IEEE Transactions on Intelligent Transportation Systems最新文献

{"title":"Trajectory Map-Matching in Urban Road Networks Based on RSS Measurements","authors":"Zheng Xing;Weibing Zhao","doi":"10.1109/TITS.2025.3544399","DOIUrl":"https://doi.org/10.1109/TITS.2025.3544399","url":null,"abstract":"The widespread deployment of wireless communication networks has catalyzed significant advancements in utilizing signal channs to address real-world challenges, such as vehicle trajectory reconstruction (VTR), drone trajectory planning, and network optimization. Existing methods primarily utilize time-difference-of-arrival (TDoA) measurements for vehicle localization. However, these methods require specialized decoding receivers capable of deciphering communication protocols, leading to increased application costs. received signal strength (RSS), a measure of wireless signal strength, can be recorded by any standard communication device, thus allowing RSS-based VTR to benefit from cost-effectiveness. Nevertheless, the inherently noisy and sporadic nature of RSS poses significant challenges for accurately reconstructing vehicle trajectories. This paper aims to utilize RSS measurements to reconstruct vehicle trajectories within a road network. We constrain the trajectories to comply with signal propagation rules and vehicle mobility constraints, thereby mitigating the impact of the noisy and sporadic nature of RSS data on the accuracy of trajectory reconstruction. The primary challenge involves exploiting latent spatial-temporal correlations within the noisy and sporadic RSS data while navigating the complex road network. To overcome these challenges, we develop an hidden Markov model (HMM)-based RSS embedding (HRE) technique that utilizes alternating optimization to search for the vehicle trajectory based on RSS measurements. This model effectively captures the spatial-temporal relationships among RSS measurements, while a road graph model ensures compliance with network pathways. Additionally, we introduce a maximum speed-constrained rough trajectory estimation (MSR) method to effectively guide the proposed alternating optimization procedure, ensuring that the proposed HRE method rapidly converges to a favorable local solution. The proposed method is validated using real RSS measurements from 5G NR networks in Chengdu and Shenzhen, China. The experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art methods, even with limited RSS data.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4647-4660"},"PeriodicalIF":7.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726577","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}

{"title":"Green Micro-Grid for Railway Infrastructure","authors":"Hui Deng;Yumei Li;Chao Tang;Jie Liu;Qiang Yi;Yuan Wang;Ping Wang;Mingyuan Gao","doi":"10.1109/TITS.2025.3544264","DOIUrl":"https://doi.org/10.1109/TITS.2025.3544264","url":null,"abstract":"Rail transit has now been widely popularized, with the demand for the electrical energy required for railway operations showing a significant upward trend. Traditional power supply systems face challenges meeting railway operations’ continuously expanding energy consumption and the global vision for green, low-carbon transportation. Therefore, green microgrids are proposed to be integrated with them, constructing a green microgrid structure for the rail transit system to enhance its safety and stability. This paper discusses the structure of green microgrids and conducts a comprehensive review and comparative analysis of various green energy sources, energy storage systems, and advanced control strategies in green microgrids. The application of microgrids in rail systems is forward-looking, and future rail transit is expected to achieve a deep integration of self-sufficiency in energy, intelligent scheduling, and sustainable development.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4346-4364"},"PeriodicalIF":7.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735395","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}

