IEEE Transactions on Intelligent Transportation Systems最新文献

{"title":"DBKE: Design of Blockchain-Envisioned Vehicle-to-Vehicle Secure Key Management Protocol Using ECC","authors":"Sanjeev Kumar Dwivedi;Ruhul Amin;Muhammad Khurram Khan;Ashok Kumar Das;Adesh Pandey;Md Abdul Saifulla","doi":"10.1109/TITS.2025.3572305","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572305","url":null,"abstract":"Vehicle-to-vehicle (V2V) authentication is essential in the Vehicular Ad-hoc Network (VANET). V2V communication improves the safety of drivers and assists them in making the appropriate decision according to road conditions. Since V2V communication happens in open public channels, an adversary takes advantage of it and tries to launch several potential threats. This article designs a blockchain-assisted V2V communication and authentication scheme using Elliptic Curve Cryptography (ECC) and a Physically Unclonable Function (PUF) called DBKE. In DBKE, vehicles perform their registration with their nearest Roadside Unit (RSU) without assisting the centralized cloud server. Then, one vehicle can communicate with another using the information stored in the blockchain. During the communication, both vehicles first perform the process of mutual authentication and then securely generate the session keys without sending the private parameters to the public channels. The formal analysis of DBKE is done with the Scyther and Real-or-Random (ROR) models, which confirm that the DBKE is robust and safe from attacks. Furthermore, the detailed comparative study of the security features reveals that the DBKE scheme provides superior security and low computation cost compared to the existing V2V authentication protocols.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9293-9304"},"PeriodicalIF":7.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536601","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":"A Review of Computer Vision for Railways","authors":"Bryan Olivier;Feng Guo;Yu Qian;David P. Connolly","doi":"10.1109/TITS.2025.3552011","DOIUrl":"https://doi.org/10.1109/TITS.2025.3552011","url":null,"abstract":"Modern railways continue to strive for remote and automated methods to improve the visual inspection procedures for their assets. In some cases, these inspections provide new information that could not previously be collected, while in other cases they help them improve upon the quality control, safety, time and costs associated with manual inspection. As such, computer vision continues to find applications for visually inspecting the track, earthworks, tunnels, overhead line equipment and rolling stock. Considering the recent pace of computer vision related developments, this paper seeks to review the state of the art of the field for railways. First, the hardware and data requirements are discussed, focusing on the unique challenges associated with operating optical equipment in a railway environment, such as contamination, power sources and lighting. This also discusses the most common mounting arrangements for camera hardware, including rolling-stock, satellites and way-side cameras. Next, image processing algorithms are discussed, comparing classical approaches and more modern artificial intelligence approaches, for example You Only Look Once (YOLO) and Region-Based Convolutional Neural Network (R-CNN). Then the most common applications for computer vision in the rail industry are analysed. First the track is studied considering computer vision analysis for the detection of different types of rail surface defects on plain line and turnouts, fastener defects, concrete track slab cracking and ballast particle characterisation. Next, the overhead line equipment is considered with applications related to detecting contact loss between pantograph and contact wire, stagger behaviour and defective catenary components. This is followed by discussion of other applications such as rail tunnelling subsidence, tunnel inspection, level crossings, trespass and on-track safety hazards. Finally, opportunities for future research are discussed such as hyperspectral imaging and generative AI, along with possible frontier technologies such as quantum computing.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"11034-11065"},"PeriodicalIF":7.9,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536423","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":"Transportation Analytics Using Smart Card Data: A Systematic Review","authors":"Leonardo Monteiro-Fialho;Enrique Cueto-Rubio;Carlos Granell;Sergio Trilles","doi":"10.1109/TITS.2025.3571101","DOIUrl":"https://doi.org/10.1109/TITS.2025.3571101","url":null,"abstract":"The widespread adoption of smart card technology in mobility services, particularly in public transportation, as well as in car and bike sharing systems, has opened up new avenues for transportation service providers to gain insights into passenger travel behaviors. Smart card data offers a rich source of information on passenger trips, enabling a wide range of transportation analytics applications, including operator performance monitoring, demand modeling, and travel behavior analysis. This systematic mapping review aims to comprehensively examine the current state-of-the-art in leveraging smart cards for analytical studies applied to public transportation research. The review focuses on identifying and analyzing the main analytical purposes, methods, techniques, datasets, and trends used in these studies. The findings of this review provide valuable insights into the current research landscape of smart card data for public transport and highlight potential knowledge gaps that warrant further research.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"11010-11033"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11023145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536396","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}

