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Keywords = road sign comprehension

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15 pages, 4592 KiB  
Article
SSAM_YOLOv5: YOLOv5 Enhancement for Real-Time Detection of Small Road Signs
by Fatima Qanouni, Hakim El Massari, Noreddine Gherabi and Maria El-Badaoui
Digital 2025, 5(3), 30; https://doi.org/10.3390/digital5030030 - 29 Jul 2025
Viewed by 354
Abstract
Many traffic-sign detection systems are available to assist drivers with particular conditions such as small and distant signs, multiple signs on the road, objects similar to signs, and other challenging conditions. Real-time object detection is an indispensable aspect of these detection systems, with [...] Read more.
Many traffic-sign detection systems are available to assist drivers with particular conditions such as small and distant signs, multiple signs on the road, objects similar to signs, and other challenging conditions. Real-time object detection is an indispensable aspect of these detection systems, with detection speed and efficiency being critical parameters. In terms of these parameters, to enhance performance in road-sign detection under diverse conditions, we proposed a comprehensive methodology, SSAM_YOLOv5, to handle feature extraction and small-road-sign detection performance. The method was based on a modified version of YOLOv5s. First, we introduced attention modules into the backbone to focus on the region of interest within video frames; secondly, we replaced the activation function with the SwishT_C activation function to enhance feature extraction and achieve a balance between inference, precision, and mean average precision (mAP@50) rates. Compared to the YOLOv5 baseline, the proposed improvements achieved remarkable increases of 1.4% and 1.9% in mAP@50 on the Tiny LISA and GTSDB datasets, respectively, confirming their effectiveness. Full article
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31 pages, 1306 KiB  
Article
Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
by Jin Ran, Meiling Li, Jian Rong, Ding Zhao, Ahmetjan Kadir and Qiang Luo
Appl. Sci. 2025, 15(9), 4949; https://doi.org/10.3390/app15094949 - 29 Apr 2025
Viewed by 407
Abstract
With the rapid expansion of highway networks, the demand for timely and reliable road information has steadily increased. However, some guide signs on newly built or extended highways in China have not been updated or adjusted in time, resulting in incomplete information and [...] Read more.
With the rapid expansion of highway networks, the demand for timely and reliable road information has steadily increased. However, some guide signs on newly built or extended highways in China have not been updated or adjusted in time, resulting in incomplete information and non-standard setups. These issues not only affect drivers’ navigation experience but may also negatively impact road safety and traffic efficiency. Therefore, it is crucial to establish a scientifically sound evaluation system and a comprehensive assessment model for highway guide signs. This study selected a representative highway (G2 Expressway in China) as the research subject and combined questionnaire surveys with field investigations to identify common issues such as vague information and irregular placement of guide signs. Through an in-depth analysis of travel demand, the core requirements of drivers were summarized as safety, efficiency, and comfort. Based on these insights, the study proposes four key design principles for guide signs: standardization, readability, continuity, and consistency. A set of quantifiable evaluation indicators was developed through a comprehensive analysis of key factors affecting signage performance, and factor analysis was applied to verify the independence and rationality of the indicators. On this basis, an evaluation model was constructed using the technique for order preference by similarity to ideal solution (TOPSIS) to scientifically quantify the effectiveness of guide signs. The model was applied in a field study on the Hebei section of the G2 Expressway in China (with comprehensive traffic sign coverage, high traffic volume, and more traffic sign issues), with results demonstrating the feasibility and practicality of the proposed evaluation system and model. This research offers a systematic solution to enhance the service quality of highway guide signs and provides essential references for future highway planning and management practices. It aims to comprehensively improve drivers’ travel experiences and promote the development of sustainable and intelligent transportation networks, offering valuable insights for building integrated urban systems. Full article
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27 pages, 15440 KiB  
Article
Dynamic Performance of a Steel Road Sign with Multi-Material Electronic Signboard Under Mining-Induced Tremors from Different Mining Areas: Experimental and Numerical Research
by Paweł Boroń and Joanna Maria Dulińska
Materials 2025, 18(7), 1451; https://doi.org/10.3390/ma18071451 - 25 Mar 2025
Viewed by 412
Abstract
This study investigates the dynamic performance of a road sign equipped with a multi-material electronic signboard subjected to mining-induced seismic tremors. The key innovative aspect lies in providing new insights into the dynamic performance of multi-material electronic signboards under high-energy mining tremors, enhancing [...] Read more.