{"title":"Vehicle-Level Fairness-Oriented Constrained Multi-Agent Reinforcement Learning for Adaptive Traffic Signal Control","authors":"Wanting Liu;Chengwei Zhang;Wanqing Fang;Kailing Zhou;Yihong Li;Furui Zhan;Qi Wang;Wanli Xue;Rong Chen","doi":"10.1109/TITS.2025.3544223","DOIUrl":"https://doi.org/10.1109/TITS.2025.3544223","url":null,"abstract":"Multi-agent Reinforcement Learning (MARL) has shown considerable promise in enhancing the efficiency of adaptive traffic signal control (ATSC) systems. However, existing MARL approaches primarily focus on optimizing overall traffic flow, often overlooking the issue of fairness in vehicle waiting times. Considering that there is no need to strive for the ultimate fairness, this paper models the ATSC problem as a Constrained Partially Observable Markov Game (CPOMG), where fairness is modeled as a constraint on the maximum waiting time of vehicles on lanes of intersections instead of a reward term that pursues maximization. CPOMG aims to find a cooperative control policy with optimal traffic efficiency within the constrained solution space by multiple agents. On this basis, this paper proposes a new centralized training and decentralized execution cooperative MARL method, i.e., vehicle-level fairness multi-agent proximity policy optimization (VF-MAPPO). VF-MAPPO leverages a centralized trained global Critic Network to estimate the average vehicle traffic efficiency and vehicle maximum waiting time, and an Actor Network shared by all intersections for decentralized execution, which converts the optimization problem with constraints to an unconstrained optimization objective through the Lagrange multiplier method and adopts proximity policy optimization during training. Additionally, VF-MAPPO incorporates spatial-temporal graph attention in the Critic network to efficiently extract state representations in multi-intersection environments. We qualitatively analyzed the monotonic improvement guarantee of VF-MAPPO. Extensive experimental validation across two real-world and one synthetic scenarios substantiates that VF-MAPPO enhances vehicle-level fairness and maintains average traffic efficiency, surpassing state-of-the-art methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4878-4890"},"PeriodicalIF":7.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726578","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}

{"title":"FEMASF: An SVD-Based Algorithm for Accurately Estimating the Mounting Angle and Scale Factor","authors":"Bokun Ning;Haiyong Luo;Linfeng Bao;Fang Zhao;Fan Wu","doi":"10.1109/TITS.2025.3543255","DOIUrl":"https://doi.org/10.1109/TITS.2025.3543255","url":null,"abstract":"Accurately estimating the position and attitude of vehicles is essential for intelligent transportation systems. The GNSS/INS integrated system offers precise navigation information. However, the system’s positioning errors may accumulate rapidly in challenging GNSS signal conditions. Odometer (ODO) and nonholonomic constraints (NHC) are commonly employed to mitigate the rapid accumulation of INS errors. Compensating for the mounting angles of INS and the scale factor of the odometer is necessary to fully exploit the potential of ODO/NHC. However, many studies employ Kalman filters with small mounting angle assumption, which limits their applicability for large mounting angles in practice. To accurately estimate the mounting angle of INS with any installation attitude, we propose a new algorithm called Fast Estimation of Mounting Angle and Scale Factor (FEMASF). FEMASF employs Singular Value Decomposition (SVD) to obtain a closed-form solution for the parameters. It also incorporates an enhanced Sage-Husa scheme, enhancing overall estimation accuracy by reducing the weight of outlier data through a forgetting factor. Extensive simulation experimental results demonstrate that our proposed FEMASF algorithm outperforms filter-based methods in terms of accuracy and convergence speed for large mounting angles. Specifically, for the −90° mounting angle, FEMASF achieves 0.45° angle error, while the velocity-based Kalman filter (VKF) fails to converge and the position-based Kalman filter (PKF) yields about 4° error. Furthermore, neither VKF nor PKF converges for the 120° mounting angle, whereas FEMASF exhibits only about 3.2° estimation error.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4504-4516"},"PeriodicalIF":7.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735349","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}

{"title":"Vision-Based Geometric Model for Accurate and Fast Lane Recognition in Complex Conditions","authors":"Ying Zhang;Haoran Qi;Shuaishuai Ge;Tingyi Zhao;Jinchao Chen;Tao You;Yantao Lu;Chenglie Du","doi":"10.1109/TITS.2025.3543809","DOIUrl":"https://doi.org/10.1109/TITS.2025.3543809","url":null,"abstract":"Lane recognition is an important component of autonomous driving system and advanced driving assistance system (ADAS) for intelligent vehicles. In complex driving conditions, accurate and fast lane recognition is a challenging issue. In this paper, a vision-based geometric model (VBGM) is proposed for accurate and fast lane recognition in complex conditions. The framework of the VBGM includes an image preprocessing stage and a lane recognition stage. In the image preprocessing stage, the region of interest (ROI) is extracted from the original image, and the original image is transformed into an undistorted greyscale image. In the lane recognition stage, the lane contour is first extracted using the Roberts operator. Then, to accurately and quickly recognize the lane marking, a lane recognition coordinate system (LRCS) and a rotational LRCS (R-LRCS) are constructed. The distracting contours in abnormal regions are padded based on the LRCS using a contextual frames correlation (CFC) strategy, and the midpoints of the lane contour are identified based on the R-LRCS. Finally, an adaptive-order polynomial fitting model is built to fit the lane marking according to the midpoints in the LRCS. To evaluate the effectiveness of the proposed method, two state-of-the-art methods are selected for comparison. The comparative results indicate that the proposed method possesses a higher recognition rate and speed for lane recognition in complex conditions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4692-4704"},"PeriodicalIF":7.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725113","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}

