IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Cooperative Resource Status Exchange for Reliable Vehicular Sidelink Broadcasts","authors":"Mahmoud Elsharief;Saifur Rahman Sabuj;Sean Kwon;Han-Shin Jo","doi":"10.1109/OJITS.2026.3671296","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3671296","url":null,"abstract":"Vehicular sidelink communications are essential to autonomous driving, yet broadcast reliability under distributed operation remains constrained by collisions and inefficient resource use. The third-generation partnership project (3GPP) has defined a new radio (NR) for vehicle-to-everything (V2X) Mode 2 for autonomous resource reservation and selection. An important challenge in the design of NR-V2X systems is the efficient allocation of resources. Resource allocation facilitates reliable communication between user equipment (UEs) and other network elements. A cooperative approach among UEs is required to ensure reliable resource allocation and communication in such networks. This paper presents cooperative resource status exchange (CRSE), a cooperative scheme that augments autonomous sidelink broadcasts by disseminating a compact resource status map in each packet. It significantly maximizes the packet reception ratio (PRR) and resource utilization. In addition, CRSE integrates game theory to provide a sophisticated method for analyzing and optimizing resource selection strategies among UEs. The analytical model and simulation results show that CRSE performs better than new radio NR-V2X Mode 2, UE-scheduling, and short-term sensing-based resource selection (STS-RS) in terms of PRR and successful transmission rate. The results show that CRSE improves the PRR by approximately 24.6%, 15.2%, and 12.3% over NR-V2X, UE-scheduling, and STS-RS, respectively. Furthermore, the results demonstrate the possibility of realizing the maximum value of PRR and a successful transmission rate.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"771-786"},"PeriodicalIF":5.3,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11422953","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Passenger Flow Congestion Propagation in Urban Rail Transit Network With an Epidemic Approach","authors":"Xi Chen;Erlong Tan;Ziyi Meng;Xiaolei Ma","doi":"10.1109/OJITS.2026.3671091","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3671091","url":null,"abstract":"Congestion in urban rail transit (URT) systems resulting from the surge in travel demands affects the safety operation. Understanding the mechanism behind congestion propagation can contribute to improving system safety. Epidemic models have been widely used to describe the spread of congestion within URT networks. This study introduces an enhanced version of the epidemic model, called the susceptible-alert-infected-susceptible (SAIS) model with awareness, which incorporates the alerting state. This addition enables us to describe the preventive response of metro stations to congestion propagation upon receiving warning information. Our model allows stations without congestion to transition to an “alert” state based on the occurrence of congestion in neighboring stations. The alert state signifies that the transit agency will implement congestion control measures to prevent the station from becoming congested. Thus, an alert metro station has a reduced likelihood of transitioning to a congested state. We demonstrate that the SAIS model can decrease the density of congested stations, increase the congestion propagation threshold, and reduce the extent of congestion propagation. We also conduct numerical simulations for a real-world instance on the Beijing URT network to support our analytical findings. Furthermore, we quantitatively assess the influence of different parameter values on congestion propagation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"759-770"},"PeriodicalIF":5.3,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Safe and Energy-Efficient 5G NR V2X Communications in Rural Environments","authors":"Zhanle Zhao;Son Dinh-van;Yuen Kwan Mo;Siddartha Khastgir;Matthew D. Higgins","doi":"10.1109/OJITS.2026.3670715","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3670715","url":null,"abstract":"Connected braking can reduce fatal collisions in connected and autonomous vehicles (CAVs) by using reliable, low-latency 5G New Radio (NR) links, especially NR Sidelink Vehicle-to-Everything (V2X). In rural areas, road side units are sparse and power-constrained, so energy efficiency must be considered alongside safety. This paper studies how three communication control factors including subcarrier spacing (SCS), modulation and coding scheme (MCS), and transmit power (<inline-formula> <tex-math>${boldsymbol{P}}_{text {t}}$ </tex-math></inline-formula>) should be configured to balance safety and energy consumption in rural scenarios in light and heavy traffic scenarios. Safety is quantified by the packet receive ratio (PRR) against the minimum communication distance <inline-formula> <tex-math>${boldsymbol{D}}_{text {comm}}$ </tex-math></inline-formula>, defined as the distance that the vehicle travels during the transmission of the safety message. Results show that, under heavy traffic, increasing <inline-formula> <tex-math>${boldsymbol{P}}_{text {t}}$ </tex-math></inline-formula> and