IEEE Transactions on Intelligent Transportation Systems最新文献_第10页

Oranits: Mission Assignment and Task Offloading in Open RAN-Based ITS Using Metaheuristic and Deep Reinforcement Learning 基于元启发式和深度强化学习的开放式基于ran的ITS任务分配和任务卸载

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-29 DOI: 10.1109/TITS.2026.3705751

Ngoc Hung Nguyen;Nguyen Van Thieu;Quang-Trung Luu;Anh Tuan Nguyen;Senura Hansaja Wanasekara;Nguyen Cong Luong;Fatemeh Kavehmadavani;Van-Dinh Nguyen

{"title":"Oranits: Mission Assignment and Task Offloading in Open RAN-Based ITS Using Metaheuristic and Deep Reinforcement Learning","authors":"Ngoc Hung Nguyen;Nguyen Van Thieu;Quang-Trung Luu;Anh Tuan Nguyen;Senura Hansaja Wanasekara;Nguyen Cong Luong;Fatemeh Kavehmadavani;Van-Dinh Nguyen","doi":"10.1109/TITS.2026.3705751","DOIUrl":"https://doi.org/10.1109/TITS.2026.3705751","url":null,"abstract":"In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing. Existing studies often overlook the intricate interdependencies between missions and the costs associated with offloading tasks to edge servers, leading to suboptimal decision-making. To bridge this gap, we introduce <monospace>Oranits</monospace>, a novel system model that explicitly accounts for mission dependencies and offloading costs while optimizing performance through vehicle cooperation. To this end, we propose a twofold optimization approach. First, we develop a metaheuristic-based evolutionary computing algorithm, namely the Chaotic Gaussian-based Global ARO (CGG-ARO), serving as a baseline for one-slot optimization. Second, we design an enhanced reward-based deep reinforcement learning (DRL) framework, referred to as the Multi-agent Double Deep Q-Network (MA-DDQN), that integrates both multi-agent coordination and multi-action selection mechanisms, significantly reducing mission assignment time and improving adaptability over baseline methods. Extensive simulations reveal that CGG-ARO improves the number of completed missions and overall benefit by approximately 7.1% and 7.7%, respectively. Meanwhile, MA-DDQN achieves even greater improvements of 11.0% in terms of mission completions and 12.5% in terms of the overall benefit. These results highlight the effectiveness of <monospace>Oranits</monospace> in enabling faster, more adaptive, and more efficient task processing in dynamic ITS environments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12515-12530"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

See Better While Driving: Sense-Act Cooperative Attention With Asymmetric Soft Actor-Critic for Autonomous Driving 驾驶时看得更清楚：自动驾驶的非对称软行为评论的感觉-行为合作注意

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-10 DOI: 10.1109/TITS.2026.3698927

Yueyong Chen;Huifeng Hu;Yihong Hu;Ping Wang

{"title":"See Better While Driving: Sense-Act Cooperative Attention With Asymmetric Soft Actor-Critic for Autonomous Driving","authors":"Yueyong Chen;Huifeng Hu;Yihong Hu;Ping Wang","doi":"10.1109/TITS.2026.3698927","DOIUrl":"https://doi.org/10.1109/TITS.2026.3698927","url":null,"abstract":"Reinforcement learning has shown strong potential in autonomous driving. However, under high-dimensional visual inputs and dense traffic interactions, existing serial perception-decision architectures still face inaccurate attention allocation, weak representation learning, and slow policy convergence. To address these issues, this paper proposes SacaSAC, a cooperative reinforcement learning framework for complex urban driving. The proposed Sense-Act Cooperative Attention (SACA) learns a reward-related map through reward supervision and explicitly incorporates it into attention generation, enabling visual representations to focus on key regions related to task outcomes. An asymmetric Actor-Critic architecture is further adopted, where privileged information is used during training to stabilize value estimation and improve policy learning. Experiments in CARLA unsignalized intersection scenarios and extended test settings show that the proposed method outperforms multiple baselines in task completion, safety, training stability, and interpretability. In the Town10 test scenario, it improves the success rate by 36 percentage points over the base SAC. Tests under different maps, road topologies, and sensor configurations further verify its robustness and generalization. The proposed framework provides an effective approach for reliable scene understanding and safe urban driving under complex interactions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13310-13324"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MUSCLE-Net: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting MUSCLE-Net：用于行人轨迹预测的多尺度感知网络

