Search Results (63)

External Human–Machine Interfaces (eHMIs) enhance pedestrian safety in interactions with autonomous vehicles (AVs) by signaling crossing risk based on time-to-arrival (TTA), categorized as low, medium, or high. This study compared five eHMI configurations (single-level low, medium, high; two-level low-medium, medium-high) against a three-level (low-medium-high) configuration to assess their impact on pedestrians’ crossing decisions, mental workload (MW), and situation awareness (SA) in vehicle platoon scenarios under full and partial eHMI penetration. In a video-based experiment with 24 participants, crossing decisions were evaluated via temporal gap selection, MW via P300 event-related potentials in an auditory oddball task, and SA via the Situation Awareness Rating Technique. The three-level configuration outperformed single-level medium, single-level high, two-level low-medium, and two-level medium-high in gap acceptance, promoting safer decisions by rejecting smaller gaps and accepting larger ones, and exhibited lower MW than the two-level medium-high configuration under partial penetration. No SA differences were observed. Although the three-level configuration was generally appreciated, future research should optimize presentation to mitigate issues from rapid signal changes. Notably, the single-level low configuration showed comparable performance, suggesting a simpler alternative for real-world eHMI deployment. Full article

Research on augmented reality (AR) and mixed reality (MR) demonstrated their potential to support decision-making in construction. However, most efforts emphasized technological advancements, often overlooking how to present and interact with information to effectively support decision-making in AR/MR environments. This study proposes an interface design method that integrates situation awareness (SA) and immersive analytics (IA), two complementary frameworks that address user information needs and immersive interaction design. The method guides the design of AR/MR interfaces by aligning information content, presentation, and interaction with SA requirements and IA design principles. To evaluate its effectiveness, the method was applied to develop AR and MR interface prototypes for a simulated decision-making task involving field managers during indoor construction activities of high-rise construction projects. Results show high levels of SA achieved by participants, with no statistically significant differences between AR and MR interfaces, demonstrating the method’s effectiveness to support SA in both environments. The proposed method provides a structured approach for designing immersive interfaces that enable better perception, comprehension, and projection in dynamic construction scenarios. Moreover, it provides designers with practical guidance for interface development and allows practitioners to assess existing AR/MR solutions based on their capacity to enhance SA through IA. Full article

Situation awareness (SA) involves analyzing sensory data, such as audio signals, to identify anomalies. While acoustic features are widely used in audio analysis, existing methods face critical limitations; they often overlook the relevance of SA audio segments, failing to capture the complex relational patterns in audio data that are essential for SA. In this study, we first propose a graph neural network (GNN) with an attention mechanism that models SA audio features through graph structures, capturing both node attributes and their relationships for richer representations than traditional methods. Our analysis identifies suitable audio feature combinations and graph constructions for SA tasks. Building on this, we introduce a situation awareness gated-attention GNN (SAGA-GNN), which dynamically filters irrelevant nodes through max-relevance neighbor sampling to reduce redundant connections, and a learnable edge gated-attention mechanism that suppresses noise while amplifying critical events. The proposed method employs sigmoid-activated attention weights conditioned on both node features and temporal relationships, enabling adaptive node emphasizing for different acoustic environments. Experiments reveal that the proposed graph-based audio features demonstrate superior representation capacity compared to traditional methods. Additionally, both proposed graph-based methods outperform existing approaches. Specifically, owing to the combination of graph-based audio features and dynamic selection of audio nodes based on gated-attention, SAGA-GNN achieved superior results on two real datasets. This work underscores the importance and potential value of graph-based audio features and attention mechanism-based GNNs, particularly in situational awareness applications. Full article

