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13 pages, 1064 KiB  
Article
The Detection of Pedestrians Crossing from the Oncoming Traffic Lane Side to Reduce Fatal Collisions Between Vehicles and Older Pedestrians
by Masato Yamada, Arisa Takeda, Shingo Moriguchi, Mami Nakamura and Masahito Hitosugi
Vehicles 2025, 7(3), 76; https://doi.org/10.3390/vehicles7030076 - 20 Jul 2025
Viewed by 301
Abstract
To inform the development of effective prevention strategies for reducing pedestrian fatalities in an ageing society, a retrospective analysis was conducted on fatal pedestrian–vehicle collisions in Japan. All pedestrian fatalities caused by motor vehicle collisions between 2013 and 2022 in Shiga Prefecture were [...] Read more.
To inform the development of effective prevention strategies for reducing pedestrian fatalities in an ageing society, a retrospective analysis was conducted on fatal pedestrian–vehicle collisions in Japan. All pedestrian fatalities caused by motor vehicle collisions between 2013 and 2022 in Shiga Prefecture were reviewed. Among the 164 pedestrian fatalities (involving 92 males and 72 females), the most common scenario involved a pedestrian crossing the road (57.3%). In 61 cases (64.9%), pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side (i.e., crossing from right to left from the driver’s perspective, as vehicles drive on the left in Japan). In 33 cases (35.1%), pedestrians crossed from the vehicle’s lane side to the oncoming traffic lane side. Among cases of pedestrians crossing from the vehicle’s lane side, 54.5% were struck by the near side of the vehicle’s front, whereas 39.7% of those crossing from the oncoming traffic lane side were hit by the far side of the vehicle’s front (p = 0.02). Therefore, for both crossing directions, collisions frequently involved the front left of the vehicle. When pedestrians were struck by the front centre or front right of the vehicle, the collision speeds were higher when pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side rather than crossing from the vehicle’s lane side to the oncoming traffic lane side. A significant difference in collision speed was observed for impacts with the vehicle’s front centre (p = 0.048). The findings suggest that increasing awareness that older pedestrians may cross roads from the oncoming traffic lane side may help drivers anticipate and avoid potential collisions. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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23 pages, 7503 KiB  
Article
EMF Exposure of Workers Due to 5G Private Networks in Smart Industries
by Peter Gajšek, Christos Apostolidis, David Plets, Theodoros Samaras and Blaž Valič
Electronics 2025, 14(13), 2662; https://doi.org/10.3390/electronics14132662 - 30 Jun 2025
Viewed by 376
Abstract
5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) [...] Read more.
5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article
(This article belongs to the Special Issue Innovations in Electromagnetic Field Measurements and Applications)
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26 pages, 13313 KiB  
Article
Exploring Augmented Reality HMD Telemetry Data Visualization for Strategy Optimization in Student Solar-Powered Car Racing
by Jakub Forysiak, Piotr Krawiranda, Krzysztof Fudała, Zbigniew Chaniecki, Krzysztof Jóźwik, Krzysztof Grudzień and Andrzej Romanowski
Energies 2025, 18(12), 3196; https://doi.org/10.3390/en18123196 - 18 Jun 2025
Viewed by 447
Abstract
This article explores how different modalities of presenting telemetry data can support strategy management during solar-powered electric vehicle racing. Student team members using augmented reality head-mounted displays (AR HMD) have reported significant advantages for in-race strategy monitoring and execution, yet so far, there [...] Read more.