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-09-25 DOI: 10.1109/TITS.2026.3708429

Yu Liu;Ming Huang;Xiao Ren;Zhijie Liu;Youfu Li;He Kong

{"title":"MUSCLE-Net: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting","authors":"Yu Liu;Ming Huang;Xiao Ren;Zhijie Liu;Youfu Li;He Kong","doi":"10.1109/TITS.2026.3708429","DOIUrl":"https://doi.org/10.1109/TITS.2026.3708429","url":null,"abstract":"Accurate pedestrian trajectory prediction is essential for safe navigation in autonomous driving and intelligent transportation systems. Despite substantial progress made by recent methods, most existing approaches are limited in fully exploiting diverse observations and often overlook the scale dependency of future motion, treating multiscale features uniformly regardless of underlying motion dynamics. This limits their robustness across diverse pedestrian behaviors. To address these challenges, we propose a Predicted-MUltiSCale-Aware Network (MUSCLE-Net) for Pedestrian Trajectory Forecasting that integrates complementary multimodal cues with scale-adaptive prediction mechanisms. The proposed framework is built upon a Multiscale Multimodal Feature Extraction (MMFE) module, which combines multiscale representation, modality-aware recalibration, and directional cross-modal fusion to construct semantically aligned representations from bounding boxes, velocities, and pose information. Building on these features, a Multiscale Enhanced Hierarchical Prediction (MEHP) module performs prediction-aware future-motion refinement via a probabilistic coarse predictor, scale-aligned fusion, and progressive refinement, adaptively selecting scale-relevant cues to mitigate spatial drift. Extensive experiments on the JAAD and PIE benchmarks demonstrate that the proposed MUSCLE-Net achieves competitive performance and consistent gains compared with state-of-the-art trajectory prediction methods. Code is publicly available at <uri>https://github.com/AISLAB-sustech/MUSCLE-Net</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13396-13411"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Trajectory Planning Method for Unpaved Roads Based on Dynamics Response Prediction 基于动态响应预测的非铺装道路轨迹规划方法

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-29 DOI: 10.1109/TITS.2026.3705445

Wenguang Wu;Jin He;Lin Hu;Jiaming Tang;Yi Cao;Fang Wang

{"title":"A Trajectory Planning Method for Unpaved Roads Based on Dynamics Response Prediction","authors":"Wenguang Wu;Jin He;Lin Hu;Jiaming Tang;Yi Cao;Fang Wang","doi":"10.1109/TITS.2026.3705445","DOIUrl":"https://doi.org/10.1109/TITS.2026.3705445","url":null,"abstract":"Unpaved roads are marked by irregular undulations and more pronounced geometric curvature than paved roads. For autonomous vehicles, trajectory planning that ignores vehicle dynamic response characteristics easily gives rise to safety risks in such complex scenarios. This paper proposes a trajectory planning method that takes vehicle-road cooperative information as input and incorporates vehicle dynamic response prediction. First, local road reference lines, elevation models and risk models are constructed with sensing data from on-board and roadside devices; a quintic polynomial model is adopted to pre-plan candidate trajectory clusters, and a vehicle dynamics model is introduced for virtual pre-tracking to predict vehicle dynamic response states during tracking. A multi-objective cost function integrating vehicle safety, path smoothness, trajectory deviation and predicted dynamic response is designed to evaluate and optimize candidate trajectories under road spatial constraints and vehicle dynamic constraints. Performance tests are conducted on 84,000 road models with diverse convex-concave features generated via Latin Hypercube Sampling based on real unpaved roads. Results show that compared with traditional dynamic programming and collision detection methods, the proposed method improves the average planning success rate by 23.8% and 55.99%, increases the average planned path length by 15.4% and 47.2%, reduces the rollover index by 39.0% and 13.6%, and cuts the yaw index by 58.1% and 34.9%, respectively, which significantly enhances vehicle safety and traffic efficiency in complex road scenarios and demonstrates application potential in vehicle-to-infrastructure systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13343-13362"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Safety Testing of Autonomous Vehicles With Implicit Traffic Distributions by Dense Reinforcement Learning 基于密集强化学习的隐式交通分布自动驾驶汽车智能安全测试