The track-to-track association (T2TA) problem is a fundamental and critical challenge in information fusion, situational awareness, and target tracking. Existing algorithms based on statistical mathematics, fuzzy mathematics, gray theory, and artificial intelligence suffer from several limitations that are hard to solve, such as over-idealized models, unrealistic assumptions, insufficient real-time performance, and high computational complexity due to pairwise matching requirements. Considering these limitations, we propose a self-adaptive step-2-based K-nearest neighbor density peak clustering (SAS-KNN-DPC) algorithm to address T2TA problem. Firstly, the step-2 temporal neighborhood affinity matrix under a non-registration framework is defined and the calculation methods for multi-feature track-point fusion similarity matrix are given. Secondly, the proposed self-adaptive multi-feature similarity truncation matrix is defined to measure the multidimensional distance between track points and the self-adaptive step-2 truncation distance is also defined to enhance the adaptivity of the algorithm. Finally, we propose improved definitions of local distance and global relative distance to complete both cluster center selection and association assignment. The proposed algorithm eliminates the need for exhaustive pairwise matching between track sequences and avoids time alignment, significantly improving the real-time performance of T2TA. Simulation results demonstrate that compared to other algorithms, the proposed algorithm achieves higher accuracy, reduced computational time, and better real-time performance in complex scenarios. Full article

Digitalization has revolutionized the maritime industry, particularly in navigation systems. The use of advanced tools such as the Electronic Chart Display and Information System (ECDIS) has increased the need for information processing. However, the complexity of these systems can be overwhelming for navigators. To address the concern of usability of these complex navigation systems, training with simulator data allows the crew to familiarize themselves with these systems, handle complex navigation scenarios effectively, support the transition from paper-based systems to digital systems, and help in improving their situational awareness (SA) at sea. We propose a tool that provides optimal conditions for assessing situational awareness and informing the development of intuitive systems and user interfaces. In the maritime safety domain, there is an inverse correlation between situational awareness and scenario/system complexity, highlighting the importance of effective training and assessments to improve SA. The proposed tool utilizes the Situational Awareness Global Assessment Technique (SAGAT) method, widely used in other domains, to calculate an individual’s SA score. It evaluates participants’ situational awareness in different navigational scenarios on Ship Handling Simulators, using dynamic questionnaires and contextual maps. Additionally, it integrates a rule-based system to assess participants’ performance and calculate a situational awareness score in real time, offering possibilities for assessing the SA of navigators. Full article

Human–robot collaboration for lunar surface exploration requires high safety standards and tedious operational procedures. This process generates extensive task-related data, including various types of faults and influencing factors. However, these data are characteristic of multi-dimensional, time series, and intertwined. Also, prolonged tasks and multi-factor data coupling pose significant challenges for astronauts in achieving safe and efficient fault localization and resolution. In this paper, we propose a method to enhance the base large language models (LLMs) by embedding knowledge graphs (KGs) of lunar surface exploration, thereby assisting astronauts in reasoning about faults during the exploration process. A multi-round dialog dataset is constructed through the knowledge subgraph embedded in the request analysis process. The LLM is fine-tuned using the p-tuning method to develop a specialized LLM suitable for lunar surface exploration. With reference to the situational awareness (SA) theory, multi-level prompts are designed to facilitate multi-round dialogues and aid decision-making. A case study shows that our proposed model exhibits greater expertise and reliability in responding to lunar surface exploration tasks than classical commercial models, such as ChatGPT and GPT-4. The results indicate that our method provides a reliable and efficient aid for astronauts in fault analysis during lunar surface exploration. Full article

Remote tower technology is an important development direction for air traffic control to reduce the construction and operation costs of small or remote airports. However, its digital and virtualized working environment poses new challenges to controllers’ situational awareness (SA). In this study, a dataset is constructed by collecting eye-tracking (ET) and heart rate variability (HRV) data from participants in a remote tower simulation control experiment. At the same time, probe questions are designed that correspond to the SA hierarchy in conjunction with the remote tower control task flow, and the dataset is annotated using the scenario presentation assessment method (SPAM). The annotated dataset containing 25 ET and HRV features is trained using the LightGBM model optimized by a Tree-structured Parzen Estimator, and feature selection and model interpretation are performed using the SHapley Additive exPlanations (SHAP) analysis. The results show that the TPE-LightGBM model exhibits excellent prediction capability, obtaining an RMSE, MAE and adjusted R² of 0.0909, 0.0730 and 0.7845, respectively. This study presents an effective method for assessing and predicting controllers’ SA in remote tower environments. It further provides a theoretical basis for understanding the effect of the physiological state of remote tower controllers on their SA. Full article