{"title":"Unsupervised Learning of 3D Scene Flow With LiDAR Odometry Assistance","authors":"Guangming Wang;Zhiheng Feng;Chaokang Jiang;Jiuming Liu;Hesheng Wang","doi":"10.1109/TITS.2025.3538765","DOIUrl":"https://doi.org/10.1109/TITS.2025.3538765","url":null,"abstract":"3D scene flow represents the 3D motion of each point in the point cloud, which is a base 3D perception task for autonomous driving, like optical flow for 2D images. As non-learning methods are often inefficient or struggled to learn accurate correspondence in complex 3D real world, recent works turn to supervised learning methods, which require ground truth labels. However, acquiring the ground truth of 3D scene flow is challenging mainly due to the lack of sensors capable of capturing point-level motion and the complexity of accurately tracking each point in real-world environments. Therefore, it is important to resort to self-supervised methods, which do not require ground truth labels. In this paper, a novel unsupervised learning method of scene flow with LiDAR odometry is proposed, which enables the scene flow network can be trained directly on real-world LiDAR data without scene flow labels. In this structure, supervised odometry provides a more accurate shared cost volume for the interframe association of 3D scene flow. In addition, because static and occluded points are more suitable for using the pose transform while dynamic and non-occluded points are more suitable for using the scene flow transform, a static mask and an occlusion mask are designed to classify the states of points and a mask-weighted warp layer is proposed to transform source points in a divide-and-conquer manner. The experiments demonstrate that the divide-and-conquer strategy makes the predicted scene flow more accurate. The experiment results compared to other methods also show the application ability of our proposed method to real-world data. Our source codes are released at: <uri>https://github.com/IRMVLab/PSFNet</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4557-4567"},"PeriodicalIF":7.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735401","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}

{"title":"Dynamic Spatio-Temporal Planning Strategy of EV Charging Stations and DGs Using GCNN-Based Predicted Power Demand","authors":"Shahriar Rahman Fahim;Rachad Atat;Cihat Kececi;Abdulrahman Takiddin;Muhammad Ismail;Katherine R. Davis;Erchin Serpedin","doi":"10.1109/TITS.2025.3541190","DOIUrl":"https://doi.org/10.1109/TITS.2025.3541190","url":null,"abstract":"As a sustainable participant in the modernization of transportation systems, electric vehicles (EVs) call for a well-planned charging infrastructure. To meet the ever-increasing charging demands of EVs, an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs) is necessary. With newly allocated CSs, additional distributed generators (DGs) are required to compensate for the load increase. Given a budget to be allocated over a certain time horizon, we formulate the joint spatio-temporal CSs and DGs planning problem as a multi-objective optimization problem. During each planning period, the allocation strategy aims at minimizing the total power generation costs and CSs/DGs installation costs while satisfying budgetary and power constraints and ensuring a minimum level for the charging requests satisfaction rate. In this regard, we first predict the future power demand of EVs using a graph convolutional neural network (GCNN). Then, using the power demand forecast, we obtain the optimal number and locations of CSs and DGs at each time stage using reinforcement learning. A case study of the proposed allocation strategy over 6 time stages for the 2000-bus power grid of Texas coupled with 720 initially existing CSs is presented to illustrate the performance of the planning strategy.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4528-4542"},"PeriodicalIF":7.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735379","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}