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-08 DOI: 10.1109/TITS.2026.3707517

Kun Ren;Yizhou Xu;Jingxuan Yang;Yi Zhang;Jianming Hu;Shuo Feng

{"title":"Intelligent Safety Testing of Autonomous Vehicles With Implicit Traffic Distributions by Dense Reinforcement Learning","authors":"Kun Ren;Yizhou Xu;Jingxuan Yang;Yi Zhang;Jianming Hu;Shuo Feng","doi":"10.1109/TITS.2026.3707517","DOIUrl":"https://doi.org/10.1109/TITS.2026.3707517","url":null,"abstract":"Reliable safety evaluation is essential for the development and large-scale deployment of autonomous vehicles (AVs). However, the complexity of high-dimensional traffic environments and the rarity of safety-critical events make direct testing extremely inefficient and costly, requiring an enormous number of simulation miles. Importance sampling has been employed to accelerate this process, but most existing methods rely on known or explicit traffic behavior distributions, which are often inaccessible in practice. To address this limitation, we propose Deep-IIS, which extends implicit importance sampling by utilizing rejection sampling to handle unknown distributions without requiring explicit density functions. By integrating dense deep reinforcement learning to adaptively optimize key sampling parameters, Deep-IIS intelligently concentrates sampling on high-risk regions, significantly improving testing efficiency while maintaining evaluation reliability. We validate the proposed method in two representative traffic scenarios, demonstrating its superior ability to uncover safety-critical cases and to produce accurate performance estimates under implicit distributions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13040-13053"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Facilitating Fast Response of the Heterogeneous Vehicle Platoon Based on the Multi-Delay Proportional-Retarded Controller 基于多时延比例延迟控制器的异构车辆队列快速响应研究

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-03 DOI: 10.1109/TITS.2026.3707827

Xu Zhu;Jiale Li;Jie Wei

{"title":"Facilitating Fast Response of the Heterogeneous Vehicle Platoon Based on the Multi-Delay Proportional-Retarded Controller","authors":"Xu Zhu;Jiale Li;Jie Wei","doi":"10.1109/TITS.2026.3707827","DOIUrl":"https://doi.org/10.1109/TITS.2026.3707827","url":null,"abstract":"For heterogeneous vehicle platoons, delays often severely impact the internal stability and string stability and reduce the response speed. To fully leverage the delays to improve platoon stability and facilitate fast response, this paper designs a proportional-retarded (PR) controller for the heterogeneous vehicle platoon by intentionally introducing multiple delays. Based on this, a feasible rightmost pole assignment method is proposed. Accordingly, the assignable range of the rightmost pole is determined by using the Cauchy-Riemann equation, and it is revealed that the assignable range is independent of the communication topology. Meanwhile, the guidelines for designing the controller parameters and delays are provided to facilitate fast response of the vehicle platoon. Moreover, to enhance the string stability progressively upstream along the platoon, the rightmost poles for each subsystem are reasonably separated by tuning multiple delays, and then the string stability conditions are derived. Finally, simulation results demonstrate the effectiveness and feasibility of the proposed method. Compared to the single-delay PR controller and traditional proportional-derivative (PD) controller, the heterogeneous vehicle platoon under the multi-delay PR controller exhibits a fast response, strong disturbance suppression capability, and significantly improved ride comfort and fuel economy.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13054-13069"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning 利用整数线性规划求解和机器学习优化护理出租车调度

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-10 DOI: 10.1109/TITS.2026.3709001

