Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2025.3645071
Ben Rahman;Maryani;Thoyyibah T.
IDChat (Internet-Dependent Cryptographic Hybrid Authentication Technology) is a universal digital identity framework designed to eliminate costly and vulnerable SMS-based one-time passwords (OTPs). It fuses civil identity (NIK), multi-biometric modalities (fingerprint and retina), and genomic-derived entropy to reinforce cryptographic key generation under a unified fusion engine. Targeting Indonesia’s 120 million internet users—90% of whom still rely on paid SMS OTPs—IDChat introduces a privacy-preserving Digital Genetic Signature (DGS) generated via SHA-256 hashing and homomorphic encryption (BFV scheme), enhancing resistance to spoofing and brute-force attacks. Experimental validation using OpenCV with FVC2004 and CASIA-Iris datasets achieved 99.1% authentication accuracy, a false acceptance rate (FAR) of 0.008%, and an average latency of 1.9 seconds, demonstrating competitive efficiency against existing biometric systems. A comparative cost analysis indicates potential national savings of Rp 12–50 trillion annually by replacing SMS OTPs with free Wi-Fi–based verification. Unlike centralized frameworks such as Aadhaar or FIDO2, IDChat performs local (offline) verification within closed Wi-Fi environments through cached encrypted templates, ensuring independence from cellular networks. DNA information functions as a static entropy factor enrolled once during registration, avoiding any real-time biological sampling. This study presents the first technically validated multi-biometric and DNA-derived cryptographic fusion model optimized for secure, inclusive, and cost-efficient digital authentication in resource-constrained environments.
{"title":"IDChat: Toward a Universal, Multi-Biometric Digital Identity for Next-Generation Secure Communication in ASEAN","authors":"Ben Rahman;Maryani;Thoyyibah T.","doi":"10.1109/ACCESS.2025.3645071","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3645071","url":null,"abstract":"IDChat (Internet-Dependent Cryptographic Hybrid Authentication Technology) is a universal digital identity framework designed to eliminate costly and vulnerable SMS-based one-time passwords (OTPs). It fuses civil identity (NIK), multi-biometric modalities (fingerprint and retina), and genomic-derived entropy to reinforce cryptographic key generation under a unified fusion engine. Targeting Indonesia’s 120 million internet users—90% of whom still rely on paid SMS OTPs—IDChat introduces a privacy-preserving Digital Genetic Signature (DGS) generated via SHA-256 hashing and homomorphic encryption (BFV scheme), enhancing resistance to spoofing and brute-force attacks. Experimental validation using OpenCV with FVC2004 and CASIA-Iris datasets achieved 99.1% authentication accuracy, a false acceptance rate (FAR) of 0.008%, and an average latency of 1.9 seconds, demonstrating competitive efficiency against existing biometric systems. A comparative cost analysis indicates potential national savings of Rp 12–50 trillion annually by replacing SMS OTPs with free Wi-Fi–based verification. Unlike centralized frameworks such as Aadhaar or FIDO2, IDChat performs local (offline) verification within closed Wi-Fi environments through cached encrypted templates, ensuring independence from cellular networks. DNA information functions as a static entropy factor enrolled once during registration, avoiding any real-time biological sampling. This study presents the first technically validated multi-biometric and DNA-derived cryptographic fusion model optimized for secure, inclusive, and cost-efficient digital authentication in resource-constrained environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"14892-14902"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11343738","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2026.3651330
Haolin Yang;Shengtian Zhang;Incheol Shin
In the development of unmanned agricultural machinery, efficiently and accurately detecting field obstacles is crucial for ensuring both operational safety and efficiency. However, due to the complex agricultural environments, existing obstacle detection methods still suffer from low detection accuracy and large model parameters. To address these issues, this study presents CFD-YOLOv8, an application-oriented adaptation of YOLOv8 tailored to complex farmland environments that integrates complementary architectural modules and a practical IoU-based loss to improve detection performance. First, we design a Task-Aligned Dynamic Detection Head to improve the model’s adaptability to challenging environments while achieving lightweight optimization. Second, we incorporate RFCAConv into the C2f module to expand the receptive field and strengthen the model’s focus on crucial target regions. Finally, we introduce the Powerful-IoU loss function to optimize bounding box handling, thereby accelerating convergence and enhancing localization accuracy. Experiments conducted on our custom-built field obstacle dataset demonstrated that CFD-YOLOv8 improves average detection precision by 1.9%, with precision and recall rates increasing by 3% and 0.2%, respectively, while reducing model parameters by 18.9%. These results significantly outperform current mainstream obstacle detection methods. The findings of this study offer robust technical support for autonomous obstacle avoidance and path planning in unmanned agricultural machinery operating in complex environments, laying a foundation for the further advancement of agricultural mechanization and intelligence.
