IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Distributed Signal-Free Intersection Optimization via Iterative Time Slots Adjustment for Connected and Automated Vehicles","authors":"Francesco Vitale;Ramin Niroumand;Claudio Roncoli","doi":"10.1109/OJITS.2026.3650976","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3650976","url":null,"abstract":"We propose a novel control strategy for signal-free intersection management through trajectory optimization for connected and automated vehicles. Such methodology is designed to be employed in a distributed manner, hence with no need for central units or specific tasks for leading vehicles, while only a limited amount of information needs to be exchanged and processed. The approach relies on an iterative distributed allocation and subsequent optimization of the time slots to cross the intersection. The distributed allocation aims to identify the constraints for the optimization problem to be solved, which enables the formulation of uncoupled subproblems that can be solved by each vehicle independently. The iterative algorithm initially allows the allocated time slots to overlap via a violation function that gradually decreases to zero as the algorithm progresses. This provides the optimization problem with enough flexibility to allow vehicles to resize their time slots and make them more suitable to meet their own requirements of trajectory smoothness and error minimization. We include simulation results and sensitivity analyses to demonstrate the effectiveness of our approach.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"285-303"},"PeriodicalIF":5.3,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11329051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026411","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":"Transformer Architectures for Distracted Driving Behavior Detection: A Comprehensive Review of Vision-Based Approaches","authors":"Muhammad Fawzan Anwari Muhammad Saiful Anuar;Fadhlan Hafizhelmi Kamaru Zaman;Syahrul Afzal Bin Che Abdullah;Kok Mung Ng;Kanendra Naidu Vijyakumar;Shyh Kang Ng","doi":"10.1109/OJITS.2025.3650561","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3650561","url":null,"abstract":"Distracted driving is a leading cause of road accidents, with visual and manual distractions being particularly prevalent. Traditional computer vision methods, particularly Convolutional Neural Networks (CNNs), have been extensively utilized for detecting driver behavior; however, they face challenges in effectively modeling long-range dependencies and complex spatiotemporal patterns. Recent advancements in Vision Transformer (ViT) demonstrate significant potential to address these limitations by leveraging global attention mechanisms and a scalable architecture. This review presents a comprehensive review of ViT-based approaches in distracted driving detection, which covers both image-based and video-based methods. It examines several architectural innovations, such as lightweight ViT variants, pose-aware attention-enhanced models, and hybrid ViT-architecture designs. The review also explores multi-modal and multi-view fusion strategies, which integrate several inputs such as RGB, infrared, depth, and physiological signals to enhance model robustness across diverse driving scenarios. In addition, the paper highlights benchmark datasets and performance comparisons used in distracted driving behavior detection. Finally, this review highlights the current challenges, including computational cost and interpretability, while also proposing directions for future research. Overall, ViT-based models present a promising foundation for developing the next generation of intelligent driver monitoring systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"233-267"},"PeriodicalIF":5.3,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11322789","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982216","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":"Computationally Efficient Observer-Based Robust MPC for High-Speed Vehicle Centralized Trajectory Tracking With H∞ Disturbance Attenuation","authors":"Manuel Jiménez-Salas;Fernando Viadero-Monasterio;Miguel Meléndez-Useros;Beatriz L. Boada","doi":"10.1109/OJITS.2026.3693433","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3693433","url":null,"abstract":"This paper proposes a novel centralized trajectory tracking LPV controller for automated vehicles based on Observer-Based Robust Model Predictive Control (OB-RMPC), combining longitudinal and lateral control of the vehicle through a coupled dynamics vehicle model with the aim of applying it during high-velocity evasive maneuvers. For this purpose, minimization of the trajectory tracking errors has been pursued through a cost function which combines the MPC estimated states and control input costs with an <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> condition. Since not all vehicle states are available for easy measurement, an observer-based approach has been followed for the controller synthesis. First, a Luenberger observer is designed for unknown states estimation. Then, the OB-RMPC optimization problem is tackled by addressing the dynamics of the estimation error and estimated states. Quadratic boundedness condition, augmented system robust positive invariance, and actuator saturation constraints are managed through the use of Linear Matrix Inequalities (LMIs) formulation. A mechanism to update the stability boundaries of the estimation error set is implemented online. Finally, to mitigate the computational demands of the OB-RMPC strategy, an online algorithm is proposed. The control action is calculated based on the states estimation, the optimized bound of the estimation error and an offline-constructed look-up table. The proposed strategy is validated through simulations in Matlab® Simulink and CarSim® under different high-velocity driving scenarios. The obtained results emphasize the superior tracking effectiveness of the proposed control method compared to other state-of-the-art control strategies in high-velocity maneuvers without compromising lateral stability.