IEEE Transactions on Intelligent Transportation Systems最新文献

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CDF-Based Filter and Fuzzy-Based Adaptive CPFTS Controller for Constrained Underactuated Crane System: Theory and Experiment 约束欠驱动起重机系统的cdf滤波与模糊自适应CPFTS控制:理论与实验
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3572525
Yang Gao;Zhongcai Zhang;Peng Huang;Yuqiang Wu
{"title":"CDF-Based Filter and Fuzzy-Based Adaptive CPFTS Controller for Constrained Underactuated Crane System: Theory and Experiment","authors":"Yang Gao;Zhongcai Zhang;Peng Huang;Yuqiang Wu","doi":"10.1109/TITS.2025.3572525","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572525","url":null,"abstract":"This work centers around investigating the fixed-time tracking issue for an underactuated crane system with full-state constraints, disturbance, and measurement noise. A two-module design strategy consisting of state estimation stage and fixed-time tracking control design stage is proposed. Specifically, the composite disturbance filtering (CDF) approach is introduced to estimate states while rejecting and attenuating disturbance. Then by using fuzzy-based variables separation technique and integral barrier Lyapunov function (iBLF), the constrainedly practically fixed-time stability (CPFTS) is implemented to the studied crane system in the second stage. In the end, simulation and experiment results are presented to illustrate the efficacy of the proposed strategy.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9711-9725"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536401","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
PIP-Net: Pedestrian Intention Prediction in the Wild PIP-Net:野外行人意图预测
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3570794
Mohsen Azarmi;Mahdi Rezaei;He Wang
{"title":"PIP-Net: Pedestrian Intention Prediction in the Wild","authors":"Mohsen Azarmi;Mahdi Rezaei;He Wang","doi":"10.1109/TITS.2025.3570794","DOIUrl":"https://doi.org/10.1109/TITS.2025.3570794","url":null,"abstract":"Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field. In this article, we introduce PIP-Net, a novel framework designed to predict pedestrian crossing intentions by AVs in real-world urban scenarios. We offer two variants of PIP-Net designed for different camera mounts and setups. Leveraging both kinematic data and spatial features from the driving scene, the proposed model employs a recurrent and temporal attention-based solution, outperforming state-of-the-art performance. To enhance the visual representation of road users and their proximity to the ego vehicle, we introduce a categorical depth feature map, combined with a local motion flow feature, providing rich insights into the scene dynamics. Additionally, we explore the impact of expanding the camera’s field of view, from one to three cameras surrounding the ego vehicle, leading to an enhancement in the model’s contextual perception. Depending on the traffic scenario and road environment, the model excels in predicting pedestrian crossing intentions up to 4 seconds in advance, which is a breakthrough in current research studies in pedestrian intention prediction. Finally, for the first time, we present the Urban-PIP dataset, a customised pedestrian intention prediction dataset, with multi-camera annotations in real-world automated driving scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9824-9837"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536690","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
Practical Prescribed Performance Tracking Control and Optimization for Nonlinear Vehicular Platoon 非线性车辆排的实用预定性能跟踪控制与优化
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3572146
Shixi Wen;Ge Guo;Yuan Zhao
{"title":"Practical Prescribed Performance Tracking Control and Optimization for Nonlinear Vehicular Platoon","authors":"Shixi Wen;Ge Guo;Yuan Zhao","doi":"10.1109/TITS.2025.3572146","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572146","url":null,"abstract":"This study investigates the online optimization control problem for a vehicular platoon in the presence of unmodeled vehicle dynamics, unknown external disturbances, and uncertain inter-vehicle communication topology. A novel practical performance-prescribed reinforcement learning-based distributed sliding mode (PRLDSM) control framework is constructed for the platoon to strengthen the robustness and possess the online self-learning capacity for optimizing and control. Specifically, a composite controller is proposed for the platoon which consists of an online optimal controller and a sliding mode controller. By the strong learning capacity provided by the PRLDSM controller, the optimal policy and cost function can be recursively approximated by online simultaneous tuning of both actor and critic neural networks. Moreover, it is proved that all the signals in the closed-loop platoon control system are uniformly ultimately bounded and approach zero by selecting appropriate parameters. Theoretical analysis eventually guarantees that the tracking errors can be stabilized to satisfy both the individual vehicle stability and disturbance string stability with the prescribed transient response and steady-state accuracy. The obvious feature of the proposed PRLDSM controller is that the requirement of the exact vehicle dynamics and topological matrix of the inter-vehicle communication topology can be avoided. Both numerical examples and experimental studies illustrate the effectiveness of the proposed control method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9625-9639"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536700","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
