IEEE Transactions on Intelligent Transportation Systems最新文献_第5页

Model Predictive Control for On-Ramp Vehicle Merging to a Platoon on Main Road in Finite Time

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-24 DOI: 10.1109/TITS.2025.3541955

Zhiwen Qiang;Li Dai;Boli Chen;Yuanqing Xia

{"title":"Model Predictive Control for On-Ramp Vehicle Merging to a Platoon on Main Road in Finite Time","authors":"Zhiwen Qiang;Li Dai;Boli Chen;Yuanqing Xia","doi":"10.1109/TITS.2025.3541955","DOIUrl":"https://doi.org/10.1109/TITS.2025.3541955","url":null,"abstract":"This paper addresses the longitudinal control problem of an on-ramp vehicle merging into a platoon on the main road. To tackle this challenge, a finite-time model predictive control (MPC) algorithm with a specialized feedback control law is proposed. A constraint set of the state error is designed and based on this, a decision-making scheme is established to allow the on-ramp vehicle to assess the feasibility of the merging operation at the beginning under the designed MPC strategy. If the merging is feasible, the proposed MPC strategy will be applied to drive the on-ramp vehicle towards a small neighborhood around the desired state on the basis of platoon’s velocity and position within a finite time step before joining the platoon. Furthermore, asymptotic convergence towards the desired state is achieved by a co-designed feedback control law. Otherwise, the MPC strategy will not be triggered, instead an alternative method such as slowing down the on-ramp vehicle to create space and allow the vehicles on the main road to proceed ahead. Under the proposed method, the recursive feasibility of the MPC optimization problem is achieved at all time steps and the finite time convergence to the small neighborhood of the desired state can be proved under the MPC algorithm. An upper bound on the convergence time step is also derived, which is used to prove the effectiveness of the decision-making mechanism. In addition, the closed-loop constraints satisfaction and asymptotic stability of the on-ramp vehicle are also guaranteed. The effectiveness of the proposed MPC method is demonstrated through simulation examples.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4731-4743"},"PeriodicalIF":7.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725115","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

Identification of Out-of-the-Normal Driving Behaviors Using Instantaneous Driving Decisions—A Case-Study on Indian Drivers

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-24 DOI: 10.1109/TITS.2025.3541093

Jahnavi Yarlagadda;Digvijay Sampatrao Pawar

{"title":"Identification of Out-of-the-Normal Driving Behaviors Using Instantaneous Driving Decisions—A Case-Study on Indian Drivers","authors":"Jahnavi Yarlagadda;Digvijay Sampatrao Pawar","doi":"10.1109/TITS.2025.3541093","DOIUrl":"https://doi.org/10.1109/TITS.2025.3541093","url":null,"abstract":"Understanding the indigenous driving styles of individuals is significant to avoid oversighting the behavioral generalization across varying geographical locations. The past research on driving style classification mostly focused on identifying the driving patterns using the kinematic feature magnitudes. The variation in the instantaneous driving decisions termed as “driving volatility” is not explored in the context of performance assessment. In this regard, the present study proposes a methodology to explore the driving styles of Indian drivers, using both the magnitude and variation exhibited in the short-term driving decisions. The real-time driving profiles of 47 professional car drivers were collected and segmented into maneuvers based on the respective driving regimes. The performance features representative of each maneuver are extracted, defining 12 measures of driving volatility. The K-means clustering was performed on the event dataset at two-levels, which resulted in four patterns of driving styles under acceleration and braking regimes. The results showed that, a driver can exhibit speedy and aggressive maneuvers in a stable as well as in a highly volatile pattern. The generated driving style profiles at individual-level highlight the behavioral changes in drivers pertained to external influencing factors, and helps to identify the aberrations performed in each trip.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4443-4456"},"PeriodicalIF":7.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735476","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 Systematic Survey of Digital Twin Applications: Transferring Knowledge From Automotive and Aviation to Maritime Industry

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-24 DOI: 10.1109/TITS.2025.3535593

