Search Results (534)

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems. Full article

With the advancement of digitization in the era of Industry 4.0 (I4.0), highly automated, semi-autonomous, and fully autonomous systems are emerging. Within this context, multi-agent systems (MAS) offer a promising approach for automating tasks and processes based on autonomous agents that work together in an overall system to increase the degree of system autonomy stepwise in a modular and flexible way. A critical research challenge is determining how these agents can collaboratively engage with both other agents and human operators to facilitate the gradual transition from automated to fully autonomous industrial systems. To close transparency and connectivity gaps, this study contributes with a framework for the collaboration of agents and humans in increasingly autonomous MAS based on a Digital Twin (DT). The framework specifies a standards-based data model for MAS representation and proposes to introduce a DT infrastructure as a service layer for system coordination, supervision, and interaction. To demonstrate the feasibility and assess the quality of the framework, it is implemented and evaluated in a case study in a real-world industrial scenario. Although additional long-term evaluations across different contexts are needed, the assessment of functional completeness and selected quality attributes show that the proposed framework provides a solid technical foundation that facilitates a transparent and seamless collaboration between agents and humans within increasingly autonomous industrial MAS. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

In the field of port security, traditional human–vehicle interaction conflict (HVIC) alarm algorithms predominantly rely on the bounding box overlap ratio. This criterion often fails in complex industrial environments, leading to excessive false positives caused by stationary vehicles, perspective distortion, and boarding/alighting activities. To address this limitation, this study proposes a dual-stage intelligent agent architecture designed to minimize false alarms. The system integrates YOLOv8 as a front-end lightweight detector for real-time candidate screening and Qwen2.5-VL, a domain-adaptive multimodal large model, as the back-end semantic verifier. A comprehensive dataset comprising one million port-specific images and videos was curated to support a novel two-phase training strategy: image pretraining for object recognition followed by video fine-tuning for temporal logic understanding. The agent dynamically interprets alarm events within their spatiotemporal context. Field trials at an operational wharf demonstrate that the proposed agent achieves an alarm precision of 95.7% and reduces false positives by over 50% across major error categories. This approach offers a highly reliable, automated solution for industrial security monitoring. Full article

Amid the dual-driven trends of Industry 5.0 and smart manufacturing integration, as well as the global imperative for manufacturing sustainability to address resource constraints, carbon neutrality goals, and circular economy demands, human–robot collaborative (HRC) manufacturing has emerged as a core direction for reshaping manufacturing production modes while aligning with sustainable development principles. This paper comprehensively reviews HRC manufacturing systems, summarizing their technical framework, practical applications, and development trends with a focus on the synergistic realization of operational efficiency and sustainability. Addressing the rigidity of traditional automated lines, inefficiency of manual production, and the unsustainable drawbacks of high energy consumption and resource waste in conventional manufacturing, HRC integrates humans’ flexible decision-making and environmental adaptability with robots’ high-precision and continuous operation, not only improving production efficiency, quality, and safety but also optimizing resource allocation, reducing energy consumption, and minimizing production waste to bolster manufacturing sustainability. Its core technologies include task allocation, multimodal perception, augmented interaction (AR/VR/MR), digital twin-driven integration, adaptive motion control, and real-time decision-making, all of which can be tailored to support sustainable production scenarios such as energy-efficient process scheduling and circular material utilization. These technologies have been applied in automotive, aeronautical, astronautical, and shipping industries, boosting high-end equipment manufacturing innovation while advancing the sector’s sustainability performance. Finally, challenges and future directions of HRC are discussed, emphasizing its pivotal role in driving manufacturing toward a balanced development of efficiency, intelligence, flexibility, and sustainability. Full article

Action quality assessment automates the evaluation of human movement proficiency, which is vital for applications like sports training and rehabilitation, where objective feedback enhances patient outcomes. Action quality assessment processes motion capture data to generate quality scores for action execution. In rehabilitation exercises, joints typically work synergistically in functional groups. However, existing methods struggle to accurately model the collaborative relationships between joints. Fixed joint grouping is not flexible enough, while fully adaptive grouping lacks the guidance of prior knowledge. In this paper, based on rehabilitation theory in clinical medicine, we propose a dynamic, motion-aware grouping strategy. A two-stream architecture independently processes joint position and orientation information. Fused features are adaptively clustered into 6 functional groups by a joint motion energy-driven learnable mask generator, and intra-group temporal modeling and inter-group spatial projection are achieved through two-stage attention interaction. Our method achieves competitive results and obtains the best scores on most exercises of KIMORE, while remaining comparable on UI-PRMD. Experimental results using the KIMORE dataset show that the model outperforms current methods by reducing the mean absolute deviation by 26.5%. Ablation studies validate the necessity of dynamic grouping and the two-stream design. The core design principles of this study can be extended to fine-grained action-understanding tasks such as surgical operation assessment and motor skill quantification. Full article