This article explores how different modalities of presenting telemetry data can support strategy management during solar-powered electric vehicle racing. Student team members using augmented reality head-mounted displays (AR HMD) have reported significant advantages for in-race strategy monitoring and execution, yet so far, there is no published evidence to support these claims. This study shows that there are specific situations in which various visualization modes, including AR HMDs, demonstrate improved performance for users with varying levels of experience. We analyzed racing team performance for specific in-race events extracted from free and circuit-based real race datasets. These findings were compared with results obtained in a controlled, task-based user study utilizing three visualization interface conditions. Our exploration focused on how telemetry data visualizations influenced user performance metrics such as event reaction time, decision adequacy, task load index, and usability outcomes across four event types, taking into account both the interface and participant experience level. The results reveal that while traditional web application-type visualizations work well in most cases, augmented reality has the potential to improve race performance in some of the examined free-race and circuit-race scenarios. A notable novelty and key finding of this study is that the use of augmented reality HMDs provided particularly significant advantages for less experienced participants in most of the tasks, underscoring the substantial benefits of this technology for the support of novice users. Full article
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14 pages, 2206 KiB  
Article
CNN-Based Automatic Detection of Beachlines Using UAVs for Enhanced Waste Management in Tailings Storage Facilities
by Sergii Anufriiev, Paweł Stefaniak, Wioletta Koperska, Maria Stachowiak, Artur Skoczylas and Paweł Stefanek
Appl. Sci. 2025, 15(10), 5786; https://doi.org/10.3390/app15105786 - 21 May 2025
Viewed by 392
Abstract
Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to [...] Read more.
Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to control the distance from the water to the dam, regular manual inspections need to be carried out. In this article, we propose a method for automatic detection of the water-beach line based on photographs from an unmanned aerial vehicle (UAV). An algorithm based on MobileNet v2 convolutional neural network architecture was developed for the classification of images collected by the UAV. Based on the results of this classification, the border between the water and the beach is defined. Several approaches to the model training were tested. Accuracy for the validation set reaches up to 97% for particular image fragments. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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33 pages, 5667 KiB  
Article
Modal Analyses of Flow and Aerodynamic Characteristics of an Idealized Ground Vehicle Using Dynamic Mode Decomposition
by Hamed Ahani and Mesbah Uddin
Vehicles 2025, 7(2), 47; https://doi.org/10.3390/vehicles7020047 - 19 May 2025
Viewed by 547
Abstract
This study investigates the connection between coherent structures in the flow around a vehicle and the aerodynamic forces acting on its body. Dynamic Mode Decomposition (DMD) was applied to analyze the flow field of a squareback Ahmed body at [...] Read more.
This study investigates the connection between coherent structures in the flow around a vehicle and the aerodynamic forces acting on its body. Dynamic Mode Decomposition (DMD) was applied to analyze the flow field of a squareback Ahmed body at ReH=7.7×105. DMD enabled the identification of coherent structures in the near and far wake by isolating their individual oscillation frequencies and spatial energy distributions. These structures were classified into three regimes based on their underlying mechanisms: symmetry breaking, bubble pumping, and large-scale vortex shedding in range of St0.2. The energy contributions of these flow regimes were quantified across different regions of the flow field and compared to the aerodynamic forces on the body. Additionally, the linear correlation between pressure and velocity components was examined using Pearson correlation coefficients of DMD spectral amplitudes. The locations of maximum and minimum correlation values, as well as their relationship to energy contributions, were identified and analyzed in detail. Full article
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21 pages, 4104 KiB  
Article
Linkage Analysis Between Coastline Change and Both Sides of Coastal Ecological Spaces
by Xianchuang Fan, Chao Zhou, Tiejun Cui, Tong Wu, Qian Zhao and Mingming Jia
Water 2025, 17(10), 1505; https://doi.org/10.3390/w17101505 - 16 May 2025
Cited by 2 | Viewed by 399
Abstract
As the first marine economic zone, the coastal zone is a complex and active ecosystem, serving as an important resource breeding area. However, during the process of economic development, coastal zone resources have been severely exploited, leading to fragile ecology and frequent natural [...] Read more.