Runze Mao;Yuanjiang Li;Guoyuan Li;Hans Petter Hildre;Houxiang Zhang

{"title":"A Systematic Survey of Digital Twin Applications: Transferring Knowledge From Automotive and Aviation to Maritime Industry","authors":"Runze Mao;Yuanjiang Li;Guoyuan Li;Hans Petter Hildre;Houxiang Zhang","doi":"10.1109/TITS.2025.3535593","DOIUrl":"https://doi.org/10.1109/TITS.2025.3535593","url":null,"abstract":"Digital twin (DT) technology, which creates virtual representations of physical systems to optimize their life-cycle, has drawn significant attention across various industries. The automotive and aviation industries have been pioneers in adopting DTs for enhanced efficiency, predictive maintenance, and real-time decision-making. However, the maritime industry, crucial to global trade and logistics, has lagged in DT implementation. This paper aims to bridge this gap by systematically surveying DT applications in the automotive and aviation industries and exploring how this knowledge can be transferred to the maritime industry. By analyzing existing literature, identifying key trends, and summarizing best practices, a comprehensive roadmap is provided for maritime industry adoption of DT technology. The surveyed papers are selected systematically following the PRISMA statement and categorized based on characteristics such as single vs. multiple systems, modeling methods (model-driven, data-driven, and hybrid), and life-cycle phases. We introduce DT models using a five-dimensional framework and analyze their characteristics in terms of research object, subsystem application, and modeling method. Additionally, DT applications from a product life-cycle perspective, covering design, manufacturing, operation, and maintenance phases are examined. Knowledge transfer from the automotive and aviation industries to the maritime industry is summarized. In the automotive industry, DTs enhance vehicle efficiency and safety, particularly for autonomous and electric vehicles. Aviation DT research focuses on predictive maintenance, pilot training, and real-time monitoring to improve operational efficiency and safety. The maritime industry faces data challenges and operational complexity but has significant potential for DTs to enhance ship performance, safety, and predictive maintenance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4240-4259"},"PeriodicalIF":7.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735339","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

Motion Estimation and Reconstruction for 360° Panoramic Images Based on Variant Goldberg Polyhedral Projection

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-24 DOI: 10.1109/TITS.2025.3541062

Hao Xu;Xixiang Liu;Ye Liu;Xiang Song

{"title":"Motion Estimation and Reconstruction for 360° Panoramic Images Based on Variant Goldberg Polyhedral Projection","authors":"Hao Xu;Xixiang Liu;Ye Liu;Xiang Song","doi":"10.1109/TITS.2025.3541062","DOIUrl":"https://doi.org/10.1109/TITS.2025.3541062","url":null,"abstract":"The effectiveness of transportation systems is significantly influenced by the field of view (FOV) of cameras, with larger FOVs yielding more reliable performance. Conventional cameras with restricted FOV face difficulties in scenarios characterized by field-of-view shake or low-texture imaging areas, complicating feature extraction and matching tracking. Conversely, 360° panoramic cameras, which provide an expansive field of view and high pixel density, are emerging as promising alternatives. While many studies have explored motion calculation based on panoramic vision, most resort to conventional image processing methods for panoramic images, leading to inefficiencies and reduced accuracy. This article proposes a novel approach to panoramic vision motion estimation and reconstruction. The method involves projecting the panoramic image onto a fitting sphere and establishing a mapping relationship between a uniform spherical grid and the pixels of the panoramic image. This enables distortion-free mapping imaging for the entire panorama, addressing issues with matching consecutive frame images caused by distortion in the polar regions. Going beyond conventional epipolar constraints, this paper introduces a geometric constraint of epipolarity within a three-dimensional sphere and derives pose-solving equations tailored for panoramic vision, enabling motion estimation between frames. Additionally, it derives the polar arc equation of panoramic images, accelerating three-dimensional reconstruction of dense point clouds. The sliding window estimation method is used for iterative algorithm optimization, along with an optimization mechanism aligned with the panoramic camera model. Validation on public datasets and hardware experimental platforms demonstrates the proposed method’s significantly improved accuracy compared to existing optimal algorithms.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4891-4907"},"PeriodicalIF":7.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726446","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