{"title":"Steering Motion Quality Oriented Return-to-Center Control Strategy for Vehicles","authors":"Yimeng Song;Xin Guan;Yuning Zhang;Pingping Lu;Chunguang Duan;Jun Zhan","doi":"10.1109/TITS.2025.3573780","DOIUrl":"https://doi.org/10.1109/TITS.2025.3573780","url":null,"abstract":"Most of existing steering system control strategies pay little attention to return time and speed. Additionally, most algorithms do not consider the impact of the applied torque of the driver on the steering wheel when determining the optimal return speed while the wheel is being held. This highlights an ongoing need to improve the return-to-center quality in vehicles. To address this challenge, this study proposes a steering motion quality control method based on a motion-closed loop. Here, we delineate the context wherein, by establishing a desired returnability, the return-to-center speed during release or holding phases is achieved via direct control through a motion closed-loop. This approach harmonizes the steering feel with the return-to-center performance, thereby enhancing the stability of the vehicle during straight-line driving. Subsequently, we validate the efficacy and robustness of the proposed steering motion quality-oriented vehicle return-to-center control strategy in managing returnability, as evidenced by low-speed and high-speed return-to-center simulation tests using a driving simulator.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9962-9978"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536426","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":"Semantic-MoSeg: Semantics-Assisted Moving-Obstacle Segmentation in Bird-Eye-View for Autonomous Driving","authors":"Shiyu Meng;Yuxiang Sun","doi":"10.1109/TITS.2025.3570058","DOIUrl":"https://doi.org/10.1109/TITS.2025.3570058","url":null,"abstract":"Bird-eye-view (BEV) perception for autonomous driving has become popular in recent years. Among various BEV perception tasks, moving-obstacle segmentation is very important, since it can provide necessary information for downstream tasks, such as motion planning and decision making, in dynamic traffic environments. Many existing methods segment moving obstacles with LiDAR point clouds. The point-wise segmentation results can be easily projected into BEV since point clouds are 3-D data. However, these methods could not produce dense 2-D BEV segmentation maps, because LiDAR point clouds are usually sparse. Moreover, 3-D LiDARs are still expensive to vehicles. To provide a solution to these issues, this paper proposes a semantics-assisted moving-obstacle segmentation network using only low-cost visual cameras to produce segmentation results in dense 2-D BEV maps. Our network takes as input visual images from six surrounding cameras as well as the corresponding semantic segmentation maps at the current and previous moments, and directly outputs the BEV map for the current moment. We also propose a movable-obstacle segmentation auxiliary task to provide semantic information to further benefit moving-obstacle segmentation. Extensive experimental results on the public nuScenes and Lyft datasets demonstrate the effectiveness and superiority of our network.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9251-9262"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536604","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":"Hierarchical Attention-Enhanced Correlation Refinement for Robust Visual Tracking","authors":"Si Chen;Rui Xu;Yan Yan;Yang Hua;Da-Han Wang;Shunzhi Zhu","doi":"10.1109/TITS.2025.3570076","DOIUrl":"https://doi.org/10.1109/TITS.2025.3570076","url":null,"abstract":"In recent years, visual tracking has witnessed remarkable advancements with the exploration of feature extraction and correlation modeling techniques. However, inadequate robustness of either the backbone network or the correlation operation continues to plague existing trackers, leading to frustrating drift when confronted with similar distractors or cluttered backgrounds. To address this problem, we propose a hierarchical attention-enhanced correlation refinement network (HarNet) for achieving robust visual tracking. Specifically, a gated dual-view attention (GDA) module is first designed to aggregate the intra-layer attention and the inter-layer self-attention based on a fusion gate, so as to enhance hierarchical feature representations of the template. Meanwhile, a target-aware attention (TA) module introduces the template information to the inter-layer self-attention, which can highlight the target information in the search region. Moreover, a graph guided correlation (GGC) module leverages the pixel-to-local and pixel-to-global correlations to fully exploit both local- and global-spatial information between the template and the search region, and then uses the graph convolutional network (GCN) to further learn the node relationships of the correlation map for more finegrained correlations. Thus, with the above three elaborately designed modules, the HarNet is beneficial for the enhancement of feature representation and the precise localization of the target. Extensive experiments on popular visual tracking datasets (including OTB100, VOT2016, VOT2018, VOT2019, UAV123, UAV20L, GOT-10k, and LaSOT) demonstrate the superiority of our proposed method against several state-of-the-art tracking methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9370-9386"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536600","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":"Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing","authors":"Xintao Yan;Shuo Feng;Haowei Sun;Henry X. Liu","doi":"10.1109/TITS.2025.3571966","DOIUrl":"https://doi.org/10.1109/TITS.2025.3571966","url":null,"abstract":"Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs’ safety performance unbiasedly, the probability distributions of environment statistics in the simulated naturalistic driving environment (NDE) need to be consistent with those from the real-world driving