As the first marine economic zone, the coastal zone is a complex and active ecosystem, serving as an important resource breeding area. However, during the process of economic development, coastal zone resources have been severely exploited, leading to fragile ecology and frequent natural disasters. Therefore, it is imperative to analyze coastline changes and their correlation with coastal ecological space. Utilizing long-time series high-resolution remote sensing images, Google Earth images, and key sea area unmanned aerial vehicle (UAV) remote sensing monitoring data, this study selected the coastal zone of Ningbo City as the research area. Remote sensing interpretation mark databases for coastline and typical coastal ecological space were established. Coastline extraction was completed based on the visual discrimination method. With the help of the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI) and maximum likelihood classification, a hierarchical classification discrimination process combined with a visual discrimination method was constructed to extract long-time series coastal ecological space information. The changes and the linkage relationship between the coastlines and coastal ecological spaces were analyzed. The results show that the extraction accuracy of ground objects based on the hierarchical classification process is high, and the verification effect is improved with the help of UAV remote sensing monitoring. Through long-time sequence change monitoring, it was found that the change in coastline traffic and transportation is significant. Changes in ecological spaces, such as industrial zones, urban construction, agricultural flood wetlands and irrigation land, dominated the change in artificial shorelines, while the change in Spartina alterniflora dominated the change in biological coastlines. The change in ecological space far away from the coastline on both the land and sea sides has little influence on the coastline. The research shows that the correlation analysis between coastline and coastal ecological space provides a new perspective for coastal zone research. In the future, it can provide technical support for coastal zone protection, dynamic supervision, administration, and scientific research. Full article
(This article belongs to the Special Issue Advanced Remote Sensing for Coastal System Monitoring and Management)
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16 pages, 4809 KiB  
Article
First-Arrival Tomography for Mountain Tunnel Hazard Assessment Using Unmanned Aerial Vehicle Seismic Source and Enhanced by Supervirtual Interferometry
by Jun Zhang, Rongyi Qian, Zhenning Ma, Xiaoqiong Lei, Jianyu Ling, Xu Liu and Guibin Zhang
Remote Sens. 2025, 17(10), 1686; https://doi.org/10.3390/rs17101686 - 11 May 2025
Viewed by 461
Abstract
Preliminary tunnel surveys are essential for identifying geological hazards such as aquifers, faults, and karstic zones. While first-arrival tomography is effective for imaging shallow anomalies, traditional seismic sources face significant limitations in forested mountainous regions due to mobility, cost, and environmental impact. To [...] Read more.
Preliminary tunnel surveys are essential for identifying geological hazards such as aquifers, faults, and karstic zones. While first-arrival tomography is effective for imaging shallow anomalies, traditional seismic sources face significant limitations in forested mountainous regions due to mobility, cost, and environmental impact. To address this, we deployed a seismic source delivered by an unmanned aerial vehicle (UAV) for a highway tunnel survey in Lijiang, China. The UAV system, paired with nodal geophones, enabled rapid, low-impact, and high-resolution data acquisition in rugged terrain. To enhance the weak far-offset refractions affected by near-surface attenuation, we applied supervirtual refraction interferometry (SVI), which significantly improved the signal-to-noise ratio and expanded the usable first-arrival dataset. The combined use of UAV excitation and SVI processing produced a high-precision P-wave velocity model through traveltime tomography, aligned well with borehole data. This model revealed the spatial distribution of weathered zones and bedrock interfaces, and allowed us to infer potential fracture zones. The results offer critical guidance for tunnel alignment and hazard mitigation in complex geological settings. Full article
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27 pages, 6543 KiB  
Article
Driver Injury Prediction and Factor Analysis in Passenger Vehicle-to-Passenger Vehicle Collision Accidents Using Explainable Machine Learning
by Peng Liu, Weiwei Zhang, Xuncheng Wu, Wenfeng Guo and Wangpengfei Yu
Vehicles 2025, 7(2), 42; https://doi.org/10.3390/vehicles7020042 - 3 May 2025
Viewed by 690
Abstract
Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. [...] Read more.
Vehicle accidents, particularly PV-PV collisions, result in significant property damage and driver injuries, causing substantial economic losses and health risks. Most existing studies focus on macro-level predictions, such as accident frequency, but lack detailed collision-level analysis, which limits the precision of severity prediction. This study investigates various accident-related factors, including environmental conditions, vehicle attributes, driver characteristics, pre-crash scenarios, and collision dynamics. Data from NHTSA’s CRSS and FARS datasets were integrated and balanced using random over-sampling and under-sampling techniques to address severity-level data imbalances. The mRMR algorithm was employed for feature selection to minimize redundancy and identify key features. Five advanced machine learning models were evaluated for severity prediction, with XGBoost achieving the best performance: 84.9% accuracy, 84.85% precision, 84.90% recall, and an F1-score of 84.87%. SHAP analysis was utilized to interpret the model and conduct a comprehensive analysis of accident features, including their importance, dependencies, and combined effects on severity prediction. This study achieved high accuracy in predicting accident severity across all levels in PV-PV collisions. Moreover, by integrating the SHAP model interpretation method, we conducted detailed feature analysis at global, local, and individual case levels, thereby filling the gap in PV-PV accident severity prediction and feature analysis. Full article
(This article belongs to the Special Issue Novel Solutions for Transportation Safety)
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36 pages, 2990 KiB  
Review
Advances in Multi-Source Navigation Data Fusion Processing Methods
by Xiaping Ma, Peimin Zhou and Xiaoxing He
Mathematics 2025, 13(9), 1485; https://doi.org/10.3390/math13091485 - 30 Apr 2025
Cited by 1 | Viewed by 736
Abstract
In recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering theory, such as Kalman Filtering [...] Read more.
In recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering theory, such as Kalman Filtering (KF), Particle Filtering (PF), and Federated Filtering (FF), have been continuously optimized, enabling effective handling of non-linear and non-Gaussian noise issues. On the other hand, the introduction of AI technologies like deep learning and reinforcement learning has provided new solutions for multi-source data fusion, particularly enhancing adaptive capabilities in complex and dynamic environments. Additionally, methods based on Factor Graph Optimization (FGO) have also demonstrated advantages in multi-source data fusion, offering better handling of global consistency problems. In the future, with the widespread adoption of technologies such as 5G, the Internet of Things, and edge computing, multi-source navigation data fusion is expected to evolve towards real-time processing, intelligence, and distributed systems. So far, fusion methods mainly include optimal estimation methods, filtering methods, uncertain reasoning methods, Multiple Model Estimation (MME), AI, and so on. To analyze the performance of these methods and provide a reliable theoretical reference and basis for the design and development of a multi-source data fusion system, this paper summarizes the characteristics of these fusion methods and their corresponding application scenarios. These results can provide references for theoretical research, system development, and application in the fields of autonomous driving, unmanned vehicle navigation, and intelligent navigation. Full article
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21 pages, 13911 KiB  
Article
A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
by Qiang Zhang and Hsin Guan
Appl. Sci. 2025, 15(9), 4946; https://doi.org/10.3390/app15094946 - 29 Apr 2025
Viewed by 382
Abstract
Road traffic regulations usually require that a vehicle can only move one lane during one lane change and must turn on the turn signal before changing lanes. Under such constraints, if automated vehicles can plan multiple lane-change maneuvers at one time, then not [...] Read more.
Road traffic regulations usually require that a vehicle can only move one lane during one lane change and must turn on the turn signal before changing lanes. Under such constraints, if automated vehicles can plan multiple lane-change maneuvers at one time, then not only adjacent lanes but also farther lanes can be selected as target lanes when making decisions. This would help improve the driving performance in multi-lane scenarios. Many current lane-selection or lane-change methods focus on the surrounding region of the ego vehicle, usually only considering adjacent lanes as potential target lanes. This paper proposes a new tactical functional model that attempts to perform lane-level driving task planning and decision-making over a road area far beyond the surrounding region of the ego vehicle. We refer to this road area as the “outlook region”. In this functional model, the decision-making of lane-level driving tasks will take the overall performance within the outlook region as the goal, rather than pursuing the optimal single lane-change maneuver. The proposed method is implemented using a directed graph-based approach and simulation tests are conducted. The results show that the proposed method helps improve the driving performance of automated vehicles in multi-lane scenarios. Full article
(This article belongs to the Section Transportation and Future Mobility)
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27 pages, 3865 KiB  
Article
Service Management of Employee Shuttle Service Under Inhomogeneous Fleet Constraints Using Dynamic Linear Programming: A Case Study
by Metin Mutlu Aydin, Edgar Sokolovskij, Piotr Jaskowski and Jonas Matijošius
Appl. Sci. 2025, 15(9), 4604; https://doi.org/10.3390/app15094604 - 22 Apr 2025
Viewed by 777
Abstract
Traffic congestion is becoming an increasing problem due to the rapid growth of the population. In the current situation, the mode choice of the people has a direct impact on traffic density. For this reason, many studies have been carried out by researchers [...] Read more.