M-SOS: Mobility-Aware Secured Offloading and Scheduling in Dew-Enabled Vehicular Fog of Things

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-21 DOI: 10.1109/TITS.2025.3539839

Goluguri N. V. Rajareddy;Kaushik Mishra;Santosh Kumar Majhi;Kshira Sagar Sahoo;Muhammad Bilal

{"title":"M-SOS: Mobility-Aware Secured Offloading and Scheduling in Dew-Enabled Vehicular Fog of Things","authors":"Goluguri N. V. Rajareddy;Kaushik Mishra;Santosh Kumar Majhi;Kshira Sagar Sahoo;Muhammad Bilal","doi":"10.1109/TITS.2025.3539839","DOIUrl":"https://doi.org/10.1109/TITS.2025.3539839","url":null,"abstract":"The gradual advancement of Internet-connected vehicles has transformed roads and highways into an intelligent ecosystem. This advancement has led to a widespread adoption of vehicular networks, driven by the enhanced capabilities of automobiles. However, managing mobility-aware computations, ensuring network security amidst instability, and overcoming resource constraints pose significant challenges in heterogeneous vehicular network applications within Fog computing. Moreover, the latency overhead remains a critical issue for tasks sensitive to latency and deadlines. The objective of this research is to develop a Mobility-aware Secured offloading and Scheduling (M-SOS) technique for a Dew-enabled vehicular Fog-Cloud computing system. This technique aims to address the issues outlined above by moving the computations closer to the edge of the network. Initially, a Dew-facilitated vehicular Fog network is proposed, leveraging heterogeneous computing nodes to handle diverse vehicular requests efficiently and ensuring uninterrupted services within the vehicular network. Further, task management is optimized using a Fuzzy logic that categorizes tasks based on their specific requirements and identifies the target layers for offloading. Besides, a cryptographic algorithm known as SHA-256 RSA enhances security. Moreover, a novel Linear Weight-based JAYA scheduling algorithm is introduced to assign tasks to appropriate computing nodes. The proposed algorithm surpasses the comparable algorithms by 23% in terms of AWT, 18% in terms of latency rate, 14% and 23% in terms of meeting the hard-deadline (<inline-formula> <tex-math>$H_d$ </tex-math></inline-formula>) and soft-deadline (<inline-formula> <tex-math>$S_d$ </tex-math></inline-formula>), and 35% in terms of average system cost, respectively.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4851-4864"},"PeriodicalIF":7.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726415","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

Co-MOT: Exploring the Collaborative Relations in Traffic Flow for 3D Multi-Object Tracking

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-21 DOI: 10.1109/TITS.2025.3542269

Ye Liu;Xingdi Liu;Zhongbin Jiang;Jun Liu

{"title":"Co-MOT: Exploring the Collaborative Relations in Traffic Flow for 3D Multi-Object Tracking","authors":"Ye Liu;Xingdi Liu;Zhongbin Jiang;Jun Liu","doi":"10.1109/TITS.2025.3542269","DOIUrl":"https://doi.org/10.1109/TITS.2025.3542269","url":null,"abstract":"In real-world scenes, vehicles and pedestrians on the road often exhibit consistency in their overall motion, forming the traffic flow we observe. Exploring this global collective motion consistency to aid in 3D multi-object tracking (MOT) tasks is an under-investigated issue in existing research. Recently, Graph Neural Networks (GNN) have been introduced to model interactions between targets in 3D tracking problems, achieving remarkable performance. However, existing GNN based methods usually employ neighborhood-based approaches to construct graphs which are unable to fully exploit collective relations in traffic flow. In this paper, we propose a GNN based 3D MOT method which effectively utilizes the collective motion consistency in traffic flow. Collective motion is modeled with a densely connected intra-flow graph within the collective group, allowing information to flow quickly. To build the intra-flow graph, we propose an effective collinearity condition to distinguish potential collective groups from the detected objects. For reasoning on the graph, we propose a progressive serial message-passing solver which enables the network to learn complex group movement relationships based on a thorough understanding of simple neighborhood relations. Our proposed method achieves state-of-the-art performance on public datasets: NuScenes and KITTI tracking benchmark. We have conducted extensive experiments to evaluate the comprehensive performance of our proposed method which demonstrates the effectiveness of the proposed method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4744-4756"},"PeriodicalIF":7.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724055","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

