, indicating readiness to take over, while non-takeover drivers focused on the rearview mirror, reflecting situational awareness. HRV responses showed higher physiological arousal in the takeover group pre-scenario. In summary, our multimodal approach reveals takeover behavior is associated with lower trust, cognitive unloading, increased stress, and anticipatory visual attention.

The Impact of Pedestrian Interactions at Intersections on the Three Levels of Drivers’ Situation Awareness

Evaluating drivers’ situation awareness (SA) is important in the implementation of alert prioritization. This study investigates the relationship between driving performance measures (speed, acceleration and brake usage, steering wheel and lane deviation), pedestrian interaction (location, direction and motion), and driver SA. To achieve this, a controlled study was conducted with 56 participants using a Balanced Incomplete Block Design, where each participant drove 18 out of 48 possible intersections in a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) method was used to assess drivers’ SA. Mixed effects logit models were developed to examine the different SA Levels (perception, comprehension, projection). The driving performance measures were aggregated across three time windows (1, 3, and 5 s). The findings show significant contributions from both driving performance measures and pedestrian interactions in predicting driver SA. More specifically, a one-second time window was useful for predicting pedestrian direction and a three-second time window was best for predicting pedestrian location and intention to cross. The results indicate the importance of considering different time windows for predicting various levels of driver SA responses. These findings offer insights into factors to be considered in driver SA predictive models.

Detection and tracking of ocean layers using an AUV with UKF based extremum seeking control in the Baltic Sea

Adaptive sampling and situational awareness are key features of modern autonomous underwater vehicles (AUVs) since data quality can be improved while operation time and cost can be reduced. An example for adaptive sampling in the marine environmental context is thermocline detection and tracking. The thermocline as horizontal ocean layer separates warm and cold water and is a key feature in many marine disciplines. For example, it influences the distribution and exchange of nutrients and is a habitat for many organisms. In this paper we use an unscented Kalman Filter (UKF) based extremum seeking control (ESC) to find and follow ocean layers such as the thermocline. Computer simulations and real-world tests show that the method is able to find and track non-trivial real-world ocean layers with sensors subject to hysteresis and delay effects.

An Efficient Visual Tracking System Based on Extreme Learning Machine in the Defence and Military Sector

Visual tracking is the capacity to estimate or forecast a target object’s location in each frame of a video after specifying its starting position. Visual tracking is of essential relevance in defence and military operations. The military can use it to improve situational awareness, improve precise targeting, acquire intelligence in real-time, and efficiently respond to a variety of threats and circumstances. In the past, object tracking systems have relied mostly on algorithms based on deep learning techniques and these tracking algorithms are lacking in both accuracy and speed. In this research, an Extreme Learning Machine-based visual tracking system has been proposed that incorporates properties like high accuracy, low training time, and less network computing complexity as compared to existing deep learning-based tracking algorithms. The Haar wavelet transform is utilized in the recommended technique for feature learning, while the extreme learning machine is utilised for classification and recognition. A benchmark dataset object tracking benchmark-2013 has been used to carry out the experiments. The experiment values indicated that the proposed technique has accomplished enhanced performance over another tracking model. Additionally, we tested the proposed method’s accuracy and robustness regarding certain visual characteristics: Illumination variation, occlusion, deformation, out-of-plane rotation, background clutters, and in-plane rotation. The findings of the simulation revealed that the objects in videos have been 84% accurately tracked by the suggested method.

Complexity Analysis Using Graph Models for Conflict Resolution for Autonomous Ships in Complex Situations

Abstract Maritime autonomous surface ships (MASSs) will reshape the fast-evolving ecosystem for their attractive socio-economic benefits and potential to improve safety. However, their new systems and technology need thorough verifications to identify unintended components of risk. The interaction between MASS cyber-physical systems and the existing regulatory framework is currently unpredictable; artificial intelligence-powered intelligent situation awareness and autonomous navigation algorithms must safely and efficiently adhere to the regulations which are only designed for human interpretation without MASSs consideration. This paper contributes to algorithmic regulations and particularly algorithmic COLREGs in real-world MASS applications. It focuses on codifying COLREGs into a machine-executable system applicable to MASSs, then analyzing their performance in dynamic and mixed interactions between multiple vessels in complex scenarios. Based on fullest pairwise COLREGs criteria, this paper considers decision-making (DM) and complexity analysis in multi-collision-conflict scenarios. Complexity influential factors are an interplay between the characteristics of COLREGs, traffic scenarios, MASS interactions, and the environment. Participant vessels are the decision-makers forming a decentralized uncertain DM process, casted into a multi-participant multi-conflict multicriteria DM problem. This is tackled through the technique of graph models for conflict resolution, using risk graph models and fuzzy preferences over alternative collision-avoidance states. This paper conducts a comprehensive analysis of DM and navigational complexity; we develop novel complexity estimation scores, tools for complexity monitoring for human intervention, and spatial analysis of traffic complexity for geo-intelligent MASSs deployment and operation planning. The presented work is validated on a database of historical scenarios extracted from multiple data sources.