Real-time fine-grained human activity recognition (HAR) remains a challenging problem due to rapid spatial–temporal variations, subtle motion differences, and dynamic environmental conditions. Addressing this difficulty, we propose NovAc-DL, a unified deep learning framework designed to accurately classify short human-like actions, specifically, “pour” and “stir” from sequential video data. The framework integrates adaptive time-distributed convolutional encoding with temporal reasoning modules to enable robust recognition under realistic robotic-interaction conditions. A balanced dataset of 2000 videos was curated and processed through a consistent spatiotemporal pipeline. Three architectures, LRCN, CNN-TD, and ConvLSTM, were systematically evaluated. CNN-TD achieved the best performance, reaching 98.68% accuracy with the lowest test loss (0.0236), outperforming the other models in convergence speed, generalization, and computational efficiency. Grad-CAM visualizations further confirm that NovAc-DL reliably attends to motion-salient regions relevant to pouring and stirring gestures. These results establish NovAc-DL as a high-precision real-time-capable solution for deployment in healthcare monitoring, industrial automation, and collaborative robotics. Full article

As the deployment of Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS-L2) expands, understanding their real-world safety performance becomes essential. This study examines the severity and contributing factors of crashes involving vehicles equipped with ADS and ADAS-L2 technologies using NHTSA data. Using machine learning models on crash datasets from 2021 to 2024, this research identifies patterns and risk factors influencing injury outcomes. After data preprocessing and handling missing values for severity classification, four models were trained: logistic regression, random forest, SVM, and XGBoost. XGBoost outperformed the others for both ADS and ADAS-L2, achieving the highest accuracy and recall. Variable importance analysis showed that for ADS crashes, interactions with other road users and poor lighting were the strongest predictors of injury severity, while for ADAS-L2 crashes, fixed object collisions and low light conditions were most influential. From a policy and engineering perspective, this study highlights the need for standardized crash reporting and improved ADS object detection and pedestrian response. It also emphasizes effective human–machine interface design and driver training for partial automation. Unlike previous research, this study conducts comparative model-based evaluations of both ADS and ADAS-L2 using recent crash reports to inform safety standards and policy frameworks. Full article

In the context of the digital transition, office environments are increasingly shaped by flexibility, technological integration, and occupant-centered design. These transformations influence not only building operations but also the social dynamics and well-being of workers, thereby intersecting with the broader goals of socially sustainable design. To address this complexity, Building Management Systems (BMS) and Digital Twins must evolve from static automation to adaptive frameworks that recognize and respond to diverse workplace activities and social interactions. This study proposes a standardized taxonomy of office activities as a foundation for activity recognition and environment adaptation. A systematic literature review identified key activity categories and defining attributes, which were refined and validated through direct observations, diary logs, and semi-structured interviews in small, shared offices with open-plan workspaces. The resulting taxonomy comprises four main classes—Focused Work, Meetings, Shallow Work, and Resting—each defined by contextual attributes such as plannability, social interaction, number of participants, posture, modality, location, and duration. The framework supports the development of human-centric, situationally aware BMS capable of dynamically adjusting environmental conditions to promote comfort, well-being, and energy efficiency. By integrating user agendas and feedback, this approach contributes to more inclusive and socially sustainable work environments, aligning with the emerging paradigm of adaptive, human-oriented architecture. Full article

The shift toward remote and automated admission processes in higher education introduces new challenges, including evaluator subjectivity and risks of applicant fraud. The FAIR-VID project addresses these issues by developing an artificial intelligence system that integrates multimodal data fusion with semi-supervised deep learning to assess applicant video interviews, submitted documents, and form data. This paper presents the project’s data preprocessing pipeline, designed to fuse heterogeneous modalities and to support seamless interaction between AI agents and human decision-makers throughout the admission workflow. The proposed process is intentionally general, making it applicable not only to international university admissions but also to broader human resource management and hiring contexts. Emphasis is placed on the need for robust and transparent AI adoption in admission and recruitment, supported by open-source modules and models at every stage of interaction between applicants and institutions. As a proof of concept, we provide open-source solutions for the analysis of video interviews, images, and documents enriched with semantic descriptions generated by large multimodal and complementary AI models. The paper details the multi-phase implementation of this pipeline to create structured, semantically rich datasets suitable for training advanced deep learning systems for comprehensive applicant assessment and fraud detection. Full article

Human–computer interaction (HCI) is essential for optimizing smart greenhouse management and for fostering efficient and sustainable agricultural practices. A synthesis of recent advancements in diverse interfaces, including digital twins, virtual and augmented reality, mobile applications, and sensor-based controls, alongside the integration of artificial intelligence (AI), automation, and human–robot collaboration, was examined as part of advanced automation strategies. This study highlights the importance of user-centered and context-aware design to enhance usability, address challenges like simulation sickness, and cater to varied user demographics. Emphasis is placed on responsible, adaptive, and trustworthy interaction, ensuring effective decision support and promoting human–AI synergy. This review offers an integrated perspective on current developments, identifying pathways for future sustainable interaction design in controlled-environment agriculture. Full article