Expressway Traffic Trajectory Recognition on DAS Vibration Spatiotemporal Images

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-21 DOI: 10.1109/TITS.2025.3540540

Wenting Zhang;Chuanling Li;Zhenyu Qi;Qijiu Xia;Kun Li;Yu Kang;Wenjun Lv;Ji Chang

{"title":"Expressway Traffic Trajectory Recognition on DAS Vibration Spatiotemporal Images","authors":"Wenting Zhang;Chuanling Li;Zhenyu Qi;Qijiu Xia;Kun Li;Yu Kang;Wenjun Lv;Ji Chang","doi":"10.1109/TITS.2025.3540540","DOIUrl":"https://doi.org/10.1109/TITS.2025.3540540","url":null,"abstract":"Distributed Acoustic Sensing (DAS) can capture spatio-temporal vibration images of vehicles on expressways, which can be utilized for traffic monitoring. Compared to ubiquitously deployed cameras, DAS traffic monitoring offers advantages such as full coverage, resistance to environmental interference, low computational requirements, and cost-effectiveness. However, real-world complexities result in challenges for DAS traffic images, including low signal-to-noise ratio, signal missing, and uneven intensity. As DAS traffic applications are still in their early stages, effective solutions to these challenges are yet to be developed. This paper proposes a new deep learning method named DAS High Speed Traffic Trajectory (DAS-HTT) network, which contributes threefold: (i) Multi-Scale Context Extraction Module (MSCE) effectively enlarges the receptive field to capture long-range contextual information comprehensively; (ii) Stripe Convolution Decoder (SCD) acquires remote information along four directions, preventing irrelevant region interference in feature learning; (iii) Hierarchically Hough Transform Fusion Decoder (HHTFD) introduces the structural information of trajectory linearity, reducing the reliance on label data while enhancing trajectory continuity. We conducted experiments on an operating expressway, demonstrating that DAS-HTT outperforms existing methods across seven metrics, providing trajectories that are more consistent with ground truthes.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5120-5134"},"PeriodicalIF":7.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740345","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

Unsupervised Learning Approach for Risky Driving Behavior Identification on Expressways in C-ITS Environments

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-21 DOI: 10.1109/TITS.2025.3538929

Dongmin Kim;Hwanpil Lee;Jooyoung Lee

{"title":"Unsupervised Learning Approach for Risky Driving Behavior Identification on Expressways in C-ITS Environments","authors":"Dongmin Kim;Hwanpil Lee;Jooyoung Lee","doi":"10.1109/TITS.2025.3538929","DOIUrl":"https://doi.org/10.1109/TITS.2025.3538929","url":null,"abstract":"Understanding the intricate relationship between driving behaviors, traffic crashes, and human factors is paramount in enhancing road safety. Human error, often stemming from risky driving behaviors, contributes significantly to traffic crashes. Identifying and mitigating these behaviors through advanced technologies and data analysis has become an important concern in the field of traffic safety management. This study introduces an unsupervised learning algorithm for detecting risky driving behaviors on expressways within Cooperative Intelligent Transport Systems (C-ITS) environments, employing deep clustering techniques to analyze individual driving patterns from Probe Vehicle Data (PVD). Utilizing data from 116 vehicles, including buses and heavy trucks, a Convolutional Neural Network (CNN)-based autoencoder was employed to extract latent hierarchical features, facilitating the clustering of similar driving patterns. Elementary Driving Behaviors (EDBs) were identified for different vehicle types and driving statuses, serving as a foundation for detecting risky driving behaviors against the proposed criteria. The research revealed a clear positive correlation between detected risky driving behaviors and traffic crashes across the vehicle types. Furthermore, when comparing our model’s criteria with traditional safety indexes, our proposed model demonstrated stronger correlations with traffic crashes, indicating its effectiveness in expressway driving environments. This research not only introduces a novel method for identifying risky driving behaviors but also underscores the importance of tailored traffic safety interventions in enhancing C-ITS environments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5146-5155"},"PeriodicalIF":7.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740261","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

