Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty. This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery. Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery. The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.

Recent advancements in Human-Machine Interfaces (HMIs) have transformed in vehicle experiences through visual displays and infotainment systems. While these technologies enhance usability, they often exclude users with visual impairments by prioritizing sighted interaction models. Current HMI design guidelines rarely account for the needs of elderly or disabled individuals. This paper presents a critical review of literature on HMIs with a specific focus on visual accessibility. It highlights the persistent challenges faced by blind and low-vision users, identifies gaps in current design practices, and outlines strategies for more inclusive interfaces. Visual-centric systems remain inherently inaccessible to those with reduced acuity or contrast sensitivity. Despite the promise of autonomous vehicles (AVs) to expand independent mobility, their interfaces often replicate the same exclusions found in conventional vehicles. Multimodal feedback—integrating audio and haptics with visual cues—remains uncommon, despite being essential for equitable interaction. Voice inter- faces, tactile prompts, and simplified visual elements are key to enabling access. Yet, these features are rarely implemented systematically. Although user-centered design is broadly advocated, real-world implementation often neglects the input of disabled users. Participatory co-design remains limited, and AV systems continue to reflect sighted norms. Inclusive design benefits all users, not only by ensuring access but also by improving overall usability. This review offers theoretical and practical insights by synthesizing cross-disciplinary findings and proposing a framework for embedding accessibility throughout the design lifecycle. Inclusivity must be treated as a foundational principle, integrated from the outset of interface development rather than added post hoc as a correction.

Collisions involving vulnerable road users (VRUs) represent a major concern in contemporary mobility, especially in urban contexts where attentional demands, occlusions, and complex interactions can challenge drivers’ ability to anticipate risk. Supporting driver situation awareness through adaptive interfaces and intelligent sensing is a promising strategy, particularly when combined with personalized modeling of attentional and behavioral states. This paper presents a human-centered research framework developed to explore the use of distributed sensing and adaptive interaction for enhancing road safety. Starting from a set of high-risk VRU interaction scenarios, such as occluded crossings, unexpected overtaking, and low-visibility intersections, a design rationale is outlined for using real-time data from vehicle sensors, roadside infrastructure, and driver monitoring to generate timely, non-intrusive, and individualized feedback via the HMI. The research focuses on the development of a simulation-based infrastructure for evaluating adaptive safety strategies based on driver-specific patterns of attention and behavior, in alignment with the principles of the Driver Digital Twin (DrDT) paradigm. Methodological insight is provided into the design of this experimental infrastructure, the key performance indicators used, and the experimental hypotheses. This work contributes a theoretically grounded and experimentally structured setup that may inform similar efforts in driving simulations for studying personalized safety interaction strategies.

The increasing deployment of collaborative robots in industrial environments raises human-centered challenges related to mental stress, perceived safety, and situation awareness in fence-free human–robot interaction. Although adaptive and human-aware robot behaviors are often proposed to enhance safety, solid human-centered baselines are still needed to evaluate their impact on the user experience. This paper presents a Virtual Reality (VR) based experimental study assessing user experience in a shared human–robot workspace. The VR environment replicates an industrial palletizing cell, enabling controlled and repeatable interaction scenarios with a collaborative robotic arm. Participants performed tasks combining different levels of proximity to the robot’s motion and different patterns of human behavior, ranging from routine activities to unexpected movements, which could stem either from intentional deviations required by specific tasks or from variations in the operator’s state. The paper reports baseline results obtained under non-adaptive robot behavior, providing a reference condition for the evaluation of future adaptive strategies. User experience was investigated by considering mental stress, safety awareness, and affective responses. Results indicate low to moderate mental stress and high safety awareness, while affective responses vary as a function of proximity to the robot’s motion and enacted participant behavior in the shared workspace.