This study investigates the dynamic performance of a road sign equipped with a multi-material electronic signboard subjected to mining-induced seismic tremors. The key innovative aspect lies in providing new insights into the dynamic performance of multi-material electronic signboards under high-energy mining tremors, enhancing their safety assessment in mining areas. Experimental modal analysis and finite element analysis were conducted, and the numerical model of the sign was calibrated by adjusting ground stiffness to align experimental and computational data. The fundamental natural frequencies and their corresponding mode shapes were identified as 2.75 Hz, 3.09 Hz, 8.46 Hz, and 13.50 Hz. Numerical results were validated using MAC methods, demonstrating strong agreement with experimental values and confirming the accuracy of the numerical predictions. Damping ratios of 3.79% and 3.71% for the first and second modes, respectively, were measured via hammer tests. To evaluate the sign’s dynamic performance under high-energy mining-induced tremors, two events were applied as kinematic excitation of the structure. These tremors, recorded in different mining regions, exhibited significant variations in peak ground acceleration (PGA) and dominant frequency range. A key finding was that frequency matching between the dominant frequencies of the tremor and the natural frequencies of the sign had a greater impact on the sign’s dynamic response than PGA. The Szombierki tremor, with dominant frequencies of 1.6–4.8 Hz, induced significantly higher stress and displacement compared to the Moskorzyn tremor (5–10 Hz) despite the latter having twice the PGA. These results highlight that a road sign structure can exhibit widely varying dynamic behaviors depending on the seismic characteristics of the mining zone. Therefore, a comprehensive assessment of mining-induced tremors in relation to the seismicity of specific areas is crucial for understanding their potential impact on such structures. The dynamic performance assessment also revealed that the electronic multi-material signboard did not undergo plastic deformation, confirming it as a safe material solution for use in mining areas. Full article
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40 pages, 11010 KiB  
Review
PRISMA Review: Drones and AI in Inventory Creation of Signage
by Geovanny Satama-Bermeo, Jose Manuel Lopez-Guede, Javad Rahebi, Daniel Teso-Fz-Betoño, Ana Boyano and Ortzi Akizu-Gardoki
Drones 2025, 9(3), 221; https://doi.org/10.3390/drones9030221 - 19 Mar 2025
Viewed by 998
Abstract
This systematic review explores the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) in automating road signage inventory creation, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to analyze recent advancements. The study evaluates cutting-edge technologies, including [...] Read more.
This systematic review explores the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) in automating road signage inventory creation, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to analyze recent advancements. The study evaluates cutting-edge technologies, including UAVs equipped with deep learning algorithms and advanced sensors like light detection and ranging (LiDAR) and multispectral cameras, highlighting their roles in enhancing traffic sign detection and classification. Key challenges include detecting minor or partially obscured signs and adapting to diverse environmental conditions. The findings reveal significant progress in automation, with notable improvements in accuracy, efficiency, and real-time processing capabilities. However, limitations such as computational demands and environmental variability persist. By providing a comprehensive synthesis of current methodologies and performance metrics, this review establishes a robust foundation for future research to advance automated road infrastructure management to improve safety and operational efficiency in urban and rural settings. Full article
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28 pages, 68080 KiB  
Article
KRID: A Large-Scale Nationwide Korean Road Infrastructure Dataset for Comprehensive Road Facility Recognition
by Hyeongbok Kim, Eunbi Kim, Sanghoon Ahn, Beomjin Kim, Sung Jin Kim, Tae Kyung Sung, Lingling Zhao, Xiaohong Su and Gilmu Dong
Data 2025, 10(3), 36; https://doi.org/10.3390/data10030036 - 14 Mar 2025
Cited by 1 | Viewed by 1364
Abstract
Comprehensive datasets are crucial for developing advanced AI solutions in road infrastructure, yet most existing resources focus narrowly on vehicles or a limited set of object categories. To address this gap, we introduce the Korean Road Infrastructure Dataset (KRID), a large-scale dataset designed [...] Read more.
