Search Results (611)

Research into Remotely Piloted Aircraft Systems (RPASs) has expanded rapidly, yet the competencies, knowledge, skills, and other attributes (KSaOs) required of RPAS pilots remain comparatively underexamined. This review consolidates existing studies addressing human performance, subject matter expertise, training practices, and accident causation to provide a comprehensive account of the KSaOs underpinning safe civilian and commercial drone operations. Prior research demonstrates that early work drew heavily on military contexts, which may not generalize to contemporary civilian operations characterized by smaller platforms, single-pilot tasks, and diverse industry applications. Studies employing subject matter experts highlight cognitive demands in areas such as situational awareness, workload management, planning, fatigue recognition, perceptual acuity, and decision-making. Accident analyses, predominantly using the human factors accident classification system and related taxonomies, show that skill errors and preconditions for unsafe acts are the most frequent contributors to RPAS occurrences, with limited evidence of higher-level latent organizational factors in civilian contexts. Emerging research emphasizes that RPAS pilots increasingly perform data-collection tasks integral to professional workflows, requiring competencies beyond aircraft handling alone. The review identifies significant gaps in training specificity, selection processes, and taxonomy suitability, indicating opportunities for future research to refine RPAS competency frameworks and support improved operational safety. Full article

The growth of the cruise industry and rising passenger numbers have led to an increase in cruise-related accidents, presenting challenges for mass rescue operations. It is crucial to understand the evolution of MAss Life-Threatening Incidents at Sea (MALTISs) in order to make effective decisions in such situations. This study, therefore, presents a scenario deduction model for MALTIS, integrating knowledge element theory, Bayesian Networks (BNs), fuzzy set theory, and improved Dempster–Shafer (DS) evidence theory. Based on knowledge element theory, this study identifies the scenario elements in typical maritime accidents. Given the large scale and complex disaster chain characteristics of MALTISs, the BN method is employed to convert the scenario elements into BN nodes, therefore constructing the MALTIS deduction model. To minimize the subjectivity associated with expert assessments, this study combines fuzzy set theory and the improved DS evidence theory to integrate the opinions of multiple experts, thereby enhancing the reliability of the model’s deduction. BN inference is then used to calculate the probabilities of various situational states, and sensitivity analysis is conducted to identify the key nodes. The Costa Concordia grounding incident serves as an empirical case study. The deduction results closely align with the actual accident evolution, and sensitivity analysis reveals five critical nodes in the event’s progression. This validates the effectiveness of the proposed scenario deduction model. These findings demonstrate that the model can effectively support emergency decision-making in MALTISs. Full article

Road safety ranks among the most apparent concerns in present-day urban existence, with risky driving the most prevalent cause of road crashes. In this paper, we present an external camera video-based automatic hazardous driving behavior detection model for use in smart cities. We addressed the problem with a holistic approach covering data collection to hazardous driving behavior classification including zig-zag driving, risky overtaking, and speeding over a pedestrian crossing. Our strategy employs a specially generated dataset with diverse driving situations under diverse traffic conditions and luminosities. We advocate for a Multi-Speed Transformer model with dual vehicle trajectory data timescale operation to capture near-future actions in the context of extended driving trends. A new contribution lies in our symbiotic system which, apart from the detection of unsafe driving, also assumes the responsibility of triggering countermeasures through a real-time continuous loop with vehicle systems. Empirical results demonstrate the Multi-Speed Transformer’s performance with 97.5% in accuracy and 93% in F1-score over our balanced corpus, surpassing comparison baselines including Temporal Convolutional Networks and Random Forest classifiers by significant amounts. The performance is boosted to 98.7% in accuracy as well as 95.5% in F1-score with the symbiotic framework. They confirm the promise of leading-edge neural architectures paired with symbiotic systems in enhancing road safety in smart cities. The ability of the system to provide real-time risky driving behavior detection with mitigation offers a real-world solution for the prevention of accidents while not restricting driver autonomy, a balance between automatic intervention, and passive monitoring. Empirical evidence on the TRAF-derived corpus, which includes 18 videos and 414 labelled trajectory segments, indicates that the Multi-Speed Transformer reaches an accuracy of 97.5% and an F1-score of 93% under the balanced-training protocol, and in this configuration it consistently surpasses the considered baselines when we use the same data splits and the same evaluation metrics. Full article