Traffic congestion is becoming an increasing problem due to the rapid growth of the population. In the current situation, the mode choice of the people has a direct impact on traffic density. For this reason, many studies have been carried out by researchers and planners to reduce the number of vehicles on the road. Various strategies have been proposed, such as incentives for public transport, parking restrictions, parking pricing and car sharing. It is very important that these strategies are implemented by the institutions in order to reduce traffic during the commuting hours, which coincide with the rush hour. Especially in areas such as shipyards and industrial zones, which are far from the city center and relatively difficult to reach but which provide employment opportunities for thousands of people, a shuttle service is one of the most preferred strategies to discourage employees from using private cars. However, in companies with thousands of employees, this situation generates costs that cannot be ignored. The examined case study similarly needs to optimize and reduce operational costs related to fuel consumption, maintenance and tax expenses by optimizing the number of two different types of service vehicles required for employee transportation at the Yalova Shipyard. For this aim, a dynamic linear programming (DLP) model was used to achieve a cost-effective, sustainable and demand-responsive shuttle service. According to the analysis results, it was concluded that the annual fuel cost of the vehicles will be reduced by 33.9%, the maintenance cost by 35.2% and the annual tax cost by 49.3% by disposing of the unneeded vehicles (27%) in the studied Yalova Shipyard. Taking all these positive improvements into account, it is clear that the optimization study significantly reduces the costs incurred by the service. Full article
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28 pages, 344 KiB  
Article
Use of Drones in Disasters in the European Union: Privacy Issues and Lessons Learned from the COVID-19 Pandemic and Mass Surveillance Jurisprudence of the ECtHR and the CJEU
by Maria Maniadaki, Dimitrios D. Alexakis and Efpraxia-Aithra Maria
Laws 2025, 14(2), 27; https://doi.org/10.3390/laws14020027 - 16 Apr 2025
Cited by 1 | Viewed by 2098
Abstract
Severe earthquakes, extreme floods, tragic accidents, mega-fires, and even viruses belong to disasters that can destroy the economic, social, or cultural life of people. Due to the climate crisis, disasters will likely become more frequent and intense over the years. Unmanned aerial vehicles [...] Read more.
Severe earthquakes, extreme floods, tragic accidents, mega-fires, and even viruses belong to disasters that can destroy the economic, social, or cultural life of people. Due to the climate crisis, disasters will likely become more frequent and intense over the years. Unmanned aerial vehicles (UAVs/drones) have obtained an increasing role in disaster management, which was particularly evident during the COVID-19 pandemic. However, lack of social acceptability remains a limiting factor of drone usage. Drones as a means of state surveillance—possibly mass surveillance—are subject to certain limits since their advanced monitoring technology, including Artificial Intelligence, may affect human rights, such as the right to privacy. Due to the severity of the pandemic, which has been described as the “ideal state of emergency”, despite the rising use of drones, such privacy concerns have been underestimated so far. At the same time, the existing approach of the European Court of Human Rights (ECtHR) and the Court of Justice of the European Union (CJEU) regarding the COVID-19 health crisis and human rights during emergencies seems rather conservative and, thus, setting limits between conflicting rights in such exceptional circumstances remains vague. Under these conditions, the fear that the COVID-19 pandemic may have become a starting point for transitioning to a world normalizing the exception is evident. Such fear in terms of privacy implies a world with a narrowed scope of privacy; thus, setting questions and exploring the challenges about the future of drone regulation, especially in the European Union, are crucial. Full article
11 pages, 4877 KiB  
Proceeding Paper
Leveraging RFID for Road Safety Sign Detection to Enhance Efficiency and Notify Drivers
by Dhanasekar Ravikumar, Vijayaraja Loganathan, Pranav Ponnovian, Vignesh Loganathan and Bharanidharan Sivalingam
Eng. Proc. 2025, 87(1), 53; https://doi.org/10.3390/engproc2025087053 - 15 Apr 2025
Viewed by 272
Abstract
Road safety signboards are now difficult to see due to pollution and harsh weather elements such as snow and fog, which has resulted in more accidents. The problem is especially common in Western countries where snow can block these critical signs. An approach [...] Read more.
