As sustainable motorized individual transport becomes essential for achieving CO2 reduction targets, this study investigates the acceptance of an eco-driving app that may provide a practical solution to improve sustainable driving. The app, building on currently available technologies, was examined using an integrated model of the Technology Acceptance Model (TAM) and Trust in Technology. The study assesses the technology's acceptance and influence on future eco-driving intentions. In addition to TAM's established variables - Perceived Ease of Use and Perceived Usefulness - Trust in Technology emerged as a significant predictor of app acceptance. The study further explored the roles of Environmental Awareness and Cognitive Flexibility, two factors previously unexamined in eco-driving research, as motivators of sustainable driving intentions. Findings indicate that app acceptance, alongside Environmental Awareness and Cognitive Flexibility, support the Intention to Perform Eco-Driving. Practical implications emphasize the importance of a design that fosters user trust, while theoretical implications suggest that future research should consider cognitive and mindset factors in addition to technology acceptance when investigating eco-driving intentions. These results underscore the relevance of our study for both practitioners and researchers in advancing sustainable motorized individual transport.
Maritime fires on container ships are an important issue in global shipping operations involving numerous actors. In these scenarios, Shared Situational Awareness (SSA) plays a critical role. This paper examines the challenges of these incidents, emphasizing the prevalence of human error and the limitations of traditional fire detection and coordination methods. It applies Endsley's three-level model of Situational Awareness (SA) and proposes a model for SSA development specific to such emergencies. The Overheat Project is introduced, highlighting the development of a Digital Solution (DS) to address enhanced SSA through real-time data integration and a collaborative platform. This platform connects vessels and on-shore data systems, providing a common operational picture (COP) for all stakeholders involved in fire response. The architecture of the DS is described, outlining the onboard and ground-based components that facilitate seamless data exchange. The paper concludes by highlighting the potential benefits of this solution in improving fire safety and response capabilities on container ships.
The increasing complexity of maritime navigation and the shift towards (semi)autonomous systems necessitate enhanced situational awareness (SA) to ensure maritime safety. This transition introduces new requirements for situation modelling and SA, particularly in busy waterways. To address these challenges, we present the Federated Evidential Learning for Anomaly Detection of Ship Trajectories (FEAST) framework, which integrates Federated Learning and Evidential Learning to provide a privacy-preserving, collaborative, and uncertainty-aware approach to out-of-distribution (OOD) anomaly detection in maritime traffic. FEAST uses data from the Automatic Identification System from the Kiel region, Germany, which exhibits unique characteristics of dynamic and heterogeneous maritime activity due to its connection with the traffic-dense Kiel Canal. Our extensive evaluations demonstrate that FEAST improves OOD anomaly detection by leveraging epistemic and aleatoric uncertainty estimates, outperforming baseline methods such as Denoise AutoEncoders and Variational AutoEncoders. Consequently, FEAST forms a solution to reliable and interpretable maritime traffic anomaly detection, supporting enhanced SA in maritime operations.
Decision-making in unknown circumstances requires balancing exploitation—leveraging past outcomes—and exploration—seeking new information—a process often constrained by cognitive load. Cognitive load may impede curiosity-driven inquiry and diminish working memory capacity, leading to less adaptive but safer selections. While previous research has shown that transcranial direct current stimulation (tDCS) applied to the dorsolateral prefrontal cortex (DLPFC) enhances cognitive flexibility and risk-taking behavior, there is limited understanding of its efficacy in mitigating the adverse impacts of higher cognitive load on the exploration-exploitation trade-off. By examining how tDCS influences decision-making under various cognitive demands, our work fills this research gap. Under low and high cognitive load settings, 20 participants, 10 receiving tDCS (tDCS group) and 10 not receiving tDCS (control group), choose between three alternatives in a binary-choice task: safe option (Option A), risky option (Option B), and exploratory option (Option R). Results revealed that those who received tDCS were more likely to engage in exploratory behavior, choosing Option $R$ in 40 % of trials as opposed to 22 % in the control group, and selected riskier alternatives (Option B) in 57.7 % of trials, which was considerably more frequent than the control group (31 %). Interestingly, even when subjected to a high cognitive load, tDCS individuals retained the choice for the option $R$, underscoring the function of neuromodulation in reducing the impact of higher cognitive load. With implications for risky situations, these findings advance our knowledge of how tDCS fosters curiosity-driven behavior through enhancement in exploratory behavior and adaptive decision-making.