The proactive safety behavior of construction workers is crucial for accident prevention. This study examines the mechanism through which altruistic leadership influences such behavior, proposing a theoretical model grounded in social exchange theory, self-determination theory, and situational strength theory. The model positions psychological empowerment as a mediator and perceived organizational support as a moderator. Hypotheses were tested using survey data from 718 construction workers in China. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by a multi-group analysis based on the workers’ age and weekly working hours. The results show that altruistic leadership significantly enhances proactive safety behavior. Psychological empowerment partially mediates this relationship, while perceived organizational support positively moderates the link between psychological empowerment and proactive safety behavior. Furthermore, the positive effect of altruistic leadership was more substantial among older workers and those with longer weekly working hours. In contrast, the mediating role of psychological empowerment was more pronounced among younger workers. These findings reveal the dual influence of internal psychological mechanisms and external contextual factors in the relationship between altruistic leadership and proactive safety behavior. This study helps managers foster safety proactivity by promoting altruistic leadership and supportive organizational environments. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

In the Republic of Korea, fire outbreaks caused by electrical devices are one of the most frequent accidents, causing severe damage to human lives and infrastructure. The metropolitan police, The National Institute of Scientific Investigation, and the National Fire Research Institute conduct fire root-cause inspections to determine whether these fires are external or internal infrastructure fires. However, obtaining results is a complex process. In addition, the situation has been hampered by the lack of sufficient digital forensics and relevant programs. Apart from electrical devices, multi-sockets are among the main fire instigators. In this study, we aim to verify the feasibility of utilizing YOLO-based deep-learning object detection models for fire-cause inspection systems for multi-sockets. Particularly, we have created a novel image dataset of multi-socket fire causes with 3300 images categorized into the three classes of socket, both burnt-in and burnt-out. This data was used to train various models, including YOLOv4-csp, YOLOv5n, YOLOR-csp, YOLOv6, and YOLOv7-Tiny. In addition, we have proposed an improved YOLOv5n-SE by adding a squeeze-and-excitation network (SE) into the backbone of the conventional YOLOv5 network and deploying it into a two-stage detector framework with a first stage of socket detection and a second stage of burnt-in/burnt-out classification. From the experiment, the performance of these models was evaluated, revealing that our work outperforms other models, with an accuracy of 91.3% mAP@0.5. Also, the improved YOLOv5-SE model was deployed in a web browser application. Full article

Industry 4.0 advanced technologies are increasingly used to monitor workers and reduce accident risks to ensure workplace safety. In this paper, we present an on-premise, rule-based safety management system that exploits the fusion of data from an Ultra-Wideband (UWB) Real-Time Locating System (RTLS) and AI-based video analytics to enforce context-aware safety policies. Data fusion from heterogeneous sources is exploited to broaden the set of safety rules that can be enforced and to improve resiliency. Unlike prior work that addresses PPE detection or indoor localization in isolation, the proposed system integrates an UWB-based RTLS with AI-based PPE detection through a rule-based aggregation engine, enabling context-aware safety policies that neither technology can enforce alone. In order to demonstrate the feasibility of the proposed approach and showcase its potential, a proof-of-concept implementation is developed. The implementation is exploited to validate the system, showing sufficient capabilities to process video streams on edge devices and track workers’ positions with sufficient accuracy using a commercial solution. The efficacy of the system is assessed through a set of seven safety rules implemented in a controlled laboratory scenario, showing that the proposed approach enhances situational awareness and robustness, compared with a single-source approach. An extended validation is further employed to confirm practical reliability under more challenging operational conditions, including varying camera perspectives, diverse worker clothing, and real-world outdoor conditions. Full article

Road critical infrastructure, especially bridges, is vulnerable to threats such as extreme weather, traffic overload, accidents, and natural disasters, which can compromise stability or operability. Timely mapping of these threats and modeling crisis scenarios are vital for resilience. This study explores how Virtual Operations Support Teams (VOSTs) can enhance threat mapping and scenario modeling. VOSTs collect real-time data (e.g., from social media and sensors) and produce digital maps and analyses to improve situational awareness. The study focuses on the types of data VOSTs gather and the resulting changes in mapping procedures and scenario parameters. The findings indicate that integrating VOST capabilities supports more effective crisis planning and response for road infrastructure management. Full article