Control rooms are critical environments for monitoring and managing complex socio-technical systems across industries such as transportation, energy, and public safety. Decision Support Systems (DSS) play a pivotal role in assisting operators by processing vast amounts of data, streamlining decision-making processes, and reducing response times. The integration of AI into DSS, creating Intelligent DSS, introduces new challenges, particularly regarding explainability and trustworthiness. Operators must not only interpret complex AI-driven recommendations but also rely on them in high-stakes, time-critical scenarios. For instance, intelligent alarm management systems in railway control rooms are designed to help operators prioritize, filter, and manage alarm floods, reducing cognitive overload. However, their effectiveness heavily depends on aligning with operators' cognitive needs, maintaining situational awareness, and fostering trust in automated recommendations. This context presents new significant challenges for designing effective interactions between control room systems and operators, that differently for the back-end AI-based DSS solutions, remain less clearly defined. This gap complicates the development of clear strategies for ergonomic interaction design and their subsequent assessment. This study addresses these challenges through a systematic literature review, focusing on works within the domains of human factors and ergonomics. The review explores the following research questions: What are the main ergonomic issues identified in the current state of the art regarding operator interaction with Intelligent DSS in control rooms? What are the key interaction strategies proposed to address these issues, and what performance indicators have been identified? Performance indicators are defined operationally and accompanied by detailed methodologies for their calculation, ensuring their applicability to other design projects. These indicators include measures encompassing both objective and subjective aspects, related to situational awareness metrics, and trust in AI systems. By synthesizing research perspectives and providing actionable guidelines, this study offers a foundational reference for ergonomic design efforts in control room environments. It seeks to overcome current limitations and advance the development of safer, more efficient, and operator-friendly systems, with a particular focus on railway applications.

This paper describes the NextPerception project’s outcomes organized as examples motivated by user stories. The project developed next-generation perception sensors and enhanced the distributed intelligence paradigm to build versatile, secure, reliable and proactive human monitoring systems, in turn applied in use cases in health and automotive domains.

This paper provides an overview of the domain challenges, use cases, objectives, high-level concepts, intended innovations, and expected impact of the DistriMuSe project. The project’s main aim is to enhance human health and safety by improved sensing of human presence, behaviour, intentions and vital signs in a collaborative or common environment by means of multi-sensor systems, distributed processing and machine learning. The use cases address challenges in health monitoring of elderly, sleep and exercise, of drivers and vulnerable road users in traffic and of people interacting with robots in a factory environment. Technical development in the project focuses on unobtrusive monitoring sensors, multi-sensor systems, distribution of computation and intelligence, and domain specific needs for the use cases.

Design cards are widely used in Human-Computer Interaction (HCI) and design practice to support ideation, reflection, and collaboration. Among them, the SBAM cards were developed to assist in the design of mobile applications that promote sustainable behaviors by translating behavioral principles into actionable design prompts. This study evaluates the effectiveness of the SBAM cards in an advanced educational context involving participants from a professional training program in mobile app development. An experimental group using the cards was compared with a control group using theoretical documentation, focusing on four key user experience dimensions—perceived self-efficacy, creativity support, usability, and usefulness—as well as on the theoretical grounding and creativity of the app concepts produced. Participants with specific design expertise reported significant improvements across all user experience dimensions when using the SBAM cards. In addition, their project outcomes showed higher levels of theoretical integration and originality. These findings not only confirm the positive effects previously observed with novice designers in previous research, but also demonstrate the perceived value and adaptability of the SBAM cards in expert-level mobile app design settings, thus supporting their integration into design-driven sustainability initiatives across educational, institutional, and professional domains.

This study investigates how virtual reality (VR) and haptic technologies can enhance the efficiency and sustainability of automotive HMI design through a co-design focus group with seven expert designers, exploring their expectations, attitudes, and concerns. A user story and five potential use cases were developed based on literature findings and used as discussion materials. VR was recognized as a promising tool for usability testing, enabling early-stage assessment of ergonomics and interaction design, thereby reducing reliance on physical prototypes and minimizing environmental and economic costs. Despite the potential of haptic feedback, participants expressed skepticism regarding its technological maturity, highlighting the need for further development to ensure its effectiveness in design testing. As highlighted by the co-design focus group results, future development efforts should focus on enhancing VR realism, improving haptic feedback, and optimizing HMI prototype management, while also addressing VR context customization, data collection for usability testing, and backend challenges.

The contribution presents the development of a data model for the study of Neapolitan villas, carried out within the NEA_VIA project in dialogue with art and architectural historians. This model formed the basis for both an analysis form of the villa and its digital archive, implemented through the Airtable platform, which is increasingly used for entering and managing research data, particularly in the digital humanities. The solution supported collaborative data collection and remote validation. The paper highlights the strengths and weaknesses of this approach in order to inform future similar research projects.