IEEE Intelligent Transportation Systems Society Information IEEE智能交通系统学会信息
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3571556
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2025.3571556","DOIUrl":"https://doi.org/10.1109/TITS.2025.3571556","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"C3-C3"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213650","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
Sequential Decision MARL for Adaptive Traffic Signal Control With Different Intersections Priorities 不同交叉口优先级自适应交通信号控制的顺序决策MARL
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3572097
Wanting Liu;Chengwei Zhang;Kailing Zhou;Yihong Li;Furui Zhan;Wanli Xue;Rong Chen
{"title":"Sequential Decision MARL for Adaptive Traffic Signal Control With Different Intersections Priorities","authors":"Wanting Liu;Chengwei Zhang;Kailing Zhou;Yihong Li;Furui Zhan;Wanli Xue;Rong Chen","doi":"10.1109/TITS.2025.3572097","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572097","url":null,"abstract":"Existing multi-agent reinforcement learning (MARL) in adaptive traffic signal control (ATSC) typically models cooperative control of multiple intersections as a cooperative Markov game, optimizing the average traffic efficiency of intersections with the same emphasis. However, it is insufficient to meet the requirements in real ATSC scenarios when all intersections are treated equally. To this end, this work proposes the Captain-Member Markov Game (CM-MG) that considers the different priorities between intersections. CM-MG categorizes intersections into special and ordinary intersections, controlled by captain agents and member agents to optimize the traffic efficiency of local and overall road networks, respectively. The cooperative requirements of CM-MG are achieved through a sequential decision-making principle. Captains have priority in choosing actions, and members make decisions sequentially, following the breadth-first traversal order in the road network after obtaining their precursors’ intentions. Then, a cooperative MARL algorithm, i.e., Sequential Decision Deep Graph Network (GNSD-Light), is proposed to learn the optimal joint policy that meets the learning goals of both captains and members. To be unrestricted by the scales of intersections, GNSD-Light adopts an autoregressive framework where all agents make decisions sequentially in a predetermined order by sharing the same decision model. In addition, to obtain sufficient state representation, two relative position encoding-based spatiotemporal representation modules are designed for GNSD-Light based on the characteristics of ATSC scenarios. Finally, through adequate experiments and qualitative analysis, we have confirmed that our method effectively balances traffic efficiency among both the overall road network and special intersections.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9799-9811"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536284","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
Adaptive Multi-Objective Predictive Cruise Control With Digital Map Using a Utopia Tracking Method 基于乌托邦跟踪方法的数字地图自适应多目标预测巡航控制
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-30 DOI: 10.1109/TITS.2025.3559918
Yongjun Yan;Ziyou Song;Bingzhao Gao;Hong Chen;Jing Sun
{"title":"Adaptive Multi-Objective Predictive Cruise Control With Digital Map Using a Utopia Tracking Method","authors":"Yongjun Yan;Ziyou Song;Bingzhao Gao;Hong Chen;Jing Sun","doi":"10.1109/TITS.2025.3559918","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559918","url":null,"abstract":"The integration of look-ahead information into Model Predictive Control (MPC) frameworks has shown promise for intelligent transportation systems. However, transitioning Predictive Cruise Control (PCC) system research into practical application poses challenges due to numerous weighting parameters and increased computational demands in complex driving environments. Although the Weighted Sum Method is commonly used in PCC system research to balance fuel consumption and trip time objectives, it requires time-consuming weight tuning and often results in suboptimal performance due to fixed weighting parameters. To address this, this paper proposes a Utopia-tracking Model Predictive Control (UTM-MPC) controller, where the cost function is reformulated as the sum of the distances between the objectives and the average Utopia point over the prediction horizon. By analyzing the Pareto front of the PCC optimization problem under varying slope profiles extracted from digital map data, we demonstrate that the proposed UTM-MPC effectively