Ajna: A Wearable Shared Perception System for Extreme Sensemaking

This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users’ relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.

Postpandemic Era: Investigating the Impact of COVID-19 on Construction Workers’ Situational Awareness

Postpandemic Era: Investigating the Impact of COVID-19 on Construction Workers’ Situational Awareness

Sustainability of a PICU Situation Awareness Intervention: A Qualitative Study.

We aimed to investigate facilitators and barriers that impact the sustainability of an interprofessional situation awareness bundle. This is a single-center qualitative study at a tertiary care pediatric center examining the sustainability of an interprofessional situation awareness bundle to reduce in-hospital cardiac arrests. The bundle includes an automated clinical decision support tool, twice-daily safety huddles, and a bedside mitigation plan. A trained research staff member interviewed participants in October 2022. Interviews were audio recorded and transcribed verbatim, and recruitment continued until data saturation. Inductive and deductive analyses were used here. The authors interviewed twelve staff members via individual semistructured interviews: registered nurses (RN, n = 2) and clinicians [(advanced practice providers, n = 2), pediatric critical care fellows, n = 4 and attendings, n = 4)]. Five main themes were identified: (1) the situation awareness bundle is ingrained into daily practice and culture, (2) the bundle has strengthened communication, decision-making, and improved outcomes, (3) standardized processes, stakeholder buy-in, and support of team members are key to adoption and sustainability, (4) variation in processes and fast-changing clinical context remains a challenge for reliable use, and (5) the situation awareness bundle excluded families. The situation awareness bundle has become ingrained, strengthened, and sustained over the last 5 years through integration into daily practice and culture and leveraging standardized processes, tools and technology. It is associated with improved communication and shared decision-making. Understanding the key components for implementation and sustainability is necessary for ongoing spread and improvement in the future.

Evaluating performance: a scoping review on video-based assessments of non-technical skills in the operating room

Background: Patient safety remains a critical concern in the high-stakes environment of the operating room (OR). Human factors and non-technical skills (NTS) play pivotal roles in surgical performance and in preventing errors. Various assessment tools and methodologies have been developed to evaluate NTS among OR personnel, reflecting the growing recognition of their importance. Objectives: To map and analyze NTS among OR personnel during real-life surgeries using medical video recording systems and to evaluate NTS assessment tools described in existing literature. Methods: Four databases, PubMed, Web of Science, Medline and Embase, were searched for relevant studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed. Eligibility criteria included studies focusing on video recordings providing an overview of the entire OR with a focus on NTS. Results: Twenty-four articles were included in this scoping review. A total of eight NTS were assessed, most frequently communication (79%), leadership (54%), teamwork (50%) and situational awareness (50%). Observational methods varied, ranging from one or more cameras to more sophisticated medical data recorders, such as the OR Black Box (ORBB). Additionally, a wide variety of assessment methods were used, mostly validated tools (NOTSS, SPLINTS, ANTS), but also broader systems-based frameworks (SEIPS) or self-developed tools. Conclusions: This scoping review highlights the diverse methodologies used to measure NTS in the OR using medical video recordings, indicating a need for further research to address challenges and standardize practices. Despite the proven potential benefits of video recording in the OR, legal, ethical and logistical challenges may serve as possible barriers to actual implementation.

Multi-sensor possibility PHD filter for space situational awareness

Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness (SSA) applications. In this process, the uncertainties of targets, dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system. The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.