selecting a low-rate MCS at SCS = 30 kHz sustains high PRR at <inline-formula> <tex-math>${boldsymbol{D}}_{text {comm}}$ </tex-math></inline-formula>, albeit with higher energy cost. In light traffic, maintaining lower <inline-formula> <tex-math>${boldsymbol{P}}_{text {t}}$ </tex-math></inline-formula> with low MCS levels achieves a favorable reliability-energy trade-off while preserving acceptable PRR at <inline-formula> <tex-math>${boldsymbol{D}}_{text {comm}}$ </tex-math></inline-formula>. These findings demonstrate the necessity of adaptive, energy-aware strategy to guarantee both safety and energy efficiency in rural V2X systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"746-758"},"PeriodicalIF":5.3,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LOGIC+: LiDAR-Only Geometric-Intensity Confidence Grids for Drivable Area Estimation","authors":"Juan Luis Hortelano;Víctor Jiménez-Bermejo;Jorge Villagra","doi":"10.1109/OJITS.2026.3670457","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3670457","url":null,"abstract":"Autonomous vehicles today rely on high-definition maps for navigation and scene understanding. The creation and maintenance of these maps are costly processes that raise the entry bar for the deployment of autonomous driving technologies in the real world. A potential solution to this problem is estimating the drivable area in real time, a capability made possible by recent advancements in sensor technology and particularly relevant for complex urban environments. LiDAR-only methods for detecting drivable area are scarce and typically appear in fusion frameworks with other sensor technologies. Nevertheless, the optimization of single-sensor modalities coupled with flexible fusion solutions are key to unlock the dependencies on high-definition maps that navigation systems have nowadays. In this work we propose LOGIC<inline-formula> <tex-math>${mathcal {C}}$ </tex-math></inline-formula>: a LiDAR-Only Geometric-Intensity Confidence Grids drivable area estimation algorithm. The approach leverages both local and non-local geometric features of point clouds, using non-parametric techniques for intensity analysis. These features are treated as individual drivability estimations and computed with confidence maps that allow for intelligent fusion in a Linear-Opinion Pool. The fused drivability proposals are combined with occupancy information and input into a Dynamic Occupancy Grid to handle moving obstacles in the environment. The proposed method is tested in the Waymo Open Dataset which includes diverse urban driving scenes where is able to match the performance of state-of-the-art approaches without training or case-by-case parameter tuning.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"728-745"},"PeriodicalIF":5.3,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Congestion Spillover Effects of Freeway Crash Induced All Lane Closure Incidents: A Bayesian Copula-Based Approach","authors":"Sanjida Afroz Iqra;Mohamed Abdel-Aty;Chenzhu Wang;Zubayer Islam","doi":"10.1109/OJITS.2026.3669936","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3669936","url":null,"abstract":"Freeway Crash Induced All Lanes Closure (FCIALC) events are among the most severe non-recurrent incidents, often triggering substantial disruption across both the freeway and arterial network. This study develops a two-stage Bayesian modeling framework to quantify the interdependence between freeway lane closures and arterial congestion using real-world data. First, a hierarchical Bayesian Network (BN) is used to estimate the freeway all lanes closure duration (ALCD) levels based on pre-crash traffic conditions, crash severity, vehicle type, time of day, etc. By employing Bayesian Inference the model identifies critical scenarios involving fatal crashes and low pre-crash freeway speed as significant predictors of longer closure durations. In the second stage, a bivariate copula-based Bayesian regression model is used to jointly model freeway ALCD and arterial congestion duration. Results show that arterial congestion is significantly influenced by freeway ALCD, particularly when pre-crash arterial speeds are low. The Clayton copula outperforms other structures, indicating a positive lower-tail dependency, where short durations of freeway closure are often associated with short arterial congestion. The joint model substantially improves the predictive performance for arterial congestion duration over independent models, supporting the need to consider these dependencies in Integrated Corridor Management (ICM). Findings highlight the importance of optimizing diversion strategies based on real-time arterial capacity, broadcasting early warnings through multiple upstream Dynamic Message Signs (DMS), and adjusting signal timing of the surrounding arterial network to alleviate congestion. Given the sharp reduction in freeway capacity during FCIALC events, countermeasures such as temporary shoulder-use lanes or flex lanes managed by Lane Control Signs (LCS) can help restore freeway capacity. Moreover, the presence of lower-tail dependence in the joint model emphasizes that faster incident management is essential for faster network-wide recovery.