environment. However, although human driving behaviors have been extensively investigated in the transportation engineering field, most existing models were developed for traffic flow analysis without considering the distributional consistency of driving behaviors, which could cause significant evaluation biasedness for AV testing. To fill this research gap, a distributionally consistent NDE modeling framework is proposed in this paper. Using large-scale naturalistic driving data, empirical distributions are obtained to construct the stochastic human driving behavior models under different conditions. To address the error accumulation problem during the simulation, an optimization-based method is further designed to refine the empirical behavior models. Specifically, the vehicle state evolution is modeled as a Markov chain and its stationary distribution is twisted to match the distribution from the real-world driving environment. The framework is evaluated in the case study of a multi-lane highway driving simulation, where the distributional accuracy of the generated NDE is validated and the safety performance of an AV model is effectively evaluated.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9187-9200"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536442","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":"Road Side Unit Location Optimization Considering Communication Channel Competition and 6G Technology","authors":"Yining Ren;Yinhai Wang;Zhizhou Wu;Constantinos Antoniou;Yunyi Liang","doi":"10.1109/TITS.2025.3572183","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572183","url":null,"abstract":"This study investigates the problem of road side unit (RSU) location optimization considering vehicle-to-RSU (V2R) communication channel competition. To hedge against the uncertainty of vehicle density, the problem is formulated as a stochastic mixed-integer nonlinear program with equilibrium constraints. This program aims to minimize the expectation of weighted sum of V2R communication delay, packet loss rate and packet collision rate and age of information in V2R communication over all scenarios given RSU location budget limit. Decision variables are RSU locations and the number of connected autonomous vehicles (CAVs) communicating with each located RSU. Equilibrium constraints in the program model V2R communication channel competition among CAVs and ensures the choice of CAVs on RSUs to satisfy user equilibrium principle. The V2R communication is calculated under 6G technology. The program is linearized by using piecewise linearization method. To enhance the solution efficiency, a progressive hedging algorithm is developed to decompose the relaxed linearized model into several subproblems. The optimal solution to the relaxed linearized model is found by iteratively formulating and the solving subproblems. A branch and bound algorithm is introduced to obtain the optimal integer solution to the linearized model. The numerical results show that the proposed model can achieve 20.55% lower total communication delay than the state-of-the-art model only optimizing total V2R information propagation delay, when CAVs choose RSUs for communication in a competitive manner.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9867-9881"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536689","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":"IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY","authors":"","doi":"10.1109/TITS.2025.3571552","DOIUrl":"https://doi.org/10.1109/TITS.2025.3571552","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"C2-C2"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196787","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}

{"title":"MFV3DL: Monocular Vision Method for Future Vehicle 3D Localization","authors":"Wen Zhang;Zhexuan Sun;Shengrong Lv;Konghao Mei;Guangkun Chen;Zhenya Yang","doi":"10.1109/TITS.2025.3572742","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572742","url":null,"abstract":"Vision-based future vehicle localization provides intuitive trajectory prediction, serving as a critical foundation for Advanced Driving Assistance Systems (ADAS) to formulate collision avoidance decisions. Among existing approaches, ego-view trajectory prediction has proven effective for driver monitoring and intervention in vision-based localization. This method aligns closely with human perceptual processing, making it essential for the Driver-in-the-Loop (DIL) development stage in modern ADAS. However, most existing ego-view trajectory prediction approaches rely on two-dimensional image-based predictions, creating a gap with human three-dimensional perception. This disparity negatively impacts the accuracy and timeliness of driver decision-making and intervention. In this paper, we propose MFV3DL (Monocular Vision Method for Future Vehicle 3D Localization), a dual-stream framework integrating 2D image trajectory prediction and depth prediction to achieve future vehicle 3D localization. To enhance accuracy, we leverage Multi-Object Tracking and Segmentation (MOTS) results and depth estimation as inputs for the dual-stream architecture. Additionally, we introduce a Related Information Fusion (RIF) unit to enable cross-modal interaction between the two streams. For depth stream predictions, we propose a ConvLSTM-based depth prediction method. Experimental results on the KITTI dataset demonstrate that MFV3DL outperforms state-of-the-art methods. In diverse driving scenarios, MFV3DL achieves superior 3D visualization results compared to 2D trajectory-based predictions. Baseline comparisons and ablation studies further validate that the proposed ConvLSTM-based depth prediction enhances the dual-stream architecture and RIF unit for 3D localization tasks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9277-9292"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536280","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}