{"title":"CFD-YOLOv8: A Complex Farmland Obstacle Detection Method Based on Task-Aligned Detection Head and Receptive Field Attention","authors":"Haolin Yang;Shengtian Zhang;Incheol Shin","doi":"10.1109/ACCESS.2026.3651330","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3651330","url":null,"abstract":"In the development of unmanned agricultural machinery, efficiently and accurately detecting field obstacles is crucial for ensuring both operational safety and efficiency. However, due to the complex agricultural environments, existing obstacle detection methods still suffer from low detection accuracy and large model parameters. To address these issues, this study presents CFD-YOLOv8, an application-oriented adaptation of YOLOv8 tailored to complex farmland environments that integrates complementary architectural modules and a practical IoU-based loss to improve detection performance. First, we design a Task-Aligned Dynamic Detection Head to improve the model’s adaptability to challenging environments while achieving lightweight optimization. Second, we incorporate RFCAConv into the C2f module to expand the receptive field and strengthen the model’s focus on crucial target regions. Finally, we introduce the Powerful-IoU loss function to optimize bounding box handling, thereby accelerating convergence and enhancing localization accuracy. Experiments conducted on our custom-built field obstacle dataset demonstrated that CFD-YOLOv8 improves average detection precision by 1.9%, with precision and recall rates increasing by 3% and 0.2%, respectively, while reducing model parameters by 18.9%. These results significantly outperform current mainstream obstacle detection methods. The findings of this study offer robust technical support for autonomous obstacle avoidance and path planning in unmanned agricultural machinery operating in complex environments, laying a foundation for the further advancement of agricultural mechanization and intelligence.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"14903-14915"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11334032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vehicles equipped with Automated Driving Systems (ADS) are eventually making their first steps into the market as of the recent regulations enabling both SAE J3016 L3 and L4 to be operated on public roads in a limited Operation Design Domain (ODD). The gradual adoption of ADS featured vehicles poses questions concerning the feasibility of traditional safety validation approaches to generate sufficient safety evidence to cover the operational space of such technologies. One method that is suggested in the recent UNECE Regulation 157 for the Automated Lane Keeping System (ALKS) is to use safety models to establish a performance benchmark for the ADS to securely address traffic scenarios potentially leading to rear-end collisions. However, such models only encompass the braking action as a means to reduce the collision risk in case of critical scenarios whereas evasive lateral maneuvers are not taken into consideration. Building upon this research gap, the present manuscript studies the effectiveness of evasive lane-change maneuvers in addressing UN-R157 safety-critical scenarios by extending one of the proposed safety reference models to accommodate for the steering action. Overall, the introduction of evasive lane-change into safety reference models results in more ambitious benchmarks, especially for rear-end potential collisions with a large speed difference.