Riku Nakao;Akihito Hiromori;Hamada Rizk;Hirozumi Yamaguchi

{"title":"Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning","authors":"Riku Nakao;Akihito Hiromori;Hamada Rizk;Hirozumi Yamaguchi","doi":"10.1109/TITS.2026.3709001","DOIUrl":"https://doi.org/10.1109/TITS.2026.3709001","url":null,"abstract":"In this paper, we formulate a new vehicle dispatch optimization problem, called Nursing Care Taxi Dispatch, as a variant of the Vehicle Routing Problem, considering constraints related to wheelchair use, user compatibility, pick-up and drop-off times, and vehicle limitations. Previous neural-based methods for Vehicle Routing Problems have typically addressed a few simple constraints, while our new problem involves multiple complex constraints, resulting in having fewer destinations to select. This complexity makes it more difficult to obtain solutions that allow all nodes to be visited with a limited number of vehicles. To balance low violation rate, computational efficiency, and solution quality, we propose a supervised machine learning approach based on the Transformer architecture. We first obtain a set of high-quality solutions using an integer linear programming solver for given inputs and then train our learning model through supervised learning. Additionally, we introduce the post-processing of the paths generated by the learning model, ensuring that all constraints are satisfied. We compared each instance’s objective function value (operating time), execution time, and constraint violation rate across different methods: our proposed method and some existing methods including integer linear programming and machine learning-based methods, using real-world facility data. Our method successfully produced balanced solutions regarding operating time, execution time, and constraint violation rate. Notably, we observed a decrease in the operating time for all problem sizes and regions, while keeping constraint violations to a minimum compared to existing methods. Especially, the decrease reached up to 8% for problem sizes with fewer than 30 users.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12369-12384"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11603825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Dual-Aligned SAC Approach With Adaptive Risk Threshold for Safe Motion Planning of Autonomous Driving 基于自适应风险阈值的自动驾驶安全运动规划双对齐SAC方法

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-08-03 DOI: 10.1109/TITS.2026.3714197

Xin Cheng;Zhenjun Qiu;Zhidong Wang;Xiangkun He;Jingmei Zhou;Chuan Hu

{"title":"A Dual-Aligned SAC Approach With Adaptive Risk Threshold for Safe Motion Planning of Autonomous Driving","authors":"Xin Cheng;Zhenjun Qiu;Zhidong Wang;Xiangkun He;Jingmei Zhou;Chuan Hu","doi":"10.1109/TITS.2026.3714197","DOIUrl":"https://doi.org/10.1109/TITS.2026.3714197","url":null,"abstract":"Safe reinforcement learning (RL) algorithms have attracted significant attention due to safety concerns arising from their deployment in real-world environments. Balancing both safety and driving smoothness remains a critical challenge for autonomous vehicles (AVs). A novel risk-aware RL framework built upon Soft Actor-Critic (SAC) is proposed in this paper. Our primary contribution is a decomposition of optimization objectives: we introduce a dedicated Temporal Convolutional Network (TCN)-based Risk Critic designed to encapsulate heterogeneous risk factors. This network simultaneously models long-term latent collision risks and short-term transient risks, outputting a risk estimate incorporated into the policy optimization via Lagrangian relaxation. Concurrently, the Value Critic focuses solely on maximizing reward based on driving smoothness and destination features. This explicit separation allows the Actor network to learn policies that inherently balance safety and comfort. Secondly, an Exponential Weighted Moving Average Forecast (EWAF)-based adaptive threshold model with a catch-up mechanism is presented to deal with time-varying scenarios. Thirdly, to mitigate safety hazards during the training phase, we employ expert-guided (EG) replay buffer initialization whose samples generated by a robust control-theory-based controller to warm-start the RL training process, significantly reducing early-stage collisions. Extensive simulations conducted in the CARLA simulator demonstrate the effectiveness of our approach, showcasing substantial improvement in comprehensive performance. The source code for our algorithm is available at <uri>https://github.com/hover03/Dual-SAC-2AD</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13230-13240"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Railway Localization With Vision: An Integrated Framework for GNSS, INS, and NetVLAD-Based Visual Place Recognition 用视觉增强铁路定位：基于GNSS、INS和netvladv的视觉位置识别集成框架