Comprehensive datasets are crucial for developing advanced AI solutions in road infrastructure, yet most existing resources focus narrowly on vehicles or a limited set of object categories. To address this gap, we introduce the Korean Road Infrastructure Dataset (KRID), a large-scale dataset designed for real-world road maintenance and safety applications. Our dataset covers highways, national roads, and local roads in both city and non-city areas, comprising 34 distinct types of road infrastructure—from common elements (e.g., traffic signals, gaze-directed poles) to specialized structures (e.g., tunnels, guardrails). Each instance is annotated with either bounding boxes or polygon segmentation masks under stringent quality control and privacy protocols. To demonstrate the utility of this resource, we conducted object detection and segmentation experiments using YOLO-based models, focusing on guardrail damage detection and traffic sign recognition. Preliminary results confirm its suitability for complex, safety-critical scenarios in intelligent transportation systems. Our main contributions include: (1) a broader range of infrastructure classes than conventional “driving perception” datasets, (2) high-resolution, privacy-compliant annotations across diverse road conditions, and (3) open-access availability through AI Hub and GitHub. By highlighting critical yet often overlooked infrastructure elements, this dataset paves the way for AI-driven maintenance workflows, hazard detection, and further innovations in road safety. Full article
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23 pages, 2861 KiB  
Article
Harnessing Generative AI for Text Analysis of California Autonomous Vehicle Crashes OL316 (2014–2024)
by Mohammad El-Yabroudi, Sri Harsha Pothuguntla, Athar Ghadi and Balakumar Muniandi
Electronics 2025, 14(4), 651; https://doi.org/10.3390/electronics14040651 - 8 Feb 2025
Viewed by 1160
Abstract
Autonomous vehicles (AVs) are expected to eventually replace traditional vehicles that require human drivers. In recent years, several AV manufacturers have begun on-road testing to validate the safety of these vehicles. California is one of the few states to permit such testing, regulating [...] Read more.
Autonomous vehicles (AVs) are expected to eventually replace traditional vehicles that require human drivers. In recent years, several AV manufacturers have begun on-road testing to validate the safety of these vehicles. California is one of the few states to permit such testing, regulating it through a permit system. To ensure transparency and public awareness, the state mandates that any licensed AV manufacturer conducting on-road tests report crashes involving AVs. This must be conducted using a standardized format known as OL316, a requirement that has been in place since late 2014. While previous research has explored AV crash data, most studies have focused on specific timeframes without covering the entire period since 2014. Moreover, converting the data from PDFs to machine-readable formats has often been a manual process, and the description text field in reports has rarely been fully analyzed. This article presents a comprehensive, machine-readable dataset of AV crashes from 2014 to September 2024, along with publicly available parsing code to streamline future data analysis. Additionally, we provide an updated statistical analysis of AV crashes during this period. Furthermore, we leverage Generative AI (GenAI) to analyze the description text field of the OL316 reports. This analysis identifies common crash scenarios, contributing factors, and additional insights into moderate and major incidents. The final dataset comprises 728 crash entries. Notably, only 2% of the crashes were categorized as major, while 14% were classified as moderate. Furthermore, 43% of the crashes occurred while the AV was stationary, whereas 55% took place while the AV was in motion. Our GenAI analysis indicates that, in many instances, human drivers of non-autonomous vehicles were at fault. Common causes include rear-end collisions due to insufficient following distances, traffic violations such as running red lights or stop signs, and reckless behaviors like lane boundary violations or speeding. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Vehicles)
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13 pages, 4025 KiB  
Article
The Effects of Temporary Portable Rumble Strips on Vehicle Speeds in Road Work Zones
by Ahmed Jalil Al-Bayati, Mason Ali, Fadi Alhomaidat, Nishantha Bandara and Yuting Chen
Safety 2024, 10(4), 105; https://doi.org/10.3390/safety10040105 - 16 Dec 2024
Cited by 1 | Viewed by 2459
Abstract
The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the [...] Read more.