{"title":"Investigating Accident Preventability via Evasive Lane-Change Maneuvers, a Candidate Safety Reference Model","authors":"Riccardo Donà;Konstantinos Mattas;Sándor Vass;Biagio Ciuffo","doi":"10.1109/ACCESS.2026.3652820","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3652820","url":null,"abstract":"Vehicles equipped with Automated Driving Systems (ADS) are eventually making their first steps into the market as of the recent regulations enabling both SAE J3016 L3 and L4 to be operated on public roads in a limited Operation Design Domain (ODD). The gradual adoption of ADS featured vehicles poses questions concerning the feasibility of traditional safety validation approaches to generate sufficient safety evidence to cover the operational space of such technologies. One method that is suggested in the recent UNECE Regulation 157 for the Automated Lane Keeping System (ALKS) is to use safety models to establish a performance benchmark for the ADS to securely address traffic scenarios potentially leading to rear-end collisions. However, such models only encompass the braking action as a means to reduce the collision risk in case of critical scenarios whereas evasive lateral maneuvers are not taken into consideration. Building upon this research gap, the present manuscript studies the effectiveness of evasive lane-change maneuvers in addressing UN-R157 safety-critical scenarios by extending one of the proposed safety reference models to accommodate for the steering action. Overall, the introduction of evasive lane-change into safety reference models results in more ambitious benchmarks, especially for rear-end potential collisions with a large speed difference.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"7669-7680"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2026.3651768
Yen-Li Lai;Wei-Shun Yu;Pei-Chun Lin
Staircases represent one of the most challenging terrains commonly encountered in urban environments. This paper presents a comprehensive stair-climbing strategy for a leg–wheel transformable robot that exploits the varying contact points of its leg–wheel mechanism. The proposed strategy integrates workspace analysis, stability adjustment through center of mass shifting and rolling along stair edges, foothold planning under mechanical constraints, swing trajectory design to reduce slippage, impact, and moment of inertia, as well as gait transition from flat ground to stair climbing. Furthermore, the applicable range of stair dimensions is analyzed. A vision-based method is further developed to estimate stair dimensions in real time from depth images, enabling the robot to autonomously generate climbing behaviors. The proposed strategy is validated through a series of indoor and outdoor experiments on staircases with varying dimensions. Results show that the robot can accurately estimate stair depth and height, and successfully climb staircases with varying step sizes. Moreover, the robot closely follows the planned trajectories throughout the climbing process. These results demonstrate the effectiveness and robustness of the proposed approach, highlighting its potential for reliable stair climbing in complex real-world environments.
{"title":"Stair-Climbing Strategy of a Leg-Wheel Transformable Robot Using Visual Feedback and Varying Leg-Wheel Contact Points","authors":"Yen-Li Lai;Wei-Shun Yu;Pei-Chun Lin","doi":"10.1109/ACCESS.2026.3651768","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3651768","url":null,"abstract":"Staircases represent one of the most challenging terrains commonly encountered in urban environments. This paper presents a comprehensive stair-climbing strategy for a leg–wheel transformable robot that exploits the varying contact points of its leg–wheel mechanism. The proposed strategy integrates workspace analysis, stability adjustment through center of mass shifting and rolling along stair edges, foothold planning under mechanical constraints, swing trajectory design to reduce slippage, impact, and moment of inertia, as well as gait transition from flat ground to stair climbing. Furthermore, the applicable range of stair dimensions is analyzed. A vision-based method is further developed to estimate stair dimensions in real time from depth images, enabling the robot to autonomously generate climbing behaviors. The proposed strategy is validated through a series of indoor and outdoor experiments on staircases with varying dimensions. Results show that the robot can accurately estimate stair depth and height, and successfully climb staircases with varying step sizes. Moreover, the robot closely follows the planned trajectories throughout the climbing process. These results demonstrate the effectiveness and robustness of the proposed approach, highlighting its potential for reliable stair climbing in complex real-world environments.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"7631-7648"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11339443","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2026.3653003
Guo-Long Fu;Hong-Bing Zeng;Yong-Shui Liu
This paper examines the stability of linear time-varying delay systems, assuming that the delay varies periodically within a certain range. By dividing the delay function into monotonically increasing and decreasing intervals and introducing generalized looped functionals within each interval, a new delay-monotonicity-based Lyapunov functional is derived. According to this functional, the integral terms of its derivative are bounded using the third-order Bessel-Legendre inequality(BLI), leading to a sufficient condition that considers periodic delay monotonicity. A numerical example demonstrates that the method proposed in this paper significantly reduces conservatism.
