Search Results (30)

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Current teleoperated robotic systems for retinal surgery cannot effectively control subtle tool-to-tissue interaction forces. This limitation may lead to patient injury caused by the surgeon’s mistakes. To improve the safety of retinal surgery, this paper proposes a haptic shared control framework for teleoperation based on a force-constrained supervisory controller. The supervisory controller leverages Control Barrier Functions (CBFs) and the interaction model to modify teleoperated inputs when they are deemed unsafe. This method ensures that the interaction forces at the slave robot’s end-effector remain within the safe range without the robot’s dynamic model and the safety margin. Additionally, the master robot provides haptic feedback to enhance the surgeon’s situational awareness during surgery, reducing the risk of misjudgment. Finally, simulated membrane peeling experiments are conducted in a controlled intraocular surgical environment using a teleoperated robotic system controlled by a non-expert. The experimental results demonstrate that the proposed control framework significantly reduces the rate of force constraint violation. Full article

Introduction: Child trafficking is a clear violation of human rights, robbing minors of their fundamental entitlements. These encompass the right to personal identity, familial bonds, cultural heritage, access to healthcare and proper nourishment, education, freedom of speech, and the assurance of safety and security. Children and young people, given their inherent vulnerability and limited access to support networks, frequently struggle to safeguard themselves effectively. This predicament presents traffickers with opportunities to exploit and manipulate them. Therefore, it is essential for professionals across various sectors—including education, healthcare, protective and social services, as well as the justice system—to undergo comprehensive training and be integrated into a robust social protection system. This preparation should equip them to conduct screenings, accurately assess needs, and adhere to international guidelines when addressing cases of child trafficking. Aim: The aim of this study is to explore the child-centered anti-trafficking approaches employed by Italy and Turkey, situated along migration pathways in the Mediterranean region and experiencing significant regular and irregular migration flows in recent years. Both nations fall within the classification of southern European welfare regimes. Methodology: This research specifically delves into the social protection policies aimed at children and young victims established by these two countries. Carried out between 1 February 2020 and 20 May 2021, this study employed a semi-structured interview approach, conducting qualitative in-depth interviews in both Italy and Turkey. This research targeted experts from various disciplines engaged in combating human trafficking in both countries, with a total sample size of 46 participants, comprising 15 experts from Italy and 31 from Turkey. Grounded theory formed the basis of the study, with data analyzed using the MAXQDA 2020 Pro Analytics program, employing a multidisciplinary and empowerment approach. Results: The analysis yielded 2942 codes, 17 sub-themes, and four main themes. The study identified four main themes: (i) characteristics of child victims and vulnerable child groups, (ii) services provided to at-risk groups and child trafficking victims within the current national counter-trafficking framework, (iii) challenges encountered in delivering services to children and young individuals, and (iv) recommendations for establishing an effective and child-centered protection system. Discussion and Conclusion: It is imperative to ensure that victims of child trafficking have access to comprehensive social protection measures. It has been noted that both Italy and Turkey offer various services to victims of child trafficking, including in-kind and -cash social assistance, free legal aid, shelter services, access to education and healthcare, as well as prevention, awareness, and advocacy programs. However, there are also differences between the two countries in certain aspects. Recommendations aimed at addressing these differences can be developed by adhering to the minimum standards outlined in the Council of Europe Convention on Action against Trafficking in Human Beings. Full article

Despite the increasing sophistication of autonomous vehicles (AVs) and promises of increased safety, accidents will occur. These will corrode public trust and negatively impact user acceptance, adoption and continued use. It is imperative to explore methods that can potentially reduce this impact. The aim of the current paper is to investigate the efficacy of informational assistants (IAs) varying by anthropomorphism (humanoid robot vs. no robot) and dialogue style (conversational vs. informational) on trust in and blame on a highly autonomous vehicle in the event of an accident. The accident scenario involved a pedestrian violating the Highway Code by stepping out in front of a parked bus and the AV not being able to stop in time during an overtake manoeuvre. The humanoid (Nao) robot IA did not improve trust (across three measures) or reduce blame on the AV in Experiment 1, although communicated intentions and actions were perceived by some as being assertive and risky. Reducing assertiveness in Experiment 2 resulted in higher trust (on one measure) in the robot condition, especially with the conversational dialogue style. However, there were again no effects on blame. In Experiment 3, participants had multiple experiences of the AV negotiating parked buses without negative outcomes. Trust significantly increased across each event, although it plummeted following the accident with no differences due to anthropomorphism or dialogue style. The perceived capabilities of the AV and IA before the critical accident event may have had a counterintuitive effect. Overall, evidence was found for a few benefits and many pitfalls of anthropomorphising an AV with a humanoid robot IA in the event of an accident situation. Full article