Single track orbit determination analysis for low Earth orbit with approximated J2 dynamics

In the domain of Space Situational Awareness (SSA), the challenges related to orbit determination and catalog correlation are notably pronounced, exacerbated by data scarcity. This study introduces an initial orbit determination methodology that relies on data obtained from a single surveillance radar, with the need for fast algorithms within an operational context serving as the main design driver. The result is a linearized least-squares fitting procedure incorporating an analytically formulated approximation of the dynamics under the J2 perturbation, valid for short-term propagation. This algorithm utilizes all available observables, including range-rate, distinguishing it from other similar methods. A significant contribution of this paper is the enhancement of estimation quality by incorporating information about the object’s predicted orbital plane into the methodology, a method denoted as OPOD. The proposed methods are evaluated through a series of simulations against a classical range and angles fitting method (GTDS) to examine the effects of track length and measurement density on the quality of full state estimation, including the impact of using arcs that are too short. The OPOD methodology shows promising results throughout a wide range of scenarios.

Light curve attitude estimation using particle swarm optimizers

Knowledge of the attitude of a space object is useful in space situational awareness for independently evaluating a satellite’s health and characterizing unknown objects. In cases where only non-resolved optical observations are available, the object’s attitude may be estimated using a time sequence of brightness observations, also known as the light curve. This attitude estimation problem is plagued with multiple difficulties: even in the absence of noise and when all other relevant factors are perfectly known, the non-uniqueness of the problem means that multiple attitude time histories may fit the light curve equally well. In addition, there is often insufficient information about the object to generate an initial state guess for an estimator. This paper presents a method that estimates an observed object’s attitude and angular velocity while accounting for ambiguities and without needing any initial state guess. The only inputs are the light curve, the object’s albedo shape, and the object’s position relative to the Sun and the observer. The ability of the estimator to resolve attitude time histories is demonstrated using simulated light curves by comparing state estimates against known true states.

Multi-radio fast routing recovery protocol based on spectrum situation awareness

UAV networks often encounter jamming attacks, under which multi-radio protocols have to switch radios to accelerate communication recovery. However, the existing protocols rely on exchange of hello messages to detect jamming, leading to long sensing time and thus slow routing recovery. To address the issues raised by jamming attacks, we propose a new routing protocol, Electromagnetic Spectrum situation awareness Optimized Link State Routing (ESOLSR) protocol, to improve the existing OLSRv2 protocol. ESOLSR utilizes the spectrum situation awareness capability from the physical layer, and adopts joint-updating of link status, updating of interface functions, and adaptive adjustment of parameters. Our simulation results show that the improved protocol, ESOLSR, can recover routing and resume normal communication 26.6% faster compared to the existing protocols.

Enhancing pilot vigilance assessment: The role of flight data and continuous performance test in detecting random attention loss in short IFR flights

Situational awareness (SA) and fatigue management are crucial aspects of aviation safety, particularly during demanding flight phases. This study introduces an innovative approach employing flight data, machine learning, and Continuous Performance Test (CPT) metrics to predict pilot performance and SA during instrument approaches under Instrument Meteorological Conditions (IMC). Data were collected from over 10 pilots across more than 40 flights in a high-fidelity Cessna 172 analog flight simulator.Significant correlations were observed between dynamic cognitive performance parameters and the exceedance shape factor, a novel measure of pilot sustained attention introduced in this research. Key variables identified through correlation analysis included variability, interstimulus change, and reaction time standard deviation.Importantly, commission scores and reaction time standard deviation emerged as key predictors in the machine learning model, specifically the Optimizable Gaussian Process Regression (GPR) model with a radial basis function kernel. The model achieved a validation R-squared of 0.90 and a test R-squared of 0.70. These systems could incorporate additional data sources, such as eye-tracking and scan pattern analysis, for a better assessment of pilot SA and fatigue levels. While post-flight measurements are inherently reactive, they are effective for monitoring the degradation of pilot CPT scores after each leg of high-frequency, short-duration flights.Notable limitations include the need to understand individual cognitive differences among pilots, such as age, experience, and cognitive style. The predictive model also requires validation in actual flight conditions to determine its ecological validity. Future research should aim to address these limitations, generalize the findings, and integrate CPT data with other sensor inputs to provide a more comprehensive understanding of pilot performance.