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"1311-1328"},"PeriodicalIF":5.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003360","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":"Robust Reinforcement Learning for Fault-Tolerant Autonomous Driving Using a Kalman-Filter Autoencoder Approach","authors":"Hassanien Zanki;Árpád Huszák","doi":"10.1109/OJITS.2026.3696244","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3696244","url":null,"abstract":"Ensuring reliable and safe autonomous driving in the presence of sensor noise, missing data, and actuation faults is a major challenge for Reinforcement Learning (RL) systems. Such faults can disrupt perception and control pipelines, leading to unsafe behavior and unstable policies. Although recent RL-based driving policies have shown promise in structured simulation environments, their performance often deteriorates under real-world sensor corruption and actuator faults, underscoring the need for robustness-focused designs. To address this, we propose the Kalman-Filter Autoencoder (KFAE) approach, which integrates a denoising autoencoder with a Kalman filter to generate temporally consistent and fault-resilient latent state representations for policy learning. This design allows RL agents to operate more robustly in dynamic and uncertain environments. We evaluate the model in fault-injected driving environments against the Noisy Environment (NE) baseline. The evaluation spans four experimental phases. In Phase 1, KFAE reduced Collision Count (CC) by 64.3%, while it increased Steps Till Collision (STC) by 9.5% and Episodes Reward Mean (ERM) by 133.8%. In Phase 2, a sensitivity analysis over ten fault parameters showed up to 129.3% improvement in ERM, 161.2% in STC, and a 92.9% reduction in CC. In Phase 3, across 27 Taguchi-designed high-dimensional fault scenarios, KFAE achieved average gains of 417% in ERM, 196% in STC, and a 66.7% reduction in CC. This progression from simpler highway settings to the more interaction-intensive intersection task demonstrates the method’s scalability and reflects conditions closer to urban driving challenges. In Phase 4, we extended our evaluation to the more complex <monospace>intersection-v0</monospace>environment, characterized by cross-traffic and increased conflict points. Focusing on CC as the most safety-critical metric, KFAE consistently achieved lower CC than the NE baseline across most experiments, with reductions exceeding 50% in several challenging configurations. This demonstrates that KFAE’s robustness benefits generalize from structured highway driving to more complex urban intersection scenarios. To strengthen baseline comparisons, we further benchmark KFAE against three robustness-oriented baselines: domain-randomized fault training (DR), adversarial observation/action perturbation training (AdvObs), and the self-supervised BYOL representation baseline. These results demonstrate the effectiveness of combining filtering and denoising to enhance RL–based decision-making in safety-critical autonomous driving environments. These findings highlight the potential of robust RL frameworks like KFAE to improve both safety and decision-making reliability in autonomous vehicles operating in complex real-world traffic environments.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"1399-1437"},"PeriodicalIF":5.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11534170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171395","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":"Evolution of Deep Learning Techniques and Challenges for Vehicle Trajectory Prediction","authors":"Sidra Rashid;Muazzam A. Khan Khattak;Muhammad Usman Akram;Raheel Mumtaz;Toqeer Ali Syed;It Ee Lee;Qamar Wali","doi":"10.1109/OJITS.2026.3693912","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3693912","url":null,"abstract":"Accurate vehicle trajectory prediction (VTP) plays a fundamental role in the advancement of intelligent transportation systems and autonomous driving. It improves situational awareness, optimizes decision-making and safety in complex traffic scenarios. Although several surveys have reviewed vehicle trajectory prediction approaches, a comprehensive review of recent deep learning-based approaches from a system and modeling perspective is still lacking. To bridge this gap, we conducted a systematic review of the literature on deep learning-based methods by critically analyzing emerging trends in model design. We introduce a system-oriented taxonomy of vehicle trajectory prediction methods which includes classical, reinforcement learning-based, collaborative, and deep learning-based methods. We evaluate their capacity to model uncertainty, inter-agent interactions, and dynamic traffic behavior. This survey emphasizes three key perspectives that drive performance in deep learning-based models including data preprocessing and feature extraction, model creation and attention mechanisms. Deep-learning-based methods are critically analyzed in design dimensions, e.g. temporal modeling strategy, interaction encoding, modeling capability, suitable deployment scenarios and their key strengths for trajectory prediction. Finally, we discuss open research challenges and propose practical recommendations for next-generation VTP in autonomous driving.