In recent years, the rapid development of remote tower technology has made it crucial to accurately assess the situational awareness (SA) levels of remote tower controllers. Such an assessment is significant for controller training and remote tower system design. This study employed the SART scale to compare controllers’ SA scores in traditional and remote tower environments. Results revealed significant differences, especially in attention demand and situational understanding. Subsequently, a quantitative analysis of controllers’ perception, understanding, and decision-making abilities was conducted, integrating subjective and objective data. Eye-tracking, heart rate, working memory scales, and communication-coordination scales showed significant results. Experienced controllers had better psychological safety skills, while trainees were more likely to increase vigilance. Moreover, a series of sensitive SA indicators were identified. An evaluation index system was established using the entropy weight method. By calculating the Euclidean distance, Gray relational degree, and comprehensive proximity coefficient, the SA levels of controllers were comprehensively evaluated. The top five important indicators were average blink rate, scan length, average fixation duration, fixation duration, and average pupil diameter. These findings support enhancing air traffic control safety and refining SA assessment for remote tower controllers. Full article

Autonomy is being increasingly used in domains like maritime, aviation, medical, and civil domains. Nevertheless, at the current autonomy level, human takeover in the human–autonomy interaction process (HAIP) is still critical for safety. Whether humans take over relies on situation awareness (SA) about the correctness of autonomy decisions, which is distorted by human anchoring and omission bias. Specifically, (i) anchoring bias (tendency to confirm prior opinion) causes the imperception of key information and miscomprehending correctness of autonomy decisions; (ii) omission bias (inaction tendency) causes the overestimation of predicted loss caused by takeover. This paper proposes a novel HAIP safety assessment method considering effects of the above biases. First, an SA-based takeover decision model (SAB-TDM) is proposed. In SAB-TDM, SA perception and comprehension affected by anchoring bias are quantified with the Adaptive Control of Thought-Rational (ACT-R) theory and Anchoring Adjustment Model (AAM); behavioral utility prediction affected by omission bias is quantified with Prospect Theory. Second, guided by SAB-TDM, a dynamic Bayesian network is used to assess HAIP safety. A case study on autonomous ship collision avoidance verifies effectiveness of the method. Results show that the above biases mutually contribute to seriously threaten HAIP safety. Full article

Enhancing transparency through interface design is an effective method for improving driving safety while reducing driver workloads, potentially fostering human–machine collaboration. However, to ensure system usability and safety, operator psychological factors and operational performance must be well balanced. This study investigates how the introduction of transparency design into urban rail transit driving tasks influences drivers’ situational awareness (SA), trust in automation (TiA), sense of agency (SoA), workload, operational performance, and visual behavior. Three transparency driver–machine interface (DMI) information conditions were evaluated: DMI1, which provided continuous feedback on vehicle operating status and actions; DMI1+2, which added inferential explanations; and DMI1+2+3, which further incorporated proactive predictions. Results from simulated driving experiments with 32 participants indicated that an appropriate level of transparency significantly enhanced TiA and SoA, thereby yielding the greatest acceptance. High transparency significantly aided in predictable takeover tasks but affected gains in TiA and SoA, increased workload, and disrupted perception-level SA. Compared with previous research findings, this study indicates the presence of a disparity in transparency needs for low-workload tasks. Therefore, caution should be exercised when introducing high-transparency designs in urban rail transit driving tasks. Nonetheless, an appropriate transparency interface design can enhance the driving experience. Full article

The use of drones or Unmanned Aerial Vehicles (UAVs) and other flying vehicles has increased exponentially in the last decade. These devices pose a serious threat to helicopter pilots who constantly seek to maintain situational awareness while flying to avoid objects that might lead to a collision. In this paper, an Airborne Visual Artificial Intelligence System is proposed that seeks to improve helicopter pilots’ situational awareness (SA) under UAV-congested environments. Specifically, the system is capable of detecting UAVs, estimating their distance, predicting the probability of collision, and sending an alert to the pilot accordingly. To this end, we aim to combine the strengths of both spatial and temporal deep learning models and classic computer stereo vision to (1) estimate the depth of UAVs, (2) predict potential collisions with other UAVs in the sky, and (3) provide alerts for the pilot with regards to the drone that is likely to collide. The feasibility of integrating artificial intelligence into a comprehensive SA system is herein illustrated and can potentially contribute to the future of autonomous aircraft applications. Full article