Manual maintenance and repair tasks are frequently demanding and time-consuming, necessitating workers to pinpoint problems and remember intricate procedures for their resolution in contemporary industrial environments. To address these challenges we propose an innovative assistance system that leverages a semantically zoomable Digital Twin (DT) of our factory environment to provide context-sensitive assistance to factory workers thereby helping them make informed decisions in maintenance and repair tasks. Our system aids workers by providing situation-specific guidance in an intuitive user interface in Augmented Reality (AR) glasses based on dynamic inputs such as user feedback, spatial marker tracking and registration in AR, and object detection. Based on these dynamic cues the user can zoom into the multi-layered 3D DT in Unity scene and access the necessary visualization relevant to that situation. This assistance in troubleshooting and repair procedures could potentially reduce their cognitive load and minimize time and errors. A preliminary study (N = 6) is carried out to provide an initial understanding of the impact of situation-awareness and the semantic zoom-based visualizations as assistance feature and demonstrate its usability of the assistance system.
The rapid evolution of digital technology necessitates quick and effective educational strategies. This paper presents the CONSALE (CONstructing Situation Awareness in microLearning Environments) project, which introduces an innovative approach to designing microlearning-based courses by integrating user-centered Situation Awareness design principles with Understanding by Design methodology. By aligning microlearning modules with explicit learning objectives and practical competencies needed in real-world settings, the CONSALE project addresses modern challenges of workplace and lifelong learning. Building on principles from Situation Awareness-Oriented Design and Goal-Directed Task Analysis, CONSALE ensures the development of coherent and focused educational units in order to support personalized and adaptive learning experiences for reskilling and upskilling across various industries.
As global emergencies grow more complex, emerging technologies such as drones, augmented reality (AR), and autonomous vehicles (AVs) are redefining the landscape of crisis management. This paper explores the cutting-edge integration of these technologies, showcasing their potential to revolutionize firefighting, disaster response, and urban safety. Using vivid realworld examples—from drones mapping wildfires in California to AR wearables enhancing situational awareness in low-visibility conditions—this survey uncovers key applications, challenges, and societal impacts. Insights into human-centered design, trustbuilding, and policy evolution highlight a path forward for deploying these game-changing tools. By addressing barriers like cognitive overload, public skepticism, and ethical considerations, this research envisions a future where responders are faster, safer, and smarter in the face of disaster.
Large language models (LLMs) such as GPT-4o are gaining attention for their ability to mimic human behaviors, but their use in replicating hacking strategies in the field of cybersecurity is still not fully examined. This investigation assesses GPT-4o as a simulator for cyberattacks, focusing on an important area in utilizing AI to examine the decision-making processes of adversaries. Through the HackIT simulation platform, GPT-4o's decision-making was compared against 84 human participants across bus and hybrid network topologies, under varying configurations of temperature (0.5, 1, 1.5) and top-k sampling (2, 3, 4). The results showed that GPT-4o did as well as human participants. It used an average of 32 systems in bus topologies and 28 systems in mixed topologies, and the mean squared errors (MSE) ranged from 0.03 to 1.44 in each case. In particular, the AI model was better at adapting to linear configurations and was very good at recognizing real systems, sometimes surpassing human performance. These results show that GPT-4o has a lot of promise as a defense tool for predicting and evaluating, giving us new information about how attackers work and where networks are weak. To make LLM-driven defense solutions even better, more study should be done on dynamic and adaptive attack situations.
Air traffic management has long been associated with situation awareness, especially supporting pilots in assessment and response to challenging situations. For multi-domain air and space operations, artificial intelligence and recently large language models (LLMs) can increase semantic understanding. In this paper, we focus on LLM aerospace analysis of Notice to Airman (NOTAM) and Notice to Space Operators (NOTSO). The notices afford a communication of the situation that can be extracted as an ontology to support human operators. Results show that LLM-based clustering can facilitate cognitive situation awareness.