Behavioral changes are critical for addressing sustainability challenges, which have become increasingly urgent due to the growing impact of global greenhouse gas emissions on ecosystems and human livelihoods. However, translating awareness into meaningful action requires practical tools to bridge this gap. Mobile applications, utilizing strategies from human–computer interaction (HCI) such as gamification, nudging, and persuasive technologies, have proven to be powerful in promoting sustainable behaviors. To support designers in developing effective apps of this kind, theory-based design guidelines were created, drawing on established theories and design approaches aimed at shaping and encouraging virtuous user behaviors fostering sustainability. To make these guidelines more accessible and enhance their usability during the design phase, this study presents their transformation into the SBAM card deck, a deck of 11 design cards. The SBAM cards aim to simplify theoretical concepts, stimulate creativity, and provide structured support for design discussions, helping designers generate solutions tailored to specific project contexts. This study also evaluates the effectiveness of the SBAM cards in the design process through two workshops with design students. Results show that the cards enhance ideation, foster creativity, and improve designers’ perceived self-efficacy compared to the exploitation of the same design guidelines information presented in traditional textual formats. This paper discusses the SBAM cards design and evaluation methodology, findings, and implications, offering insights into how the SBAM design cards can bridge the gap between theory and practice in sustainability-focused mobile app development. To ensure broader accessibility, the SBAM cards have been made available to the public through a dedicated website.

Phishing attacks continue to pose a significant threat to online security, targeting individuals across various domains. This paper presents an investigation into the eye movements differences exhibited by individuals with varying levels of computer science experience when confronted with phishing attempts. Leveraging advanced eye-tracking tools, our study scrutinized the gaze patterns and response strategies of computer science experts and novices when exposed to simulated phishing email scenarios. Our findings reveal striking disparities in the visual and cognitive processing of phishing content between the two groups. Computer science experts displayed a heightened vigilance, exhibiting more efficient scanning of suspicious elements and quicker recognition of phishing indicators. Conversely, novices exhibited longer fixation times on deceptive elements and were prone to falling for fraudulent schemes. The insights gained from our research hold valuable implications for the development of targeted cybersecurity education and the design of more effective anti-phishing tools and email clients.

Purpose: This study examines patients' perspectives on the integration of artificial intelligence (AI) in radiology through focus groups, aiming to identify the main issues and areas for improvement. It is part of a larger research project that employs various methodologies to explore the views of both patients and radiologists regarding AI tools. Methods: We conducted two focus groups using a narrative story and vignettes: one with patients who self-assessed as AI experts and the other with non-AI experts. Results: The focus groups revealed diverse opinions on AI use in diagnostics, focusing on six main topics: acceptance, concerns, communication between radiologists and patients, explainability of AI, medical records, and emotional aspects. Conclusions: The findings underscore the importance of developing patient-centric AI solutions to build trust in AI-assisted diagnostic tools, considering emotional and communicational aspects and addressing both physician and patient concerns to facilitate smoother integration of AI in radiology.

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface.This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

Environmental change must be addressed as an urgent matter that directly affects each one of us, rather than being viewed solely as a future concern. Indeed, when combined, the actions of individuals can make a significant difference in addressing the global climate crisis. To achieve this, besides raising awareness about the issue and the potential impact each individual can have, a collective endeavor is necessary. This entails a shift, ranging from subtle to substantial, in people's mindsets, behaviors, and habits concerning consumption, mobility, and other crucial facets of daily life. The design of interactive technologies, especially mobile applications, can play a pivotal role in this context. This is due to the growing dependence individuals have on these personal tools to enhance various aspects of their lives. Starting from an overview of key theories regarding human behaviors and habits, we examine insights gained from persuasive technologies, interventions targeting digital behavior change, nudges, and gamification. Subsequently, we formulate design guidelines for mobile applications with the objective of cultivating more sustainable behaviors and habits (referred to as SBAM—Sustainable Behavior Applications for Mobile devices). We prototype an exemplary mobile application compliant with the guidelines and validate them in terms of their expected efficacy in fostering sustainable behaviors and habits by means of a focus group with 9 green users.

In scenarios of partially autonomous driving, drivers can easily become distracted and engage in secondary activities unrelated to driving. However, when it becomes necessary to take-over the control, they must be in the right conditions to resume driving safely. This article explores the design and evaluation of a Human-Machine Interface (HMI) that, leveraging the knowledge of the driver’s state enabled by intelligent driver monitoring systems, helps the driver stay focused on the road in case of distraction and relax in case of agitation. The article presents the results of an experimental campaign in a driving simulator with 11 participants, aimed at measuring the advantage in terms of take-over reaction times with or without the proposed solution. This advantage was measured in terms of hands-on-wheel time through video analysis and eyes-on-road time through eye-tracking data analysis. The results demonstrate significant advantages both in terms of reaction times and from the perspective of user experience. The study allowed the identification of new research insights for further exploration of interface strategy.