With the rapid development of autonomous driving technology, ensuring the safety and reliability of vehicles under various complex and adverse conditions has become increasingly important. Although autonomous driving algorithms perform well in regular driving scenarios, they still face significant challenges when dealing with adverse weather conditions, unpredictable traffic rule violations (such as jaywalking and aggressive lane changes), inadequate blind spot monitoring, and emergency handling. This review aims to comprehensively analyze these critical issues, systematically review current research progress and solutions, and propose further optimization suggestions. By deeply analyzing the logic of autonomous driving algorithms in these complex situations, we hope to provide strong support for enhancing the safety and reliability of autonomous driving technology. Additionally, we will comprehensively analyze the limitations of existing driving technologies and compare Advanced Driver Assistance Systems (ADASs) with Full Self-Driving (FSD) to gain a thorough understanding of the current state and future development directions of autonomous driving technology. Full article

One of the factors that threaten traffic safety and cause various traffic problems is distracted drivers. Various studies have been carried out to ensure traffic safety and, accordingly, to reduce traffic accidents. This study aims to determine driver-distraction classes and detect driver violations with deep learning algorithms and decision-making methods. Different driver characteristics are included in the study by using a dataset created from five different countries. Weight classification in the range of 0–1 is used to determine the most important classes using the AHP method, and the most important 9 out of 23 classes are determined. The YOLOv8 algorithm is used to detect driver behaviors and distraction action classes. The YOLOv8 algorithm is examined according to performance-measurement criteria. According to mAP 0.5:0.95, an accuracy rate of 91.17% is obtained. In large datasets, it is seen that a successful result is obtained by using the AHP method, which is used to reduce transaction complexity, and the YOLOv8 algorithm, which is used to detect driver distraction. By detecting driver distraction, it is possible to partially avoid traffic accidents and the negative situations they create. While detecting and preventing driver distraction makes a significant contribution to traffic safety, it also provides a significant improvement in traffic accidents and traffic congestion, increasing transportation efficiency and the sustainability of cities. It also serves sustainable development goals such as energy efficiency and reducing carbon emissions. Full article

With the development and application of vehicle-infrastructure cooperative technology, the traffic regional safety related to intelligent connected vehicles (ICVs) has become the hotspot of the intelligent transportation system (ITS), and the integration of mixed autonomous and non-autonomous vehicles that are not cooperative in intersection areas has become a significant challenge due to the rapid advancement of autonomous vehicle technology. Autonomous vehicles in intersections with strong-structure and weak-rule characteristics pose a potential hazard in complex traffic situations. Studying the driving behavior of vehicles in intersections is of great significance due to the complex traffic environment, frequent traffic signals, and traffic violations, which can optimize the vehicle driving behavior and improve the safety and efficiency of intersection traffic. By using naturalistic driving data from the DAIR V2X-Seq dataset and general vehicle dynamic parameters, it is possible to obtain the joint-probability-density distribution of the bivariate dynamic parameters of a vehicle. This distribution represents the driving characteristics of vehicles in intersection traffic. The three vehicle dynamic parameters that have an impact on vehicles driving through the intersection area are velocity, angular velocity, and acceleration. The driving behavior characteristics of human-driven vehicles (HVs) and autonomous vehicles (AVs) were analyzed using the multivariate kernel density estimation (MKDE) method to establish the vehicle-behavior boundary. The assessment of the boundary model showed that it accurately characterizes the driving characteristics of HVs and AVs. This boundary can be used to improve the safety detection of intersection areas, enhancing the performance of autonomous vehicles and optimizing intersection traffic. Full article