The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types. Full article
(This article belongs to the Special Issue Safety Performance Assessment and Management in Construction)
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17 pages, 807 KiB  
Systematic Review
Variable Message Signs in Traffic Management: A Systematic Review of User Behavior and Future Innovations
by Paula Lagoa, Teresa Galvão and Marta Campos Ferreira
Infrastructures 2024, 9(10), 184; https://doi.org/10.3390/infrastructures9100184 - 12 Oct 2024
Cited by 4 | Viewed by 2351
Abstract
Effective traffic management is crucial in addressing the growing complexities of urban mobility, and variable message signs (VMSs) play a vital role in delivering real-time information to road users. Despite their widespread application, there is limited comprehensive understanding of how VMS influence user [...] Read more.
Effective traffic management is crucial in addressing the growing complexities of urban mobility, and variable message signs (VMSs) play a vital role in delivering real-time information to road users. Despite their widespread application, there is limited comprehensive understanding of how VMS influence user behavior and optimize traffic flow. This systematic literature review aims to address this gap by examining the effectiveness of VMS in shaping user interactions and enhancing traffic management systems. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a thorough analysis of relevant studies was conducted to identify key factors influencing VMS impact, including message content and characteristics, complementary sources of information, user demographics, VMS location, and users’ reliance on these signs. Additionally, the review explores the implications of displaying non-critical information on VMS and introduces virtual dynamic message signs (VDMSs) as an innovative approach for delivering public traveler information. The study identifies several research gaps, such as the integration of VMS with vehicle-to-everything (V2X) technologies, navigation systems, the need for validation in real-world scenarios, and understanding behavioral responses to non-critical information on VMS. This review highlights the importance of optimizing VMS for improved user engagement and traffic management, providing valuable insights and directions for future research in this evolving field. Full article
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23 pages, 18087 KiB  
Article
Evaluating the Impact of Elementary School Urban Neighborhood Color on Children’s Mentalization of Emotions through Multi-Source Data
by Ruiying Zhang, Qian Huang, Zhimou Peng, Xinyue Zhang, Lan Shang and Chengling Yang
Buildings 2024, 14(10), 3128; https://doi.org/10.3390/buildings14103128 - 30 Sep 2024
Cited by 2 | Viewed by 1598
Abstract
To address the challenge of quantitatively assessing the mentalization of emotions in color design schemes, this study uses Baidu Street View images and deep learning, integrates multi-source data, and innovatively constructs a color data model based on a comprehensive color indicator system for [...] Read more.
To address the challenge of quantitatively assessing the mentalization of emotions in color design schemes, this study uses Baidu Street View images and deep learning, integrates multi-source data, and innovatively constructs a color data model based on a comprehensive color indicator system for the quantitative assessment and visual representation of how the color environments of elementary school urban neighborhoods impact children’s mentalization of emotions. This model systematically incorporates physical color indicators, integrates elements such as perceptual frequency, and provides a novel perspective for color planning. The study’s results reveal that color metrics significantly impact children’s mentalization of emotions across multiple dimensions, with gender and age emerging as important influencing factors. Additionally, significant correlations were found between color and environmental elements such as building façades, roads, and signs. The study provides urban planners and architects with a practical color data model and recommendations for the revitalization of elementary school urban neighborhoods, offering a scientific basis for optimizing color design. Full article
(This article belongs to the Special Issue Art and Design for Healing and Wellness in the Built Environment)
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29 pages, 9366 KiB  
Article
Multimodal Driver Condition Monitoring System Operating in the Far-Infrared Spectrum
by Mateusz Knapik, Bogusław Cyganek and Tomasz Balon
Electronics 2024, 13(17), 3502; https://doi.org/10.3390/electronics13173502 - 3 Sep 2024
Cited by 3 | Viewed by 2043
Abstract
Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in [...] Read more.
Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver’s condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver’s state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver’s state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system’s explicit training. Experimental evaluations demonstrate the system’s high accuracy in detecting and responding to signs of driver fatigue and distraction. Full article
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28 pages, 35864 KiB  
Article
Custom Anchorless Object Detection Model for 3D Synthetic Traffic Sign Board Dataset with Depth Estimation and Text Character Extraction
by Rahul Soans and Yohei Fukumizu
Appl. Sci. 2024, 14(14), 6352; https://doi.org/10.3390/app14146352 - 21 Jul 2024
Cited by 1 | Viewed by 2093
Abstract
This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic [...] Read more.
This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions. Full article
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22 pages, 97889 KiB  
Article
Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users
by Anton Smoliński, Paweł Forczmański and Adam Nowosielski
Electronics 2024, 13(13), 2457; https://doi.org/10.3390/electronics13132457 - 23 Jun 2024
Cited by 4 | Viewed by 1503
Abstract
This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify [...] Read more.
This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify driver drowsiness and distraction signs. Our novel contribution includes utilizing state-of-the-art convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks for effective feature extraction and classification across diverse distraction scenarios. Additionally, we explore various data fusion techniques, demonstrating their impact on improving detection accuracy. The significance of this work lies in its potential to enhance road safety by providing more reliable and efficient tools for the real-time monitoring of driver attentiveness, thereby reducing the risk of accidents caused by distraction and fatigue. The proposed methods are thoroughly evaluated using a multimodal benchmark dataset, with results showing their substantial capabilities leading to the development of safety-enhancing technologies for vehicular environments. The primary challenge addressed in this study is the detection of driver states not relying on the lighting conditions. Our solution employs multimodal data integration, encompassing RGB, thermal, and depth images, to ensure robust and accurate monitoring regardless of external lighting variations Full article
(This article belongs to the Special Issue Advancement on Smart Vehicles and Smart Travel)
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32 pages, 11269 KiB  
Article
Improving Autonomous Vehicle Perception through Evaluating LiDAR Capabilities and Handheld Retroreflectivity Assessments
by Ziyad N. Aldoski and Csaba Koren
Sensors 2024, 24(11), 3304; https://doi.org/10.3390/s24113304 - 22 May 2024
Cited by 4 | Viewed by 1489
Abstract
Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a need for ongoing advancements in traffic sign evaluation methodologies. This paper comprehensively analyzes the relationship [...] Read more.
Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a need for ongoing advancements in traffic sign evaluation methodologies. This paper comprehensively analyzes the relationship between traffic sign retroreflectivity and LiDAR intensity to enhance visibility and communication on road networks. Using Python 3.10 programming and statistical techniques, we thoroughly analyzed handheld retroreflectivity coefficients alongside LiDAR intensity data from two LiDAR configurations: 2LRLiDAR and 1CLiDAR systems. The study focused specifically on RA1 and RA2 traffic sign classes, exploring correlations between retroreflectivity and intensity and identifying factors that may impact their performance. Our findings reveal variations in retroreflectivity compliance rates among different sign categories and color compositions, emphasizing the necessity for targeted interventions in sign design and production processes. Additionally, we observed distinct patterns in LiDAR intensity distributions, indicating the potential of LiDAR technology for assessing sign visibility. However, the limited correlations between retroreflectivity and LiDAR intensity underscore the need for further investigation and standardization efforts. This study provides valuable insights into optimizing traffic sign effectiveness, ultimately contributing to improved road safety conditions. Full article
(This article belongs to the Section Radar Sensors)
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23 pages, 5802 KiB  
Article
Assessing the Legibility of Arabic Road Signage Using Eye Gazing and Cognitive Loading Metrics
by Mohammad Lataifeh, Naveed Ahmed, Shaima Elbardawil and Somayeh Gordani
Computers 2024, 13(5), 123; https://doi.org/10.3390/computers13050123 - 15 May 2024
Cited by 1 | Viewed by 2331
Abstract
This research study aimed to evaluate the legibility of Arabic road signage using an eye-tracking approach within a virtual reality (VR) environment. The study was conducted in a controlled setting involving 20 participants who watched two videos using the HP Omnicept Reverb G2. [...] Read more.