{"title":"Car Damage Detection Based on Multi-View Fusion and Alignment: Dataset and Method","authors":"Jinbo Peng;Shoubin Dong;Hua Yuan;Xiaorou Zheng","doi":"10.1109/TITS.2025.3542174","DOIUrl":"https://doi.org/10.1109/TITS.2025.3542174","url":null,"abstract":"Traffic accidents remain a significant concern due to their potential severity and impact on society. The rapid and accurate detection of car damage is increasingly crucial. Manual assessment of car damage usually relies on multi-view car images taken at the scene, which can provide richer information for damage assessment. However, most car damage algorithms are based on single-view datasets, and it is hard to fully leverage the complementary information and alignment information between distant view and close-up images. In this paper, we propose the Multi-View Car Damage Detection model (MVA-CDD), comprising three key modules: Feature Split (FS), Feature Fusion (FF), and Image Alignment (IA). The FS module extracts global and detailed information from distant-view and close-up images separately, which are then combined by the FF module. The IA module effectively aligns car damage information in distant-view and close-up images to correct errors and biases. Meanwhile, we created the new Car Damage Detection Multi-view dataset (CDDM), which has a significant advantage in both image quantity and diversity across categories, addressing the shortcomings of existing multi-view datasets. Our proposed MVA-CDD outperforms the state-of-the-art single-view and multi-view models with the dataset. Results from ablation studies further confirm the efficiency of MVA-CDD. This study contributes to optimizing the car damage detection and claims adjudication process, leading to significant labor and material cost savings. CDDM dataset is available at <uri>https://github.com/SCUT-CCNL/CDDM</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4717-4730"},"PeriodicalIF":7.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726459","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}

{"title":"Lane Change Prediction for Autonomous Driving With Transferred Trajectory Interaction","authors":"Yuhuan Lu;Pengpeng Xu;Xinyu Jiang;Ali Kashif Bashir;Thippa Reddy Gadekallu;Wei Wang;Xiping Hu","doi":"10.1109/TITS.2025.3542517","DOIUrl":"https://doi.org/10.1109/TITS.2025.3542517","url":null,"abstract":"In mixed-autonomy traffic environments, accurately predicting the lane change behavior of human-driven vehicles is critical for ensuring the safety and reliability of autonomous vehicle decision-making. However, existing approaches face two major challenges: 1) they tend to represent the relationships between the target vehicle and surrounding vehicles using parameters like relative position and speed. This approach either requires a fixed number of surrounding vehicles or introduces significant noise by relying on virtual vehicles; and 2) they often fail to fully exploit the vast amount of available vehicle trajectory data, leaving the complexities of vehicular interactions underexplored. To address these issues, this paper presents a novel lane change prediction framework using Transformer-based transfer learning. Our design aims to leverage inter-vehicle interactions learned from trajectory data to improve lane-change prediction accuracy. Specifically, pre-trained trajectory prediction models are used to adapt dynamically to the varying number of surrounding vehicles and to capture interaction context from large sets of trajectory data. We then refine the Transformer model to integrate this context and predict the target vehicle’s lane change intentions. The Transformer encoder transforms trajectory interaction context into a lane-change-oriented context using aggregated multi-head attention. The Transformer decoder, in turn, utilizes this context alongside the target vehicle’s states through relation-aware multi-head attention to forecast lane change behavior. Extensive experiments on two real-world datasets demonstrate that our proposed framework outperforms state-of-the-art baselines in both accuracy and robustness.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4543-4556"},"PeriodicalIF":7.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735394","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}

{"title":"Reliability-Based Equilibrium Model Considering Promotive Impacts of Connected and Autonomous Vehicles on Traffic Flow Stability in a Mixed Traffic Network","authors":"Wei Wu;Min Wang;Bing Zeng;Wei Hao;George Giannopoulos","doi":"10.1109/TITS.2025.3541680","DOIUrl":"https://doi.org/10.1109/TITS.2025.3541680","url":null,"abstract":"Existing studies on traffic flow stability primarily focused on local stability, with little attention given to its extension to the network level, known as network stability. In this paper, a reliability-based equilibrium model in a mixed traffic network including human-driven vehicles and connected and autonomous vehicles is developed to analyze the impact of connected and autonomous vehicles on traffic flow stability. The basic characteristics of the model are first examined on a small network, demonstrating the non-uniqueness of the link flow in the user equilibrium pattern. Then, the model is extended to the case of a general network with Variational Inequality (VI) equations. In addition, a two-level optimization strategy is developed by incorporating the pricing and quantity control strategies to the reliability model. Numerical examples are conducted based on Sioux Falls networks to examine the performance of the proposed models.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4391-4405"},"PeriodicalIF":7.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735348","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}