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"709-727"},"PeriodicalIF":5.3,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11419131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TFF-Net: Multi-Task Visual Perception Incorporated With Temporal Feature Fusion for Driving Scene Understanding","authors":"Huei-Yung Lin;Shih-Han Wei","doi":"10.1109/OJITS.2026.3665906","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3665906","url":null,"abstract":"With the rapid advancement of autonomous driving technology, accurate perception of road scenes has become a cornerstone for achieving safe and efficient self-driving. Among various perception tasks, lane detection, road marking segmentation, road surface area extraction, and object detection are core components that directly affect vehicle navigation decisions, positioning accuracy, and obstacle avoidance capability. However, conventional techniques are often trained on single-task datasets, which not only limit the sources of available training data but also fail to fully leverage the potential of diverse scenes across datasets. In this paper we propose a multi-task visual perception system. It integrates lane detection, traffic marking semantics, road surface segmentation, and object detection within a unified framework. By sharing features through the multi-task framework, the overall computational efficiency is improved. To overcome the limitation of single-task data, the proposed TFF-Net adopts cross-dataset training to effectively integrate the data sources for different tasks, and enhances the model’s generalization ability across diverse scenes. By taking consecutive images as input, the model compensates for missing information caused by occlusion or poor lighting conditions in the current frame to improve the overall perception stability. In experiments, the proposed network is evaluated on multiple datasets across four tasks. The results have demonstrated that our approach achieves performance superior to existing methods on different metrics. Code is available at <uri>https://github.com/hank890121/MTVP</uri>","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"669-679"},"PeriodicalIF":5.3,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11398110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM and AI Agents for Autonomous Systems: A Survey of Applications, Datasets, and Security Challenges","authors":"Mohamed Amine Ferrag;Abderrahmane Lakas;Norbert Tihanyi;Merouane Debbah","doi":"10.1109/OJITS.2026.3665677","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3665677","url":null,"abstract":"The rapid integration of Large Language Models (LLMs) into autonomous systems marks a significant transition from modular, rule-based approaches to reasoning-driven, agent-based, and multimodal intelligence. LLM reasoning enables adaptive decision-making, context-aware planning, and human-aligned interaction, while AI agents extend these capabilities into structured autonomy pipelines that coordinate perception, reasoning, and control. These advancements are particularly critical in safety-sensitive domains such as autonomous driving (AD) and unmanned aerial vehicles (UAVs). This survey provides a comprehensive review of LLM reasoning and AI agents across scenario generation, decision-making, multimodal perception, cooperative V2X interactions, and UAV swarm autonomy. We examine the role of simulation platforms and datasets, including CARLA, Apollo ADS, AirSim, nuScenes, DriveLM, and emerging synthetic environments, in supporting reproducible evaluation and benchmarking. In addition, we analyze pressing security and robustness challenges, including adversarial prompt injection, data poisoning, multimodal perturbations, privacy leakage, and vulnerabilities in cooperative agent communication. Finally, we propose future research directions including adversarially robust pipelines, hybrid symbolic LLM planning, secure multimodal fusion, privacy-preserving human alignment, distributed trust mechanisms for swarm autonomy, and optimized Drone-LLM deployment across on-drone, edge, and cloud environments. By unifying applications, datasets, benchmarks, reasoning, agents, and security, this survey establishes a roadmap for developing robust, trustworthy, and secure LLM-enabled autonomous systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"615-657"},"PeriodicalIF":5.3,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11397656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Look-Ahead Cyber-Threat Forecasting for Connected and Automated Transport: A Spatio-Temporal Graph Learning Approach","authors":"Md Al Amin;Mohammad Shafat Ahsan;Jannatul Maua;Arifa Akter Eva;M. F. Mridha;Md. Jakir Hossen","doi":"10.1109/OJITS.2026.3664301","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3664301","url":null,"abstract":"Modern intelligent transportation systems (ITS) increasingly rely on connected electronic control units (ECUs), exposing in-vehicle networks to cyber-attacks such as message injection on the Controller Area Network (CAN) bus. While prior work has focused on post-factum detection, this paper addresses the underexplored task of forecasting cyber-attacks before they occur. We propose a spatio-temporal graph neural network (STGNN) architecture that models CAN traffic as a dynamic graph sequence, where nodes represent active CAN IDs and edges capture