In recent years, the Arctic region, with its abundant oil and gas resources, has become a new focus of global resource development. However, the complex natural environment, especially the effect of sea ice, poses a serious threat and challenge to the navigation safety. Accordingly, this paper focuses on the navigation risks of drilling ships in five sea areas of the Northeast Passage of the Arctic under the influence of environmental factors. A dynamic Bayesian network structure was established using the Interpretative Structural Model–Bayesian network method. Since some risk elements cannot be directly measured, the combined weight method is adopted to fill the sample data. The navigation risk situations of the five sea areas is analyzed by forward causal reasoning. Through reverse diagnostic reasoning, the main risk factors affecting navigation are obtained, and relevant suggestions are given. This has important implications for improving the ability of accident prevention and emergency handling in practical applications. The model was verified through instance verification based on scenario analysis and model verification based on sample data. The average accuracy rate of the obtained model is 83.4%. The results show that the model has certain validity and practicability in the analysis of navigation risks in Arctic shipping routes. Full article

Drowsy driving is a major contributor to road accidents, as reduced vigilance degrades situational awareness and reaction control. Reliable assessment of alertness versus drowsiness can therefore support accident prevention. Key gaps remain in physiology-based detection, including robust identification of microsleep and transient vigilance shifts, sensitivity to fatigue-related changes, and resilience to motion-related signal artifacts; practical sensing solutions are also needed. Using Electroencephalogram (EEG) recordings from the MIT-BIH Polysomnography Database (18 records; >80 h of clinically annotated data), we framed wakefulness–drowsiness discrimination as a binary classification task. From each 30 s segment, we extracted 61 handcrafted features spanning linear, nonlinear, and frequency descriptors designed to be largely robust to signal-quality variations. Three classifiers were evaluated—k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT)—alongside a DT-based bagging ensemble. KNN achieved 99% training and 80.4% test accuracy; SVM reached 80.0% and 78.8%; and DT obtained 79.8% and 78.3%. Data standardization did not improve performance. The ensemble attained 100% training and 84.7% test accuracy. While these results indicate strong discriminative capability, the training–test gap suggests overfitting and underscores the need for validation on larger, more diverse cohorts to ensure generalizability. Overall, the findings demonstrate the potential of machine learning to identify vigilance states from EEG. We present an interpretable EEG-based classifier built on clinically scored polysomnography and discuss translation considerations; external validation in driving contexts is reserved for future work. Full article

Nowadays, we live in the smart mobility era in which vehicles are equipped with small sensing devices to collect various road information. With such sensors, we are able to provide an overview of what is happening on the road and offer an efficient solution for transport problems such as congestion, accidents, avoiding traffic lights, fuel consumption, etc. Particularly, driver drowsiness is one of the most important problems that transportation systems face and mostly leads to severe accidents, injuries, and deaths. In order to overcome such a problem, a set of sensor devices has been integrated into vehicles to monitor driver and driving behaviors, and then to evaluate the driver’s situation, e.g., drowsy or awake. Unfortunately, most of the proposed drowsiness detection techniques are dedicated to analyzing one behavior type, but not both, which may affect the accuracy rate of the detection. In this paper, we propose an efficient drowsiness detection framework (RDDF) that may analyze one behavior or be adapted to both of them in order to increase the accuracy of drowsiness detection. Mainly, RDDF periodically monitors the driver and driving behaviors, extracts important patterns, and then uses and compares a set of supervised learning models to detect drowsy drivers. After that, RDDF proposes a modified version of the K-nearest neighbors (KNN) model called Jaccard-KNN (JKNN) that increases drowsiness detection accuracy and overcomes several challenges imposed by traditional models. The proposed framework has been preliminarily validated through real sensor data, and we show the effectiveness of our framework in detecting real-time drowsy drivers with an accuracy rate of up to $99\%$. Full article