leverages the geometric characteristics of the Pareto front to identify preferred trade-off solutions. The adaptive weighting mechanism—derived from the online-calculated Utopia point—enhances the robustness of the PCC system under complex and dynamic driving conditions. To mitigate the computational burden associated with integrating UTM-MPC into the MPC framework, we introduce a tailored neighboring extremal-based solving algorithm. Leveraging the receding horizon nature of MPC, this method requires only minimal updates to efficiently identify an optimal solution near the nominal trajectory from the previous sampling instance. Simulation results show that the UTM-MPC controller, with its adaptive weighting strategy, consistently outperforms the traditional Weighted Sum Method in terms of both fuel efficiency and trip time.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7450-7464"},"PeriodicalIF":7.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196816","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
Integrated Scheduling Optimization for Automated Container Terminal: A Reinforcement Learning-Based Approach 自动化集装箱码头综合调度优化:基于强化学习的方法
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-30 DOI: 10.1109/TITS.2025.3556882
Qi Wang;Xialiang Tong;Yantong Li;Chong Wang;Canrong Zhang
{"title":"Integrated Scheduling Optimization for Automated Container Terminal: A Reinforcement Learning-Based Approach","authors":"Qi Wang;Xialiang Tong;Yantong Li;Chong Wang;Canrong Zhang","doi":"10.1109/TITS.2025.3556882","DOIUrl":"https://doi.org/10.1109/TITS.2025.3556882","url":null,"abstract":"Container terminals face tremendous pressure to improve their throughput due to the expanding global shipping market. As a key for throughput, handling capacity requires effective coordination between various automated facilities. Observed from the operational practice of Tianjin port, a world-leading smart port, four critical facilities, namely quay cranes, lock stations, intersections, and yard cranes, are identified as bottlenecks that impact handling efficiency. Congestion at these facilities, in particular, pose significant challenges to terminal managers. To address these issues, we investigate an integrated terminal scheduling problem and formulate this novel problem as a mixed-integer linear program, from which we derive two efficient lower bounds. To tackle practical-sized problems, we propose a reinforcement learning (RL)-based algorithm with two modules. The offline module uses RL to learn from abundant historical data. When actual instance information is available, the online module enhances offline decisions using a rollout mechanism and mathematical programming. The proposed algorithm employs a pre-trained offline policy to handle extensive computations before actual decision-making and an online phase that provides a streamlined and stable method to enhance the solution. Extensive experiments validate the effectiveness of the proposed algorithm, demonstrating an 18.22% reduction in makespan compared to the rule-based heuristic used in actual port operations.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"10019-10035"},"PeriodicalIF":7.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536400","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
CurveFormer++: 3D Lane Detection by Curve Propagation With Temporal Curve Queries and Attention CurveFormer++:基于时间曲线查询和注意的曲线传播的三维车道检测
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-30 DOI: 10.1109/TITS.2025.3560337
Yifeng Bai;Zhirong Chen;Pengpeng Liang;Bo Song;Erkang Cheng
{"title":"CurveFormer++: 3D Lane Detection by Curve Propagation With Temporal Curve Queries and Attention","authors":"Yifeng Bai;Zhirong Chen;Pengpeng Liang;Bo Song;Erkang Cheng","doi":"10.1109/TITS.2025.3560337","DOIUrl":"https://doi.org/10.1109/TITS.2025.3560337","url":null,"abstract":"In autonomous driving, accurate 3D lane detection using monocular cameras is important for downstream tasks. Recent CNN and Transformer approaches usually apply a two-stage model design. The first stage transforms the image feature from a front image into a bird’s-eye-view (BEV) representation. Subsequently, a sub-network processes the BEV feature to generate the 3D detection results. However, these approaches heavily rely on a challenging image feature transformation module from a perspective view to a BEV representation. In our work, we present CurveFormer++, a single-stage Transformer-based method that does not require the view transform module and directly infers 3D lane results from the perspective image features. Specifically, our approach models the 3D lane detection task as a curve propagation problem, where each lane is represented by a curve query with a dynamic and ordered anchor point set. By employing a Transformer decoder, the model can iteratively refine the 3D lane results. A curve cross-attention module is introduced to calculate similarities between image features and curve queries. To handle varying lane lengths, we employ context sampling and anchor point restriction techniques to compute more relevant image features. Furthermore, we apply a temporal fusion module that incorporates selected informative sparse curve queries and their corresponding anchor point sets to leverage historical information. In the experiments, we evaluate our approach on two publicly real-world datasets. The results demonstrate that our method provides outstanding performance compared with both CNN and Transformer based methods. We also conduct ablation studies to analyze the impact of each component.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7909-7920"},"PeriodicalIF":7.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205981","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