statistical co-activation patterns. Each graph snapshot encodes temporal features such as inter-arrival statistics and entropy, and is processed using graph attention layers followed by a multi-head temporal self-attention module. We evaluate the proposed method on two real-world datasets: Car-Hacking and OTIDS, comprising over 6.5 million labeled CAN frames from a Kia Soul under multiple attack scenarios. Experimental results show that STGNN achieves an area under the ROC curve (AUC) of 0.97, F1-score of 0.94, and a Brier score of 0.040 at a 1-second forecasting horizon on Car-Hacking, and maintains strong performance on OTIDS (AUC 0.91, F1 0.87) even though its rule-based labeling may introduce inconsistencies. The model outperforms six baseline methods across all lead times and demonstrates robustness under cross-dataset transfer and architectural variation. These findings confirm the feasibility of accurate, real-time cyberattack forecasting for automotive systems and highlight the utility of spatio-temporal graph learning for predictive cybersecurity in ITS.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"694-708"},"PeriodicalIF":5.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11396006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-Grained Model-Level Digital Twin Migration Method for Intelligent Transportation Systems","authors":"Ling Xing;Bing Li;Kaikai Deng;Jianping Gao;Honghai Wu;Huahong Ma","doi":"10.1109/OJITS.2026.3664400","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3664400","url":null,"abstract":"With the rapid advancement of digital twin-enabled intelligent transportation systems, efficient migration has become essential for maintaining real-time responsiveness and reliability. Existing approaches, however, primarily emphasize resource-aware optimization while neglecting the substantial overhead from state synchronization and redundant data transmission. Moreover, they typically treat digital twins as indivisible entities, overlooking optimization opportunities at the sub-model level. This limitation results in excessive migration costs and suboptimal resource utilization. To overcome these challenges, we propose a fine-grained model-level digital twin migration framework, FGDT, featuring three key components: (i) an explicit-implicit fused coupling graph construction captures both functional dependencies and latent collaborations among heterogeneous sub-models; (ii) a skew-aware migration pattern selection dynamically balances joint versus independent migration, thereby minimizing communication overhead and improving resource allocation; and (iii) a model-level migration strategy optimization strategy leverages dual-network PPO with a soft-constraint co-placement mechanism to support adaptive, fine-grained migration decisions. Extensive experiments validate the effectiveness of FGDT, which significantly reduces average system latency while maintaining low migration overhead, thereby enhancing both resource efficiency and overall system performance.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"565-583"},"PeriodicalIF":5.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11396010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RAISA: A Vision-Based Intelligent Safety Guidance Agent for Railway Operations via Modular Prompt-Orchestrated Reasoning","authors":"Kyung Ho Kang;Kyoung Ok Yang;Jun Won Choi","doi":"10.1109/OJITS.2026.3664788","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3664788","url":null,"abstract":"Railway safety remains a critical challenge due to the difficulty of detecting and responding to front-view hazards. We present the RAIlway Safety Agent (RAISA), an AI-powered system for front-view situational interpretation and role-specific safety guidance in railway operations. RAISA integrates a vision–language model (BLIP-2) and a large language model (Mistral-7B) within a Modular Prompt-Orchestrated Reasoning framework, structured into three stages: scene analysis, situation abstraction, and role-specific instruction generation. Unlike vision-only or rule-based systems, RAISA delivers semantically precise and context-adapted safety messages for both train drivers and traffic controllers. Through multi-turn prompting, the system enables zero-shot generalization across varied conditions, such as snow, fog, tunnels, and night scenes, without task-specific fine-tuning. Evaluated on 12,760 railway front-view images, RAISA demonstrated strong semantic fidelity and operational validity under zero-shot conditions, achieving over 93% average BERTScore F1 alongside high scores on reference-free checklist-based metrics, including PCR and CAC. The curated large-scale dataset and source code are made publicly accessible. Its interpretable architecture is designed for centralized server-based inference rather than edge deployment, ensuring scalable integration with existing railway communication networks and control infrastructures. By improving situational awareness and enabling network-level coordination, RAISA contributes to enhanced operational safety and efficiency, aligning with the goals of sustainable and intelligent railway transportation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"600-614"},"PeriodicalIF":5.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}