In Hungary, the consumption of any alcoholic beverage before driving is illegal. A person is considered drunk if they have a blood alcohol concentration of 0.5 g per liter or more. The situation regarding drug use is also disappointing. This research analyses these effects on transport and their “outcome” by evaluating analyses based on police data, driver training data, and experimental data. The research aims to further raise awareness of the public health importance of this problem through a case–control study. Descriptive and correlational, statistical calculations were performed with a significance value of p < 0.05. Between 2019 and 2023, there were 10–13.000 drunk driving offenses and 1.000–1.300 drunk-driving accidents on the roads each year, most of which occurred in the capital and caused minor injuries. The results will be used to discover synergies to improve road safety. Full article

Driving under the influence (DUI) has long been recognized as a major contributor to traffic accidents. However, the factors influencing the severity of crashes in DUI situations may vary significantly between genders due to physiological and psychological differences. This study analyzes DUI single-vehicle crash data from Texas to construct a random-parameter logit model that captures gender-specific differences in crash severity. A partially constrained method is employed to better identify these gender-specific factors, emphasizing the importance of separately assessing DUI behavior for males and females in traffic safety analysis. The results reveal notable gender differences in the severity of injuries from DUI crashes. A comprehensive evaluation was conducted from four perspectives: driver characteristics, vehicle features, roadway conditions, and environmental factors. Out-of-sample simulations provided additional insights, showing that even at lower blood alcohol concentration (BAC) levels, the probability of severe injury increases significantly. In conclusion, this study not only uncovers the gender-specific mechanisms behind DUI crash severity but also offers valuable empirical evidence for integrating gender considerations into future traffic safety policies and interventions. Full article

Background/Objectives: Given the persistent threat of war and nuclear accidents, and the global reliance on marketed Prussian blue capsules manufactured in only a few countries without an openly accessible quantitative formulation, there is a critical need for robust tablet alternatives that ensure stability, scalability, and rapid deployment. This study focuses on the design and development of PB tablets for oral administration as decorporation agents for radioactive and toxic species, particularly for treatment in nuclear and radiological emergencies. Methods: Advanced tableting processes, including direct compression, wet granulation, and dry granulation, were employed to develop innovative Prussian blue tablet formulations and to provide significant flexibility for industrial-scale production. Comprehensive physicochemical and pharmacotechnical characterizations were performed to support the formulation and to ensure both the safety and efficacy of the PB tablets. Stability studies were conducted in accordance with ICH guidelines to evaluate product performance over time and to confirm that quality and performance attributes remained within specification. Results: Among the formulations evaluated, the direct compression (DC5) was recommended for industrial production due to its simplicity, short cycle time, and high throughput. Stability studies up to 18 months confirmed that the PB tablets remained within specification, and the program is ongoing at 24, 36, 48, and 60 months. Conclusions: This research provides a promising advancement in countermeasures for nuclear and radiological incidents by delivering a robust, scalable PB tablet formulation that can be rapidly manufactured and deployed in emergency situations. Full article

To address the safety challenges for personnel in the complex and hazardous environments of offshore drilling platforms, this paper introduces the Platform Safety Detection YOLO (PSD-YOLO), an enhanced, real-time object detection framework based on YOLOv10s. The framework integrates several key innovations to improve detection robustness: first, the Channel Attention-Aware (CAA) mechanism is incorporated into the backbone network to effectively suppress complex background noise interference; second, a novel C2fCIB_Conv2Former module is designed in the neck to strengthen multi-scale feature fusion for small and occluded targets; finally, the Soft-NMS algorithm is employed in place of traditional NMS to significantly reduce missed detections in dense scenes. Experimental results on a custom offshore platform personnel dataset show that PSD-YOLO achieves a mean Average Precision (mAP@0.5) of 82.5% at an inference speed of 232.56 FPS. The efficient and accurate detection framework proposed in this study provides reliable technical support for automated safety monitoring systems, holds significant practical implications for reducing accident rates and safeguarding personnel by enabling real-time warnings of hazardous situations, fills a critical gap in intelligent sensor monitoring for offshore platforms and makes a significant contribution to advancing their safety monitoring systems. Full article