Road safety signboards are now difficult to see due to pollution and harsh weather elements such as snow and fog, which has resulted in more accidents. The problem is especially common in Western countries where snow can block these critical signs. An approach addressing this issue involves a system that uses Radio Frequency Identification (RFID) and Internet of Things (IoT). The real-time alerts that this system sends to drivers improve driver safety in complex environments. For this purpose, an RFID reader is placed in the vehicle, and passive RFID tags are attached to road safety signboards. The reader picks up the signal as a vehicle comes within range, and the warning for the vehicle is sent to the driver. It helps to reduce the number of accidents resulting from poor visibility. In addition, because its multi-lingual audio alerts the drive through speakers and visual warnings displayed on a display screen, the system is accessible to drivers from various regions. To make the system more sustainable, we added some solar panels to the system to cut costs as far as energy efficiency is concerned. The system combines GPS and GSM modules to provide the vehicle position in real time in the cloud. It gives better warnings and helps avoid accidents. In addition to improving road safety, the system offers support for the environment, by limiting emissions and waste of resources caused by accidents. Traffic patterns can thus be studied with the data, creating more efficient and ecofriendly transportation systems. This solution enables a smarter vehicle network that is safer and more sustainable with quick, accurate alerts. Full article
(This article belongs to the Proceedings of The 5th International Electronic Conference on Applied Sciences)
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19 pages, 5279 KiB  
Article
Drone Noise Reduction Using Serration–Finlet Blade Design and Its Psychoacoustic and Social Impacts
by Yingyin Shen, Yuanqing Bai, Xiao Liu and Bin Zang
Sustainability 2025, 17(8), 3451; https://doi.org/10.3390/su17083451 - 12 Apr 2025
Viewed by 1514
Abstract
Unmanned aerial vehicles, particularly drones, have been increasingly deployed for different tasks in the community. They have become an important part of the economic and social benefits that society is exploiting from modern technology development. However, efforts are still required to further develop [...] Read more.
Unmanned aerial vehicles, particularly drones, have been increasingly deployed for different tasks in the community. They have become an important part of the economic and social benefits that society is exploiting from modern technology development. However, efforts are still required to further develop technologies which can mitigate the negative impacts. Among them, drone noise is considered a major health concern for the community. The present study undertakes an experimental investigation of the effectiveness of blade modifications on drone noise in an aeroacoustic wind tunnel facility. A quadcopter drone is programmed to operate in both hover and forward flights. Three modified blade configurations, including trailing-edge serrations combined serration–finlets, and an unmodified (baseline) blade, are manufactured. The far-field noise signals are recorded by two polar microphone arrays to quantify both the magnitude and directivity. The results show that all modified blades are able to reduce the drone noise at mid-to-high frequencies in both hover and forward flights, and this leads to a noticeable reduction in the overall sound pressure level. More importantly, the combined serration–finlet configuration outperforms all the other blades. Psychoacoustic analysis is also performed using the far-field acoustic time series. Interestingly, only the serration–finlet combination demonstrates a consistent reduction in the psychoacoustic annoyance levels, suggesting that it is important to use metrics from both acoustic and psychoacoustic analysis when developing noise mitigation strategies in the socio-economic context. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
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25 pages, 1931 KiB  
Article
Geometric Path Planning and Synchronization for Multiple Vehicles
by Hongjun Yu and Lanyong Zhang
Robotics 2025, 14(4), 47; https://doi.org/10.3390/robotics14040047 - 11 Apr 2025
Viewed by 482
Abstract
In known environments, vehicles plan paths to the target and take precautions to minimize risks. Due to limited dynamics, bounded turning radii, and unfavorable initial conditions, they may be momentarily exposed to threats. In this study, we propose multi-objective real-time optimization based on [...] Read more.
In known environments, vehicles plan paths to the target and take precautions to minimize risks. Due to limited dynamics, bounded turning radii, and unfavorable initial conditions, they may be momentarily exposed to threats. In this study, we propose multi-objective real-time optimization based on Dubins paths for multiple vehicles. They synchronize target arrival by reasonably changing speeds and selecting paths of similar lengths. The closer the threats are to the robots and the target, the more path options are available. Risk is reduced in path planning by minimizing the duration of exposure to threats. Vehicles strike a balance between exposure to threats and travel time to targets. We use a probability-based approach to reduce the computation burden and select satisfactory paths such that vehicles synchronize target arrival reasonably far away from threats. The performances of the proposed methods are verified in several simulation examples. Full article
(This article belongs to the Topic New Trends in Robotics: Automation and Autonomous Systems)
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