{"title":"Stability Analysis of Systems With Periodic Delay Using a Generalized Looped Functional","authors":"Guo-Long Fu;Hong-Bing Zeng;Yong-Shui Liu","doi":"10.1109/ACCESS.2026.3653003","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3653003","url":null,"abstract":"This paper examines the stability of linear time-varying delay systems, assuming that the delay varies periodically within a certain range. By dividing the delay function into monotonically increasing and decreasing intervals and introducing generalized looped functionals within each interval, a new delay-monotonicity-based Lyapunov functional is derived. According to this functional, the integral terms of its derivative are bounded using the third-order Bessel-Legendre inequality(BLI), leading to a sufficient condition that considers periodic delay monotonicity. A numerical example demonstrates that the method proposed in this paper significantly reduces conservatism.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10883-10890"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11346508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2026.3652006
Muhammad Raheel Anwar;Shah Khalid;Saied Alshahrani;Hafiz Syed Muhammad Bilal;Mohammed Aldawsari
In recent years, there has been growing interest in the automatic generation of Multiple Choice Questions (MCQs), mainly to reduce the effort and time involved in manual question construction. Many researchers and practitioners have tried different methods to produce accurate and good-quality MCQs automatically. The progress in this area has been steady but also diverse, which makes it difficult to identify which approaches work best in practice. The research trend has moved gradually from early rule-based methods to more advanced systems based on Large Language Models (LLMs). Although several review papers and surveys have summarized earlier work, most of them give limited discussion on how the field has evolved over time or what key problems remain to be solved. This paper aims to fill that gap by reviewing important studies on MCQs generation and providing a structured overview of the main question types, their essential components, and the processes involved in automated generation. It also discusses the essential stages in a general system architecture, including preprocessing, key selection, and distractor generation. In addition, the review summarizes the most frequently used datasets and evaluation measures for assessing question quality and highlights important applications and research challenges. In doing so, it outlines possible future directions to advance the use of automated MCQs generation in education, training, and professional assessment.
{"title":"MCQs Generation With Large Language Models: A Survey of Methodologies, Evolution, and Open Research Issues","authors":"Muhammad Raheel Anwar;Shah Khalid;Saied Alshahrani;Hafiz Syed Muhammad Bilal;Mohammed Aldawsari","doi":"10.1109/ACCESS.2026.3652006","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3652006","url":null,"abstract":"In recent years, there has been growing interest in the automatic generation of Multiple Choice Questions (MCQs), mainly to reduce the effort and time involved in manual question construction. Many researchers and practitioners have tried different methods to produce accurate and good-quality MCQs automatically. The progress in this area has been steady but also diverse, which makes it difficult to identify which approaches work best in practice. The research trend has moved gradually from early rule-based methods to more advanced systems based on Large Language Models (LLMs). Although several review papers and surveys have summarized earlier work, most of them give limited discussion on how the field has evolved over time or what key problems remain to be solved. This paper aims to fill that gap by reviewing important studies on MCQs generation and providing a structured overview of the main question types, their essential components, and the processes involved in automated generation. It also discusses the essential stages in a general system architecture, including preprocessing, key selection, and distractor generation. In addition, the review summarizes the most frequently used datasets and evaluation measures for assessing question quality and highlights important applications and research challenges. In doing so, it outlines possible future directions to advance the use of automated MCQs generation in education, training, and professional assessment.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10991-11018"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11339510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smart Grids rely on extensive monitoring of operational data to ensure reliable and efficient energy distribution. Underground substations, widely used in dense urban areas, pose unique challenges due to high ambient temperatures, constrained spaces, and noisy or irregular data, which limit the effectiveness of conventional data-driven analytics. This study proposes and validates a hybrid Knowledge Discovery in Databases (KDD) framework specifically designed to analyze operational data from underground substations. Real-world data from 43 substations managed by CEEE Equatorial in Porto Alegre, Brazil, were preprocessed and analyzed using the Expectation-Maximization (EM) algorithm to uncover latent operational patterns and the Apriori algorithm to extract association rules that explain and validate these patterns. The framework integrates rigorous data cleaning, normalization, discretization, and interpretable clustering and rule-mining techniques implemented in R and WEKA. Experimental results identified meaningful clusters representing distinct operational regimes, including a critical anomalous cluster characterized by high temperature combined with low-voltage and low-current conditions. Association rules reinforced the interpretability of these clusters and highlighted operational anomalies with high confidence and support. The proposed framework demonstrates practical relevance for intelligent monitoring, anomaly detection, and decision support in Smart Grid systems. Future work includes temporal modeling, incorporation of external contextual data, and the use of hybrid or deep learning approaches to enhance model scalability and diagnostic accuracy.