While driving, stress is caused by situations in which the driver estimates their ability to manage the driving demands as insufficient or loses the capability to handle the situation. This leads to increased numbers of driver mistakes and traffic violations. Additional stressing factors are time pressure, road conditions, or dislike for driving. Therefore, stress affects driver and road safety. Stress is classified into two categories depending on its duration and the effects on the body and psyche: short-term eustress and constantly present distress, which causes degenerative effects. In this work, we focus on distress. Wearable sensors are handy tools for collecting biosignals like heart rate, activity, etc. Easy installation and non-intrusive nature make them convenient for calculating stress. This study focuses on the investigation of stress and its implications. Specifically, the research conducts an analysis of stress within a select group of individuals from both Spain and Germany. The primary objective is to examine the influence of recognized psychological factors, including personality traits such as neuroticism, extroversion, psychoticism, stress and road safety. The estimation of stress levels was accomplished through the collection of physiological parameters (R-R intervals) using a Polar H10 chest strap. We observed that personality traits, such as extroversion, exhibited similar trends during relaxation, with an average heart rate 6% higher in Spain and 3% higher in Germany. However, while driving, introverts, on average, experienced more stress, with rates 4% and 1% lower than extroverts in Spain and Germany, respectively. Full article

Korea’s industrial accident rate ranks high among Organization for Economic Co-operation and Development countries. Moreover, large-scale accidents have recently occurred. Accordingly, the requirements for management and supervision in industrial sites are increasing; in this context, the “Act on Punishment of Serious Accidents, etc.” has been enacted. Aiming to prevent such industrial accidents, various data collected by devices such as sensors and closed-caption televisions (CCTVs) are utilized to track workers and detect hazardous substances, gases, and fires at industrial sites. In this study, an industrial area requiring such technology is selected. A hazardous situation event is derived, and a dataset is built using CCTV data. A violation corresponding to a hazardous situation event is detected and a warning is provided. The events incorporate requirements extracted from industrial sites, such as those concerning collision risks and the wearing of safety equipment. The precision of the event violation detection exceeds 95% and the response and delay times are under 20 ms. Thus, this system is believed to be used at industrial sites and for other intelligent industrial safety prevention solutions. Full article

Drones are widely used in a number of key fields and are having a profound impact on all walks of life. Working out how to improve drone safety through fault detection is key to ensuring the smooth execution of tasks. At present, most research focuses on fault detection at the component level as it is not possible to locate faults quickly from the global system state of a UAV. Moreover, most methods are offline detection methods, which cannot achieve real-time monitoring of UAV faults. To remedy this, this paper proposes a fault detection method based on a fault mode database and runtime verification. Firstly, a large body of historical fault information is analyzed to generate a summary of fault modes, including fault modes at the system level. The key safety properties of UAVs during operation are further studied in terms of system-level fault modes. Next, a monitor generation algorithm and code instrumentation framework are designed to monitor whether a certain safety attribute is violated during the operation of a UAV in real time. The experimental results show that the fault detection method proposed in this paper can detect abnormal situations in a timely and accurate manner. Full article

In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However, common general-purpose object detection algorithms are difficult to deploy on low-computational-power embedded platforms situated at the edge due to their high model complexity. These algorithms suffer from drawbacks such as low operational efficiency, slow detection speed, and high energy consumption. To address this issue, a lightweight violation detection algorithm based on the SP (Segmentation-and-Product) attention mechanism, named SP-YOLO-Lite, is proposed to improve the YOLOv5s detection algorithm and achieve low-cost deployment and efficient operation of object detection algorithms on low-computational-power monitoring platforms. First, to address the issue of excessive complexity in backbone networks built with conventional convolutional modules, a Lightweight Convolutional Block was employed to construct the backbone network, significantly reducing computational and parameter costs while maintaining high detection model accuracy. Second, in response to the problem of existing attention mechanisms overlooking spatial local information, we introduced an image segmentation operation and proposed a novel attention mechanism called Segmentation-and-Product (SP) attention. It enables the model to effectively capture local informative features of the image, thereby enhancing model accuracy. Furthermore, a Neck network that is both lightweight and feature-rich is proposed by introducing Depthwise Separable Convolution and Segmentation-and-Product attention module to Path Aggregation Network, thus addressing the issue of high computation and parameter volume in the Neck network of YOLOv5s. Experimental results show that compared with the baseline network YOLOv5s, the proposed SP-YOLO-Lite model reduces the computation and parameter volume by approximately 70%, achieving similar detection accuracy on both the VOC dataset and our self-built SMPC dataset. Full article