This study examines the role of algorithms—particularly artificial intelligence—in scientific research processes and how automation intersects with expert knowledge and the autonomy of the researcher. Drawing on 25 qualitative interviews with Italian university scholars in the social sciences and humanities, the research explores how academics either incorporate or resist AI at various stages in their scientific work, the strategies they employ to manage the relationship between professional expertise and algorithmic systems and the forms of trust, caution or scepticism that characterise these interactions. The findings reveal diverse patterns of use, non-use and critical engagement, ranging from instrumental and efficiency-oriented adoption to dialogical experimentation and from identity-based resistance to systemic reflexivity regarding the institutional implications of AI. The study also highlights the need to thoroughly examine the characteristics of disciplinary scientific cultures, while highlighting the importance of promoting algorithmic awareness to support scientific rigour in the digital age. Full article

Ancient walled cities represent key material evidence for early state formation and human–environment interaction on the northeastern Tibetan Plateau. However, traditional field surveys are often constrained by the vastness and complexity of the plateau environment. This study proposes an improved deep learning framework, AC-YOLOv11, to achieve automated detection of ancient city remains in the Qinghai Lake Basin using 0.8 m GF-2 satellite imagery. By integrating a dual-path attention residual network (AC-SENet) with multi-scale feature fusion, the model enhances sensitivity to faint geomorphic and structural features under conditions of erosion, vegetation cover, and modern disturbance. Training on the newly constructed Qinghai Lake Ancient City Dataset (QHACD) yielded a mean average precision (mAP@0.5) of 82.3% and F1-score of 94.2%. Model application across 7000 km² identified 309 potential sites, of which 74 were verified as highly probable ancient cities, and field investigations confirmed 3 new sites with typical rammed-earth characteristics. Spatial analysis combining digital elevation models and hydrological data shows that 75.7% of all ancient cities are located within 10 km of major rivers or the lake shoreline, primarily between 3500 and 4000 m a.s.l. These results reveal a clear coupling between settlement distribution and environmental constraints in the high-altitude arid zone. The AC-YOLOv11 model demonstrates strong potential for large-scale archaeological prospection and offers a methodological reference for automated heritage mapping on the Qinghai–Tibet Plateau. Full article

“GenAI” assistants are gradually being integrated into daily tasks and learning, but their uptake is no less contingent on perceptions of credibility or safety than on their capabilities per se. The current study hypothesizes and tests its proposed two-road construct consisting of two interface-level constructs, namely perceived transparency (PT) and perceived safety/guardrails (PSG), influencing “behavioral intention” (BI) both directly and indirectly, via the two socio-cognitive mediators trust in automation (TR) and psychological reactance (RE). Furthermore, we also provide formulations for the evaluative lenses, namely perceived usefulness (PU) and “perceived risk” (PR). Employing survey data with a sample of 365 responses and partial least squares structural equation modeling (PLS-SEM) with bootstrap techniques in SMART-PLS 4, we discovered that PT is the most influential factor in BI, supported by TR, with some contributions from PSG/PU, but none from PR/RE. Mediation testing revealed significant partial mediations, with PT only exhibiting indirect-only mediated relationships via TR, while the other variables are nonsignificant via reactance-driven paths. To uncover non-linearity and non-compensation, a Stage 2 multilayer perceptron was implemented, confirming the SEM ranking, complimented by an importance of variables and sensitivity analysis. In practical terms, the study’s findings support the primacy of explanatory clarity and the importance of clear rules that are rigorously obligatory, with usefulness subordinated to credibility once the latter is achieved. The integration of SEM and ANN improves explanation and prediction, providing valuable insights for policy, managerial, or educational decision-makers about the implementation of GenAI. Full article

This paper introduces a Symbiotic Digital Environment Framework (SDEF) that integrates Human Digital Twins (HDTs) and Machine Digital Twins (MDTs) to advance lifecycle circularity across all stages of the CADMID model (i.e., Concept, Assessment, Design, Manufacture, In-Service, and Disposal). Unlike existing frameworks that address either digital twins or sustainability in isolation, SDEF establishes a bidirectional adaptive system where human, machine, and environmental digital entities continuously interact to co-optimize performance, resource efficiency, and well-being. The framework’s novelty lies in unifying human-centric adaptability (via HDTs) with circular economy principles to enable real-time symbiosis between industrial processes and their operators. Predictive analytics, immersive simulation, and continuous feedback loops dynamically adjust production parameters based on operator states and environmental conditions, extending asset lifespan while minimizing waste. Two simulation-based scenarios in VR using synthetic data demonstrate the framework’s capacity to integrate circularity metrics (material throughput, energy efficiency, remanufacturability index) with human-machine interaction variables in virtual manufacturing environments. SDEF bridges Industry 4.0’s automation capabilities and Industry 5.0’s human-centric vision, offering a scalable pathway toward sustainable and resilient industrial ecosystems by closing the loop between physical and digital realms. Full article