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

Safety Incidents and Situation Awareness Amongst Maintenance Personnel in the Food Manufacturing Industry

Safety Incidents and Situation Awareness Amongst Maintenance Personnel in the Food Manufacturing Industry

Seeing through fire with one pixel

Seeing through fire is a critical capability in various applications, such as fire rescue, industrial combustion monitoring, and scientific research. However, the intense electromagnetic radiation emitted by flames can saturate and blind conventional imaging systems, making it challenging to visualize objects or scenes obscured by fire. In this paper, we present a novel method for real-time, full-color through-flame imaging using structured illumination and single-pixel detection. By projecting a series of carefully designed light patterns onto the scene and measuring the backscattered light with a single-pixel detector, we can computationally reconstruct the obscured scene while effectively suppressing the flame's contribution to the image. The single-pixel detector's high dynamic range and sensitivity enable it to capture the weak backscattered signal without being overwhelmed by the flame's intense radiation. We demonstrate the method's effectiveness in several experiments, showcasing its ability to image static and dynamic scenes through both steady and turbulent flames at a frame rate of 15 Hz. Furthermore, we show that the proposed method can be extended to full-color imaging by using three single-pixel detectors with different color filters. The results highlight the potential of this approach for enhancing visibility in fire-related scenarios and other challenging imaging conditions. We believe that the integration of this technology into augmented reality systems could provide firefighters and other users with valuable real-time visual information, improving situational awareness and decision-making in critical situations. We also believe the method can be used for seeing through any self-luminous objects.

An accurate and efficient second-order J2 model for the draper semianalytic satellite theory

Semi-analytical orbit propagation methods combine the advantages of analytical and numerical methods to achieve an excellent balance between efficiency and accuracy. The increasingly congested space has led to the growing prevalence of semi-analytical methods, driven by the requirements for efficient and accurate orbit propagation in Space Situational Awareness (SSA). However, the accuracy of semi-analytical methods is only retained by considering the contribution of the J2 zonal harmonic second order terms, namely the J22 term, which has not been completely modeled in semi-analytical methods due to the complex mathematical formulations. This paper proposes a complete semi-analytical solution for the J22 contribution consistent with the Draper Semianalytic Satellite Theory (DSST), including the fully closed-form and nonsingular mean element rate equations and computationally efficient short-period terms. Firstly, for the direct application of DSST in the construction of the J22 solution, the difficulties of high computational costs induced by multiplying infinite Fourier series together are analyzed. Next, an improved construction process for the J22 solution is developed by introducing a new general form of the partial derivatives of osculating elements rates to eliminate these difficulties. Considering the complexity of the construction process, the computation details for each term in this process are then presented. Numerical experiments demonstrate that the proposed J22 solution achieves remarkable accuracy and efficiency compared to previous works.

An improved maritime traffic situation complexity model for intelligent maritime management in the inland ferry area

In increasingly busy inland ferry waterways, traditional maritime traffic situation awareness mainly relies on the experience of Officers on Watch (OOW) and Vessel Traffic Service Operators (VTSOs). Although advancements in science and technology can provide more navigational data to OOW and VTSOs, they often find it difficult to intuitively perceive navigation risks from the massive amounts of heterogeneous data. Current maritime traffic situation models mostly used in open waters make it hard to objectively grasp and understand the overall complexity of ferry water area traffic and to capture the most critical ship targets in local areas. Therefore, this study proposes an improved complexity model for maritime traffic situations in inland waters. It selects dynamic traffic density factor, ship approaching factor, and conflict area to assess the local complexity between ships. Then, the global complexity of traffic in the ferry water areas can be evaluated using the entropy weighting method. These promising results suggest that assessing maritime traffic complexity on different levels can help OOW and VTSOs manage traffic in ferry water areas and identify target vessels that require traffic services and control. The application of this method supports the construction of smart maritime management, enhancing the efficiency and automation of ferry water areas.

Modeling perceived information needs in human-AI teams: improving AI teammate utility and driving team cognition

ABSTRACT As AI technologies advance, teams are beginning to see AI transition from a tool to a full-fledged teammate. Introducing an AI teammate brings several challenges, ranging from how human teammates perceive their new AI teammates from an affective standpoint to how AI should engage in the various teaming behaviors that make up effective teamwork. The current study used a mixed factorial survey and structural equation modeling to assess how participants in hypothetical human-AI teams respond to various forms of AI information-sharing, including information related to explainability, back-up behavior, situational awareness, and augmenting team memory. The study's results found that AI design features related to situational awareness and augmenting the teams' memory had the strongest effect on participants' attitudes and perceived team cognition with their teammates. However, much of this effect was mediated by participants' affective attitudes towards the AI as a teammate, with higher ratings leading directly to higher levels of perceived team cognition constructs. These results highlight the importance of fostering positive attitudes towards AI teammates, such as trust and cohesion in human-AI teams, to support the development of effective team cognition and the ability of AI information-sharing to bring about such positive impacts.