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"1364-1385"},"PeriodicalIF":5.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171739","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":"Robust and Congestion-Aware Planning of Electric Vehicle Charging Stations Under Algorithmic and Parametric Uncertainty","authors":"Siddhartha;P. E. S. N. Raju","doi":"10.1109/OJITS.2026.3714487","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3714487","url":null,"abstract":"Planning public electric vehicle charging stations (EVCSs) in emerging urban environments requires balancing operator profitability and user accessibility while accounting for traffic congestion and uncertainty in future demand and economic conditions. However, most existing EVCS planning studies focus primarily on identifying nominal optimal solutions and provide limited insight into the reliability of deployment decisions under uncertain conditions. To address this gap, this study develops a congestion-aware bi-objective framework for EVCS siting and sizing that simultaneously maximizes annualized operator profit and minimizes aggregate additional user cost, including travel, queueing, and energy-consumption components. The study integrates metaheuristics with congestion due to peak period travel, queueing-based service assessment, Monte Carlo uncertainty analysis, reliability evaluation, and elasticity-based sensitivity assessment within a unified decision-support methodology. The approach is demonstrated for the city of Guwahati, India represented by a 53-node, 76-link road network. Results highlight that robust EVCS deployment solutions consistently converge to a narrow infrastructure envelope comprising approximately 16–18 charging stations, 366–390 chargers, and a peak demand of 22–23 MW despite uncertainty in key techno-economic parameters. Reliability analysis shows that selected compromise solutions maintain profitability and affordability targets with high probability under stochastic perturbations, while sensitivity analysis identifies charging demand, retail tariffs, and fleet battery composition as the principal drivers of outcome variability. The proposed framework provides a reproducible and reliable methodology for congestion-aware EVCS planning under uncertainty.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"1919-1942"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11614020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747377","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":"Energy-Aware Hub Location via Reinforcement Learning and Thermodynamic Optimization","authors":"Kassem Danach;Samir Haddad;Wael Hosny Fouad Aly;Chadi Kallab;Jinane Sayah","doi":"10.1109/OJITS.2026.3681199","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3681199","url":null,"abstract":"The Hub Location Problem (HLP) remains a cornerstone of strategic decision-making in logistics and transportation networks. However, emerging challenges driven by climate variability, energy consumption constraints, and carbon emission regulations are reshaping the complexity of these classical models. This paper introduces a novel Energy-Aware Dynamic Hub Location Problem (EA-DHLP) that integrates climate-driven demand variations, renewable energy availability, and carbon penalties into the hub network design. We formulate the core EA-DHLP as a mixed-integer linear program (MILP) by treating climate and energy series as exogenous parameters and isolating any nonlinear couplings as optional MINLP extensions. To solve large instances of this MILP-based and dynamic problem, we propose a Hybrid Reinforcement Learning and Thermodynamic Hyper-Heuristic (RL-THH) framework. The reinforcement learning agent adaptively selects from a pool of low-level heuristics based on real-time performance feedback, while a thermodynamic-inspired temperature control mechanism dynamically balances exploration and exploitation throughout the search process. We benchmark RL-THH against 1) exact MILP solving on small/medium instances and 2) advanced metaheuristics/hyperheuristics (including Artificial Bee Colony, Grey Wolf Optimizer, and an RL-only HH variant) under matched computational budgets, with nonparametric significance testing (Wilcoxon with Holm–Bonferroni correction) and effect-size reporting (Cliff’s <inline-formula> <tex-math>$delta $ </tex-math></inline-formula>). Extensive computational experiments are conducted on both synthetic and real-world datasets, including the Australian Post and CAB datasets, incorporating climate and energy data. Results show statistically significant improvements in minimizing total costs, reducing energy consumption, and lowering carbon emissions when compared to traditional metaheuristics and the aforementioned state-of-the-art baselines. For small/medium instances, the exact MILP provides optimality certificates that contextualize RL-THH’s performance; for larger instances, RL-THH scales while preserving solution quality and stability. This study offers a scalable, adaptive, and environmentally conscious solution framework for modern hub location problems under sustainability and uncertainty considerations.