The increasing integration of advanced driver assistance systems (ADAS) and artificial intelligence (AI) in modern vehicles highlights the importance of reliable object recognition in road traffic, especially when quick decisions need to be made in difficult visibility conditions. This study investigates the human error probabilities in identifying and distinguishing objects such as cyclists, pedestrians, and traffic light colors under suboptimal conditions. It also evaluates the performance of human-AI teams in the same scenarios, comparing human-first and AI-first decision workflows. The results provide insights into the reliability of these approaches and offer guidance for optimizing workflows to improve road safety and efficiency for this specific example but also a clear view to the optimization of human-AI teaming.
Cultural heritage represents a valuable asset for any Country and in particular for those with a tourism-oriented economy. The increase in tourist flows, climate change, and the natural deterioration of heritage assets make cultural heritage management increasingly challenging. New technologies can help address these challenges if appropriately designed to support the work of managers, maintenance personnel, and operators. In this context, Situation Awareness emerges as a cornerstone for designing new decision support systems and cyber-physical systems, such as maintenance systems, designed around the user to enhance the monitoring and intervention activities required. The considerable size of some cultural heritage sites, such as archaeological parks, demands pervasive and efficient monitoring solutions. The cloud-edge architecture enables the deployment of sensors and actuators at the network's edge while aggregating data in the cloud to provide a global common operating picture for decision-makers, enhancing their SA and thereby improving performance. In this article, we propose a new cloud-edge architecture model based on the principles of Situation Awareness. An illustrative example focusing on the Archaeological Park of Paestum, in Italy, demonstrates the feasibility of this solution.
Supporting a user in exploring large information spaces, like collections of media objects, in order to support searching or learning about the content of a collection, is still a challenging issue. In this work, we present an exploratory information retrieval system that integrates dynamic auditory landmarks to enhance spatial orientation and thus making exploration more efficient. The system transforms a two-dimensional (2D) search map into an immersive three-dimensional (3D) environment with adaptive spatial audio cues influenced by user behavior. A preliminary user study suggests that auditory landmarks improve orientation and search efficiency, although the results are limited by the number of participants(ten). An analysis of tracked behavior has shown promising results regarding the ability of auditory landmarks to guide the user.
Research in human-robot interaction (HRI) often puts emphasis on either the cognitive level or on the physical level. In a scenario, where a robot physically guides a person to perform a complex series of tasks (e.g., a patient making tea), information is exchanged on the cognitive level and forces/torques are exchanged on the physical level, continuously. Such a continuous co-adaptive interaction between both agents and the environment requires the robot to be anticipating, proactive, and able to react flexibly to the user's intentions and situation context. The unification of sequential cognitive situation modeling and continuous robotic movement control is a challenge currently missing a conceptual framework. We conceptualize strategies on how to connect models of physical HRI and models of cognitive HRI, depending on the level of assistance provided by the robot system, from mere warnings of dangerous situations (level 1) to on-body continuous movement guidance (level 4). In this, we consider the requirements for the robot to be aware of the interaction environment and have a dynamic representation of the individual user. Our conceptual framework is intended to spark discussions and formalize assistance approaches with the aim to integrate cognitive and physical human-robot interaction approaches for anticipatory assistance in continuous dynamic tasks.
Artificial Intelligence (AI) is crucial for realizing the evolution of 6 G cellular-based systems. However, the trustworthiness of AI-based models and systems-including their transparency, robustness, human agency, and safety-is equally important for rapid adoption and regulatory compliance. This position paper focuses on the aspects of AI safety and explainability, and on its importance to situation awareness in future networks. We summarize related work, identify research gaps, and provide recommendations for integrating these aspects in a future telecommunication system, such as a 6 G network. We observe that the current state-of-the-art primarily targets modellevel safety and explainability, but argue that there are bigger challenges at the system-level and runtime. As we move towards 6 G networks, there is an urgent need for new research directions to address these challenges. We aim to raise awareness among the research community about these critical issues.
Trust can be defined as an information quality characteristic representing a subjective level of belief of a user (either human or automatic) that the information he is using can be admitted into the system, transferred between system processes, or used for making decisions. The trustworthiness of an automatic agent is defined by the level of reliability considered in a particular context and based on domain knowledge, and statistical information obtained from previous experience/experiments. The problem of defining trust in information provided by humans is more difficult since their characteristics can be unknown, information can be manipulated or affected by multiple biases. The paper discusses the problems of trust representation, incorporating it into a fusion-based system, and introduces an approach to modeling trust and distrust based on a unified framework of the Transferable Belief modal and Belief Based Argumentation.