This research study aimed to evaluate the legibility of Arabic road signage using an eye-tracking approach within a virtual reality (VR) environment. The study was conducted in a controlled setting involving 20 participants who watched two videos using the HP Omnicept Reverb G2. The VR device recorded eye gazing details in addition to other physiological data of the participants, providing an overlay of heart rate, eye movement, and cognitive load, which in combination were used to determine the participants’ focus during the experiment. The data were processed through a schematic design, and the final files were saved in .txt format, which was later used for data extraction and analysis. Through the execution of this study, it became apparent that employing eye-tracking technology within a VR setting offers a promising method for assessing the legibility of road signs. The outcomes of the current research enlightened the vital role of legibility in ensuring road safety and facilitating effective communication with drivers. Clear and easily comprehensible road signs were found to be pivotal in delivering timely information, aiding navigation, and ultimately mitigating accidents or confusion on the road. As a result, this study advocates for the utilization of VR as a valuable platform for enhancing the design and functionality of road signage systems, recognizing its potential to contribute significantly to the improvement of road safety and navigation for drivers. Full article
(This article belongs to the Special Issue Extended or Mixed Reality (AR + VR): Technology and Applications)
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10 pages, 3993 KiB  
Article
How to Treat a Cyclist’s Nodule?—Introduction of a Novel, ICG-Assisted Approach
by Julius M. Mayer, Sophie I. Spies, Carla K. Mayer, Cédric Zubler, Rafael Loucas and Thomas Holzbach
J. Clin. Med. 2024, 13(4), 1124; https://doi.org/10.3390/jcm13041124 - 16 Feb 2024
Cited by 1 | Viewed by 4178
Abstract
Background: Perineal nodular induration (PNI) is a benign proliferation of the soft tissue in the perineal region that is associated with saddle sports, especially road cycling. The etiology has not been conclusively clarified; however, repeated microtrauma to the collagen and subcutaneous fat tissue [...] Read more.
Background: Perineal nodular induration (PNI) is a benign proliferation of the soft tissue in the perineal region that is associated with saddle sports, especially road cycling. The etiology has not been conclusively clarified; however, repeated microtrauma to the collagen and subcutaneous fat tissue by pressure, vibration and shear forces is considered a mechanical pathomechanism. In this context, chronic lymphedema resulting in the development of fibrous tissue has been suggested as an etiological pathway of PNI. The primary aim of this study was to introduce and elucidate a novel operative technique regarding PNI that is assisted by indocyanine green (ICG). In order to provide some context for this approach, we conducted a comprehensive review of the existing literature. This dual objective aimed to contribute to the existing body of knowledge while introducing an innovative surgical approach for managing PNI. Methods: We reviewed publications relating to PNI published between 1990 and 2023. In addition to the thorough review of the literature, we presented our novel surgical approach. We described how this elaborate approach for extensive cases of PNI involves surgical excision combined with tissue doubling and intraoperative ICG visualization for exact lymphatic vessel obliteration to minimize the risk of recurrence based on the presumed context of lymphatic congestion. Results: The literature research yielded 16 PubMed articles encompassing 23 cases of perineal nodular induration (PNI) or cyclist’s nodule. Of these, 9 cases involved females, and 14 involved males. Conservative treatment was documented in 7 cases (30%), while surgical approaches were reported in 16 cases (70%). Notably, a limited number of articles focused on histopathological or radiological characteristics, with a shortage of structured reviews on surgical treatment options. Only two articles provided detailed insights into surgical techniques. Similarly to the two cases of surgical intervention identified in the literature research, the post-operative recovery in our ICG assisted surgical approach was prompt, meaning a return to cycling was possible six weeks after surgery. At the end of the observation period (twelve months after surgery), regular scar formation and no signs of recurrence were seen. Conclusion: We hope that this article draws attention to the condition of PNI in times of increasing popularity of cycling as a sport. We aimed to contribute to the existing body of knowledge through our thorough review of the existing literature while introducing an innovative surgical approach for managing PNI. Due to the successful outcome, the combination of tissue doubling, intraoperative ICG visualization and postoperative negative wound therapy should be considered as a therapeutic strategy in cases of large PNI. Full article
(This article belongs to the Special Issue Advancements in Individualized Plastic and Reconstructive Surgery)
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