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"1197-1208"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11474905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148485770","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":"Data Privacy and Security in Connected and Autonomous Vehicles: Challenges and Opportunities","authors":"Amit Chougule;Vinay Chamola;Norbert Herencsar;Fei Richard Yu","doi":"10.1109/OJITS.2026.3740201","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3740201","url":null,"abstract":"The rapid evolution of the automobile sector, driven by advancements in Connected and Autonomous Vehicles, has transformed how vehicles communicate, operate, and interact with their surroundings. Technologies such as Vehicle-to-Everything (V2X) communication enable autonomous cars to generate and exchange substantial amounts of data with real-world entities, enhancing safety, improving performance, and delivering personalized user experiences. However, this data-driven ecosystem introduces significant challenges, particularly concerning data privacy, security, and governance. The absence of transparency and comprehensive regulatory frameworks exacerbates issues of unauthorized data access, prolonged retention, and potential misuse, creating tension between consumer benefits and privacy risks. This review paper explores the multifaceted nature of data sharing in CAVs, analyzing its contributions to innovation and its associated vulnerabilities. It evaluates data-sharing mechanisms and communication technologies, highlights the benefits of data exchange across various use cases, examines privacy concerns and risks of data misuse, and critically reviews regulatory frameworks and their inadequacies in safeguarding user privacy. By providing a thorough analysis of the current state of data sharing in the automotive sector, the paper emphasizes the urgent need for robust policies and ethical data management practices. It calls for striking a balance between fostering technological advancements and ensuring secure, consumer-friendly solutions, paving the way for a trustworthy and innovative automotive future.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"2489-2509"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11720300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188099","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":"A Fuzzy Multi-Criteria and Cooperative Game-Theoretic Framework for Strategic Planning of Real-Time Adaptive On-Demand Public Transportation Systems","authors":"Seyed Behnam Razavian;Andrés Rodríguez Gutierrez;Luigi Dell'olio;Jose Luis Moura","doi":"10.1109/OJITS.2026.3740439","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3740439","url":null,"abstract":"Compared with a traditional fixed-route system, a demand-responsive transit system can respond to fluctuating demand and thereby avoid inefficiencies that would arise from traffic congestion. Yet there are several obstacles to the practical application of the approach. Objective: This paper develops a strategic decision-support framework for the implementation of Real-time Adaptive On-Demand Public Transportation (RT-ODPT) systems, aimed at addressing these challenges and supporting more informed strategy selection. Method: The study adopts a hybrid approach. First, SWOT analysis is used to structure the main internal and external factors. Then, FBWM is applied to weight these factors, followed by QSPM to compare alternative strategies. To better understand how strategies interact, a fuzzy cooperative game model based on a Taguchi orthogonal array (FGTOA) is introduced, which significantly reduces the number of required evaluations. Uncertainty in expert judgments is handled using fuzzy logic and GMIR. Results: The analysis shows that low-cost and scalable technologies (O2) play the most critical role (weight = 0.1611). Among the tested combinations, SO1–WO3–ST3–WT3 performs best, with a defuzzified value of 18.0731. At the same time, the FGTOA reduces the number of scenarios from 81 to just 9 without affecting the overall ranking. Conclusions: On the whole, the results indicate that integrating technical viability with adaptive approaches and risk awareness is central to the successful implementation of such systems. Impact: In essence, the model can facilitate the delivery of on-demand services in fringe locations and foster greater inclusivity regarding transportation, especially among individuals who have restricted access to mainstream modes of transportation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"2475-2488"},"PeriodicalIF":4.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11720393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188199","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":"StigDiff: Stigmergy Diffusion for Signal Compensation in Multi-Agent Localization","authors":"Yan Zhang;Rong Xie","doi":"10.1109/OJITS.2026.3694707","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3694707","url":null,"abstract":"Multi-agent localization faces the problem of signal loss or attenuation. To address the signal compensation problem, we propose a novel diffusion model for multi-agent signal compensation, named StigDiff. First, we use a planar flow-based variational auto-encoder as the signal encoder module to learn a probabilistic representation of the signals and capture their statistical characteristics. Then we use a stigmergy diffusion model to generate synthetic encoded vectors, which can compensate for the signal degradation and improve the localization accuracy. Finally, to improve robustness and stability, we use a linear decoder with sparsity and magnitude regularization terms to reconstruct high-fidelity signals from the synthetic encoded vectors. The experimental results show that our StigDiff method outperforms other baselines in terms of localization accuracy and robustness, demonstrating its effectiveness in compensating for signal loss or attenuation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"1452-1465"},"PeriodicalIF":5.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11526735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171399","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}