We present an overview of the Enhanced Multi-Source Information Fusion(EMSIF)projectas well as the research work carried out during the project. The aim is to create an improved situation picture for the future application domain Advanced Air Mobility. The improvement comprises two parts On the one hand new types of data sources are integrated, such as OSINT data and Background Knowledge(BK)On the other hand conventional Multi-Source Information Fusion (MSIF)is often limited to the initial stage which focuses on estimating the states of individual entities. In contrast. the subsequent stage focuses on a higher level of inference which takes into account the relations between these entities to identify critical situations and derive conclusionsThat allows the operator to detect and evaluate hazardous situations more easily, even in crowded environments
Lane change assistance systems increase safety by providing warnings and other stability assistance to drivers to avert traffic dangers. In this contribution, lane change intention recognition was performed and applied to generate warnings for drivers to increase situation awareness and avoid imminent collision. The focus is to evaluate whether utilizing predictions of driver's lane change intentions to provide warnings in the event of imminent collision will result in decreased risk of accidents. It integrates an online Fuzzy-Random Forest (fuzzy-RF) approach with which collision warnings were generated. A total of 44 drivers (39 males and 5 females) among whom 22 experienced the lane change assistance system with intention recognition and corresponding warnings. The experimental and control groups were compared to determine if the intention-based warnings improve driving performance. The results indicate reduced risk of collision and enhanced performance in some lane change scenarios but no significant difference for lane keeping. In addition, the augmented reality head-up display imagery using familiar color schemes and a minimalist layout enable drivers to identify the direction or location of the potential danger without taking their eyes of the road.
The collaboration of humans, intelligent physical agents (e.g. robots) and sometimes intelligent information agents (AIs, chatbots) in man-machine teams is already found in highly structured (e.g. industrial) work environments. Usually interactions take place in pairs. In this position article, collaborations with the following characteristics, and associated problems, are discussed: (a) the agents are not mere tools or assistants, but proactively intervene as peers; (b) goals, strategies and actions are not completely predetermined, but evolve in the course of a dialogic process; (c) the man-machine teams are multiparty with multiple humans and intelligent agents engaging in dedicated group appearance and modelling. Cognitive, dialogic systems form the technical basis of such team settings, which combine methods of multimodal information processing. A well-known airplane emergency situation is used as a running example.
Information exchanged in naturalistic human communication is implicitly grounded in its situational context. In particular, messages exchanged via social media on on-going events, like large-scale crisis events, often assume that the actual situational context is shared by the correspondents and thus not made explicit in the message text itself. Since these messages cannot be accurately interpreted without factoring in this situational context, Natural Language Processing (NLP) is a challenging task in these domains. The breakthrough capabilities on fine-grained contextual understanding and Natural Language Inference (NLI) of the recently introduced Large Language Models (LLMs), however, suggest novel avenues for tackling this problem. In the present work, we thus aim to analyze current LLMs' situation understanding and situational inference capabilities, seeking to answer the question: How well do LLMs understand situational context? We contribute i) a formalization of situational context as a conditioning factor affecting the outcome of the target task, and ii) an empirical examination of formulating this situation conditioning as a prompt engineering problem, explored on the target task of Named Entity Recognition (NER) on social media analysis for crisis computing.
The integration of AI (artificial intelligence) in workplaces has increased automation but often at the cost of transparency, potentially undermining user trust. Adaptable, user-centered systems address this challenge by enhancing users' understanding of the system and tailoring interactions to their needs through adaptable elements that allow them to customize system settings. In an online study with a between-subjects design, 197 participants interacted with an adaptable or non-adaptable system for decision-making to examine the influence of its features on perception (transparency, fairness, control), trust, and intention to use. Results showed that adaptability positively affected perceived transparency, fairness, and especially control. Furthermore, the intention to use was positively influenced both directly by perceived fairness and indirectly through two serial mediation pathways: one from perceived fairness through trust, and another from perceived control through trust. This study underscores the importance of adaptable design elements in human-computer interaction, demonstrating that they enhance user perception and intention to use, whereas AI systems that restrict user involvement and autonomy risk diminishing both trust and intention to use.