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-03-26 DOI: 10.1109/TITS.2026.3676455

Yongqiang Liu;Jian Wang;Wei Jiang;Xiao Hu;Federica Battisti;Sara Baldoni;Bai-Gen Cai;Jiang Liu

{"title":"Enhancing Railway Localization With Vision: An Integrated Framework for GNSS, INS, and NetVLAD-Based Visual Place Recognition","authors":"Yongqiang Liu;Jian Wang;Wei Jiang;Xiao Hu;Federica Battisti;Sara Baldoni;Bai-Gen Cai;Jiang Liu","doi":"10.1109/TITS.2026.3676455","DOIUrl":"https://doi.org/10.1109/TITS.2026.3676455","url":null,"abstract":"Accurate train positioning is crucial for ensuring the safety and operational efficiency of intelligent railway systems. Traditional Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integration often performs reliably only under ideal conditions. However, in GNSS-denied environments such as tunnels, signal blockage can cause significant positioning errors. To overcome these limitations, this paper proposes a seamless train positioning framework that dynamically switches between Visual Place Recognition (VPR) /INS and GNSS/INS integration. The framework operates adaptively: in tunnel or signal-denied scenarios, where GNSS is unavailable, VPR/INS integration maintains accurate localization; in open-sky environments, GNSS/INS integration ensures continuous positioning. To enable VPR-based localization, a reference image database of the railway line is pre-constructed, and the NetVLAD model is employed to extract robust global image descriptors. A location-guided image retrieval strategy is then introduced to accelerate global feature matching, providing a coarse estimation of the train’s position based on retrieval results. To ensure the accuracy of the matching process, we have implemented a double-check mechanism that accepts matching results only when validated, thereby enhancing overall railway positioning safety. Field experiments conducted on the Lhasa–Nyingchi Railway show that during GNSS signal blockage, the proposed switching system effectively reduces positioning errors, achieving an overall RMS error of approximately 1.03m.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13539-13553"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Railway Network Security and Privacy With Blockchain and Attribute-Based Encryption 利用区块链和基于属性的加密增强铁路网络的安全性和保密性

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-09-08 DOI: 10.1109/TITS.2026.3725650

Farooq Ahmed;Teng Zhou;Hazrat Bilal;Neeraj Kumar;Abdullah Mohammed Almuhaideb;Sarra Ayouni

{"title":"Enhancing Railway Network Security and Privacy With Blockchain and Attribute-Based Encryption","authors":"Farooq Ahmed;Teng Zhou;Hazrat Bilal;Neeraj Kumar;Abdullah Mohammed Almuhaideb;Sarra Ayouni","doi":"10.1109/TITS.2026.3725650","DOIUrl":"https://doi.org/10.1109/TITS.2026.3725650","url":null,"abstract":"Railway systems are gradually adopting Internet of Things (IoT) and artificial intelligence (AI)-driven technologies to improve operational efficiency, implement preventive maintenance, and enable real-time monitoring. However, this technological shift has raised significant challenges for security, privacy, and data integrity. As cloud-based infrastructure is susceptible to unauthorized access, manipulation, and breach of confidentiality, there is an urgent demand for a secure and efficient data-sharing platform. Traditional architecture predominantly focuses on the aviation and automotive sectors, leaving the implementation of AI in railway networks relatively unexplored despite their essential role in urban and inter-city transportation infrastructure. This paper presents a blockchain-based system for secure data sharing in railway networks. This framework implements ciphertext-policy attribute-based encryption (CP-ABE) and an Intel SGX-based trusted execution environment to manage and prevent unauthorized data access. The proposed system implements smart contracts to facilitate secure data sharing and access control, thereby enhancing railway data security. Our experimental evaluation reveals that the proposed system improves efficiency, authentication speed, and cost-effectiveness. The results show a 27% reduction in computational cost, a 4% decrease in authentication time, an 8% improvement in peer-to-peer connection establishment, and a 23% decline in data sharing expenses. These improvements strike a balance between security, efficiency, and accessibility, thereby optimizing real-time railway operations.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13635-13647"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0