Event-Triggered Tube-DMPC With Shrinking Ingredients for Vehicle Platoon Under Disturbance and Communication Delay 干扰和通信延迟条件下车辆队列的事件触发管-缩元dmpc
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-30 DOI: 10.1109/TITS.2025.3561134
Qianyue Luo;James Lam
{"title":"Event-Triggered Tube-DMPC With Shrinking Ingredients for Vehicle Platoon Under Disturbance and Communication Delay","authors":"Qianyue Luo;James Lam","doi":"10.1109/TITS.2025.3561134","DOIUrl":"https://doi.org/10.1109/TITS.2025.3561134","url":null,"abstract":"This paper presents a novel event-triggered robust distributed model predictive control (DMPC) methodology with a shrinking prediction horizon and shrinking terminal region for vehicle platoon control in the presence of external disturbances and communication delays. Using the framework of tube-based MPC, an information transmission triggering mechanism is designed which is determined by control plan changes for the nominal leading vehicle, to reduce the communication frequency. Then the follower vehicles only update their control inputs, prediction horizons and corresponding terminal sets when they receive new information from the leader, so as to reduce the computational burden. The proposed approach effectively compensates the information errors caused by the communication delays by utilizing the timestamp information. When the leader’s control plan changes during the delay period, the recursive feasibility of the local optimization problem and the uniform ultimate bound stability of the platoon system are guaranteed with the proposed controller. The numerical simulation demonstrates the superior performance and robustness of the proposed approach, which offers promising solutions for real-world platoon control systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8467-8480"},"PeriodicalIF":7.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196812","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 Completing Missing Pedestrian Trajectories Method Driven by Prior-Posterior Knowledge and Interactive Information 基于先验-后验知识和交互信息的行人轨迹补全方法
IF 7.9 1区 工程技术
IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-30 DOI: 10.1109/TITS.2025.3559191
Mingxing Duan;Xinyue Zheng;Huilong Pi;Yan Ding;Zhuo Tang
{"title":"A Completing Missing Pedestrian Trajectories Method Driven by Prior-Posterior Knowledge and Interactive Information","authors":"Mingxing Duan;Xinyue Zheng;Huilong Pi;Yan Ding;Zhuo Tang","doi":"10.1109/TITS.2025.3559191","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559191","url":null,"abstract":"Pedestrian historical trajectory completion significantly bolsters the predictive accuracy of models. However, traditional statistical models such as Hidden Markov Models (HMM), which focus solely on individual pedestrian trajectories, often fall short in terms of generalization. Conversely, data-driven deep learning approaches demand extensive and meticulous data annotation as well as large datasets. Additionally, leveraging sequential historical data and uncovering the correlation between neighboring pedestrians during absences presents a significant challenge. To address these issues, we introduce a novel trajectory completion method that harnesses prior-posterior knowledge and interactive information, termed CMPT. Our approach commences with the design of a Neighbor Pedestrian Selection module (NPS), adept at identifying neighboring pedestrians through a composite scoring system that evaluates feature similarity and proximity. Subsequently, we employ a Top-Graph Attention Network (T-GAT) to extract multiple correlation sets between preceding and succeeding moments within the scenario. These correlations are then fed into the Markov-Inverse Recovery module (MR), which utilizes prior and posterior insights to flesh out the neighbor influence at the unobserved intervals. Culminating in the Trajectory Reconstruction module (TR), we integrate the completed neighbor influence data with the historical trajectory of the missing pedestrian to finalize the missing trajectory reconstruction. Empirical evidence from our experiments indicates that the Final Distance Error (FDE) of the trajectories completed by CMPT is a commendable 0.30. The source code for CMPT is available from <uri>https://github.com/ZYueliang/CMPT-Net</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7969-7979"},"PeriodicalIF":7.9,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206174","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
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