{"title":"Development of a Hybrid Framework for Knowledge Discovery in Smart Grid Data From Underground Substations","authors":"Leonardo Minelli;Paulo Sérgio Sausen;Airam Teresa Zago Romcy Sausen;Renê Reinaldo Emmel Júnior","doi":"10.1109/ACCESS.2026.3652929","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3652929","url":null,"abstract":"Smart Grids rely on extensive monitoring of operational data to ensure reliable and efficient energy distribution. Underground substations, widely used in dense urban areas, pose unique challenges due to high ambient temperatures, constrained spaces, and noisy or irregular data, which limit the effectiveness of conventional data-driven analytics. This study proposes and validates a hybrid Knowledge Discovery in Databases (KDD) framework specifically designed to analyze operational data from underground substations. Real-world data from 43 substations managed by CEEE Equatorial in Porto Alegre, Brazil, were preprocessed and analyzed using the Expectation-Maximization (EM) algorithm to uncover latent operational patterns and the Apriori algorithm to extract association rules that explain and validate these patterns. The framework integrates rigorous data cleaning, normalization, discretization, and interpretable clustering and rule-mining techniques implemented in R and WEKA. Experimental results identified meaningful clusters representing distinct operational regimes, including a critical anomalous cluster characterized by high temperature combined with low-voltage and low-current conditions. Association rules reinforced the interpretability of these clusters and highlighted operational anomalies with high confidence and support. The proposed framework demonstrates practical relevance for intelligent monitoring, anomaly detection, and decision support in Smart Grid systems. Future work includes temporal modeling, incorporation of external contextual data, and the use of hybrid or deep learning approaches to enhance model scalability and diagnostic accuracy.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"10910-10922"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2026.3652842
Fehim Köylü
Traffic signs are one of the most critical components that regulate traffic on roads. They inform drivers about the priorities and restrictions on the road they are on. This study aims to provide a solution for detecting 37 frequently encountered traffic signs on Turkish highways through deep-learning-based YOLO models. Detecting signs using computer vision is challenging for classical methods due to environmental conditions, and deep-learning-based methods promise successful results. Worldwide general standards determine traffic signs, but each country’s specific differences also influence their design. We have prepared a new dataset, TraffiSign-Turk, to compare the trained models in this study. The dataset contains 25,978 different labeled objects across 10,561 distinct images. Besides traffic signs, it also includes labels for vehicles and pedestrians in the photos. Using YOLOv5 and YOLOv8, we achieved an acceptable level of successful object detection accuracy that operates at real time speed on the dataset. These findings have proven that YOLO-based models can be used to detect environmental objects necessary for autonomous driving. An online driver assistance system is developed based on trained models. We have introduced a comprehensive new dataset to the literature for autonomous vehicle studies.