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system’s robustness and scalability to larger indoor environments with more complex safety hazards. Full article

Vehicle cybersecurity is a serious concern, as modern vehicles are vulnerable to cyberattacks. How drivers respond to situations induced by vehicle cyberattacks is safety critical. This paper sought to understand the effect of human drivers’ risky driving style on response behavior to unexpected vehicle cyberattacks. A driving simulator study was conducted wherein 32 participants experienced a series of simulated drives in which unexpected events caused by vehicle cyberattacks were presented. Participants’ response behavior was assessed by their change in velocity after the cybersecurity events occurred, their post-event acceleration, as well as time to first reaction. Risky driving style was portrayed by scores on the Driver Behavior Questionnaire (DBQ) and the Brief Sensation Seeking Scale (BSSS). Half of the participants also received training regarding vehicle cybersecurity before the experiment. Results suggest that when encountering certain cyberattack-induced unexpected events, whether one received training, driving scenario, participants’ gender, DBQ-Violation scores, together with their sensation seeking measured by disinhibition, had a significant impact on their response behavior. Although both the DBQ and sensation seeking have been constantly reported to be linked with risky and aberrant driving behavior, we found that drivers with higher sensation seeking tended to respond to unexpected driving situations induced by vehicle cyberattacks in a less risky and potentially safer manner. This study incorporates not only human factors into the safety research of vehicle cybersecurity, but also builds direct connections between drivers’ risky driving style, which may come from their inherent risk-taking tendency, to response behavior to vehicle cyberattacks. Full article

This study aims to examine child pedestrian safety around parks by considering four rule-compliance measures: temporal, spatial, velocity and visual search compliance. In this regard, street crossing observations of 731 children were recorded at 17 crosswalks around four parks in Montreal, Canada. Information on child behaviors, road features, and pedestrian–vehicle interactions were gathered in three separate forms. Chi-square tests were used to highlight the individual, situational, behavioral and road environmental characteristics that are associated with pedestrian rule compliance. About half of our sampled children started crossing at the same time as the adults who accompanied them, but more rule violations were observed when the adult initiated the crossing. The child’s gender did not have a significant impact on rule compliance. Several variables were positively associated with rule compliance: stopping at the curb before crossing, close parental supervision, and pedestrian countdown signals. Pedestrian–car interaction had a mixed impact on rule compliance. Overall, rule compliance among children was high for each of our indicators, but about two-thirds failed to comply with all four indicators. A few measures, such as longer crossing signals and pedestrian countdown displays at traffic lights, may help to increase rule compliance and, ultimately, provide safer access to parks. Full article

The main goal of roadway lighting design is ensuring compliance with mandatory lighting standards and thus increasing safety for all road users. On the other hand, a design process being only a part of a road investment has to be completed in possibly a short time, due to business needs. The commonly used method for reconciling both requirements is using predefined lighting projects (templates) which are matched with similar, real-life lighting situations. This approach works well for a typical roadway lighting design but not necessarily for crosswalk illumination due to different specifics of underlying calculations (they focus on the contrast of a pedestrian against its background rather than roadway illumination). As one deals with pedestrian safety here, we decided to perform extensive tests to find out whether a standard compliant lighting project prepared for a given crosswalk can be safely applied (in terms of preserving standard compliance) to another similar crosswalk. To accomplish that, we investigated nearly 900 million situations obtained as modifications of the reference template. Results proved that even a 5% change of layout sizes (crosswalk width, lamp spacing, pole height etc.) makes 40% of obtained projects violate illumination requirements. The conclusion of this result is that the template-based design approach broadly used for roadway lighting cannot be applied for pedestrian crossings as it may cause serious safety issues. Full article