Background: This study explores forklift operators’ situational awareness (SA) and human errors in logistic operations using a multiphase approach as an innovative methodology. Methods: Ethnography, eye tracking, error taxonomy, and retrospective think-aloud (RTA) were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. After analyzing 566 events across 18 tasks, this research highlighted eye tracking’s potential by offering real-time insights into operator behavior and RTA’s potential as a method for cross-checking the causal factors underlying errors. Results: Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high-cognitive demand tasks, such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable to errors and are the most affected by operator risk generators (ORGs), representing 42% of incident risk events. This study identifies driving, hoisting, and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADASs). Conclusions: This study provides valuable insights into how we may improve safety in logistics operations by proposing a multiphase methodology to uncover the patterns of attention, perception, and cognitive errors and their impact on decision-making. Full article

Background: In safety-critical environments, human error is a leading cause of accidents, with the loss of situation awareness (SA) being a key contributing factor. Accurate SA assessment is essential for minimizing such risks and ensuring operational safety. Traditional SA measurement methods have limitations in dynamic real-world settings, while physiological signals, particularly EEG, offer a non-invasive, real-time alternative for continuous SA monitoring. However, the reliability of SA measurement based on physiological signals depends on the accuracy of SA labeling. Objective: This study aims to design an effective SA measurement paradigm specific to high-speed train driving, investigate more accurate physiological signal-based SA labeling methods, and explore the relationships between SA levels and key physiological metrics based on the developed framework. Methods: This study recruited 19 male high-speed train driver trainees and developed an SA measurement paradigm specific to high-speed train driving. A method combining subjective SA ratings and task performance was introduced to generate accurate SA labels. Results: The results of statistical analysis confirmed the effectiveness of this paradigm in inducing SA level changes, revealing significant relationships between SA levels and key physiological metrics, including eye movement patterns, ECG features (e.g., heart rate variability), and EEG power spectral density across theta, alpha, and beta bands. Conclusions: This study supports the use of multimodal physiological signals for SA assessment and provides a theoretical foundation for future applications of SA monitoring in railway operations, contributing to enhanced operational safety. Full article

Situation awareness (SA) discrimination is significant, allowing for the pilot to maintain task performance and ensure flight safety, especially during high-stress flight tasks. Although previous research has attempted to identify and classify SA, existing SA discrimination models are predominantly binary and rely on traditional machine learning methods with limited physiological modalities. The current study aimed to construct a triple-class SA discrimination model for pilots facing high-stress tasks. To achieve this, a flight simulation experiment under typical high-stress tasks was carried out and deep learning algorithms (multilayer perceptron (MLP) and the attention mechanism) were utilized. Specifically, eye-tracking (ET), heart rate variability (HRV), and electroencephalograph (EEG) modalities were chosen as the model’s input features. Comparing the unimodal models, the results indicate that EEG modality surpasses ET and HRV modalities, and the attention mechanism structure has advantageous implications for processing the EEG modalities. The most superior model fused the three modalities at the decision level, with two MLP backbones and an attention mechanism backbone, achieving an accuracy of 83.41% and proving that the model performance would benefit from multimodal fusion. Thus, the current research established a triple-class SA discrimination model for pilots, laying the foundation for the real-time evaluation of SA under high-stress aerial operating conditions and providing a reference for intelligent cockpit design and dynamic human–machine function allocation. Full article

This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper critically analyzes multifaceted challenges such as real-time data processing demands, adaptability in dynamic environments, and complexities introduced by advanced AI and machine learning techniques. Key contributions include a detailed exploration of UAV-centric SA’s transformative potential in industries such as precision agriculture, disaster management, and urban infrastructure monitoring, supported by case studies. In addition, the paper delves into algorithmic approaches for path planning and control, as well as strategies for multi-agent cooperative SA, addressing their respective challenges and future directions. Moreover, this paper discusses forthcoming technological advancements, such as energy-efficient AI solutions, aimed at overcoming current limitations. This holistic review provides valuable insights into the UAV-centric SA, establishing a foundation for future research and practical applications in this domain. Full article