Robust Optimal Prescribed Performance Control of Adaptive Cruise Control Systems With Unknown Dynamics

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-21 DOI: 10.1109/TITS.2025.3538107

Jun Zhao;Zhangu Wang;Yongfeng Lv;Congzhi Liu;Ziliang Zhao

{"title":"Robust Optimal Prescribed Performance Control of Adaptive Cruise Control Systems With Unknown Dynamics","authors":"Jun Zhao;Zhangu Wang;Yongfeng Lv;Congzhi Liu;Ziliang Zhao","doi":"10.1109/TITS.2025.3538107","DOIUrl":"https://doi.org/10.1109/TITS.2025.3538107","url":null,"abstract":"Conventional ACC method has great fluctuation and deviation when solving speed and distance control problems. Thus, this paper develops a reinforcement learning (RL) based robust optimal prescribed performance controller for ACC systems. To this end, we first construct a continuous time ACC system with unknown system dynamics (e.g., target vehicle acceleration, sensor and actuator attacks, etc). To estimate the unknown system dynamics, an unknown system dynamic estimator (USDE) is designed, where the unknown system dynamic can be accurately estimated by using the input-output information, this is helpful for controller design. Then, a RL based optimal control method is developed, where the prescribed performance function (PPF) is applied, the system states can be effectively defined within a certain range. To realize the online solution for optimal control, we design a new adaptive law based on the adaptive dynamic programming (ADP) framework to online learn the critic neural network (NN) weights, because of the strong convergence, the proposed learning algorithm can be effectively applied in practical industrial systems. Finally, the efficacy of the proposed control technique is tested through simulations and experiments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4757-4769"},"PeriodicalIF":7.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726428","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

Wheel Slip Control Algorithms for Improving Adhesion Performance of Electric Locomotives

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-20 DOI: 10.1109/TITS.2025.3540607

Xinru Guo;Yunfan Yang;Liang Ling;Wanming Zhai

{"title":"Wheel Slip Control Algorithms for Improving Adhesion Performance of Electric Locomotives","authors":"Xinru Guo;Yunfan Yang;Liang Ling;Wanming Zhai","doi":"10.1109/TITS.2025.3540607","DOIUrl":"https://doi.org/10.1109/TITS.2025.3540607","url":null,"abstract":"Low-friction surface conditions significantly contribute to the reduction of the wheel/rail adhesion capability and the occurrence of wheel/rail slipping behaviors, which may lead to the degradation of mechanical properties and frictional wear damage at the wheel/rail interface. To mitigate these undesirable consequences, modern railway locomotives are equipped with on-board anti-slip control systems. In this study, three different anti-slip controller models, comprising the traditional re-adhesion anti-slip controller and PID-based anti-slip controller with fixed threshold and with optimal threshold, are established. The wheel/rail rolling-slipping performances subjected to different anti-slip control algorithms under changing wheel/rail friction conditions are compared based on train-track interaction simulations. The results demonstrate that the PID-based anti-slip controller with an optimal threshold achieves the maximum utilization of wheel/rail adhesion in the presence of low-friction conditions, outperforming the other two types of anti-slip controllers. Additionally, the adoption of an anti-slip controller with a lower control threshold can effectively reduce the tread wear of locomotive wheels. This research can provide a deep going understanding of optimization design of anti-slip controller on railway vehicles.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4592-4605"},"PeriodicalIF":7.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726351","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