{"title":"A Comparative Analysis of YOLO-Based Traffic Sign Detections With a Novel Turkish Traffic Sign Dataset","authors":"Fehim Köylü","doi":"10.1109/ACCESS.2026.3652842","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3652842","url":null,"abstract":"Traffic signs are one of the most critical components that regulate traffic on roads. They inform drivers about the priorities and restrictions on the road they are on. This study aims to provide a solution for detecting 37 frequently encountered traffic signs on Turkish highways through deep-learning-based YOLO models. Detecting signs using computer vision is challenging for classical methods due to environmental conditions, and deep-learning-based methods promise successful results. Worldwide general standards determine traffic signs, but each country’s specific differences also influence their design. We have prepared a new dataset, TraffiSign-Turk, to compare the trained models in this study. The dataset contains 25,978 different labeled objects across 10,561 distinct images. Besides traffic signs, it also includes labels for vehicles and pedestrians in the photos. Using YOLOv5 and YOLOv8, we achieved an acceptable level of successful object detection accuracy that operates at real time speed on the dataset. These findings have proven that YOLO-based models can be used to detect environmental objects necessary for autonomous driving. An online driver assistance system is developed based on trained models. We have introduced a comprehensive new dataset to the literature for autonomous vehicle studies.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"7744-7763"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2026.3653446
Arsha Ali;Jonathon M. Smereka;Kayla Riegner;Lionel P. Robert;Dawn M. Tilbury
Human-robot teaming can benefit many domains. Teams with sufficient team situation awareness may better accomplish their goals, but team situation awareness can be challenging to develop and maintain. We interpret team situation awareness as the team’s collective understanding of the whole situation at a given time. In order to determine how team situation awareness can be developed and maintained in a human-robot team, we conducted a between-subjects experiment to investigate how shared mental models and communication impact team situation awareness, and how team situation awareness relates to performance. Results from 48 subjects showed the impact of shared mental models is relative to communication. A high shared mental model improved team situation awareness and performance efficiency when there was little communication, while the level of shared mental model was inconsequential when high communication was provided. In addition, team situation awareness was positively related to performance efficiency. The findings indicate that team situation awareness can be achieved through either high communication or a high shared mental model under limited communication, which consequently allows for improved performance.
{"title":"Promoting Human–Robot Team Effectiveness: Shared Mental Models and Communication Improve Team Situation Awareness and Performance","authors":"Arsha Ali;Jonathon M. Smereka;Kayla Riegner;Lionel P. Robert;Dawn M. Tilbury","doi":"10.1109/ACCESS.2026.3653446","DOIUrl":"https://doi.org/10.1109/ACCESS.2026.3653446","url":null,"abstract":"Human-robot teaming can benefit many domains. Teams with sufficient team situation awareness may better accomplish their goals, but team situation awareness can be challenging to develop and maintain. We interpret team situation awareness as the team’s collective understanding of the whole situation at a given time. In order to determine how team situation awareness can be developed and maintained in a human-robot team, we conducted a between-subjects experiment to investigate how shared mental models and communication impact team situation awareness, and how team situation awareness relates to performance. Results from 48 subjects showed the impact of shared mental models is relative to communication. A high shared mental model improved team situation awareness and performance efficiency when there was little communication, while the level of shared mental model was inconsequential when high communication was provided. In addition, team situation awareness was positively related to performance efficiency. The findings indicate that team situation awareness can be achieved through either high communication or a high shared mental model under limited communication, which consequently allows for improved performance.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"14 ","pages":"7616-7630"},"PeriodicalIF":3.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11346936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/ACCESS.2026.3653110
Tymoteusz Zwierzchowski
This paper features a novel approach to modelling an inventory management system. The work takes into account a single product warehouse with multiple suppliers. Each supplier delivers its product with a certain lead time. Furthermore, the amount of product each supplier can deliver at a single time instant is limited by the supplier’s maximum order quantity. The system prioritizes faster suppliers (i.e. suppliers with shorter lead times), which can result in them delivering large portions of the full resupply orders at time instants with low enough values of the control signal. The warehouse is subject to a demand of dual nature: the first type of demand is contractual, resulting from a priori known obligations to its customers. The second type is a random, unknown term, bounded by a maximum value, realized by selling product leftover from trading with contracted customers. The controller’s goal is to ensure full demand satisfaction. We begin by employing a reference model with just one supplier and no random demand. Then, a sliding mode controller is applied to generate a desired resupply order profile capable of fulfilling the contractual demand at any time instant. This control scheme is designed to keep the amount of goods in the warehouse at its absolute minimum–in other words, the effect of the demand will always empty the warehouse at each time instant. We then continue by using this resupply order profile as a desired trajectory in a sliding mode controller for the real system. Finally, it is proven that with appropriate compensation for the random demand present in the system, this approach can achieve full demand satisfaction at all time instants in a system with multiple suppliers with varying lead times.
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