Pub Date : 2025-08-13DOI: 10.1109/TITS.2025.3594337
Jiawei Liu;Da Yang;Donghai Zhai;Meng-Si Yu
Monocular vision systems have emerged as a promising solution for 3D object detection due to their cost-effectiveness and deployment simplicity. However, existing methods heavily rely on prior information such as camera parameters and complex 3D dataset annotations. Most current approaches focus on static RGB images, struggling to recover 3D information from 2D inputs. We propose a novel monocular 3D vehicle detection method based on point tracking from a roadside perspective, leveraging dynamic inter-frame associations in video data. Our method operates without prior data like vehicle dimensions or camera parameters, and eliminates the need for complex 3D dataset construction and annotation. It integrates object detection and semantic segmentation for feature extraction, high-precision inter-frame data association, and trajectory-based state analysis. 3D reconstruction is achieved through reference point determination and coordinate calibration under geometric constraints. Experimental results on the DAIR-V2X-I dataset demonstrate superior performance over baseline models, with error reductions ranging from 1.3% to 7.3% across various metrics (A3DS, A2DS, AGS, ALS, APS, AW). This approach presents a practical solution for monocular 3D object detection while eliminating dependency on prior information and complex datasets.
{"title":"Monocular 3D Vehicle Detection Based on Video Point Tracking: A Novel Approach Without Prior Information","authors":"Jiawei Liu;Da Yang;Donghai Zhai;Meng-Si Yu","doi":"10.1109/TITS.2025.3594337","DOIUrl":"https://doi.org/10.1109/TITS.2025.3594337","url":null,"abstract":"Monocular vision systems have emerged as a promising solution for 3D object detection due to their cost-effectiveness and deployment simplicity. However, existing methods heavily rely on prior information such as camera parameters and complex 3D dataset annotations. Most current approaches focus on static RGB images, struggling to recover 3D information from 2D inputs. We propose a novel monocular 3D vehicle detection method based on point tracking from a roadside perspective, leveraging dynamic inter-frame associations in video data. Our method operates without prior data like vehicle dimensions or camera parameters, and eliminates the need for complex 3D dataset construction and annotation. It integrates object detection and semantic segmentation for feature extraction, high-precision inter-frame data association, and trajectory-based state analysis. 3D reconstruction is achieved through reference point determination and coordinate calibration under geometric constraints. Experimental results on the DAIR-V2X-I dataset demonstrate superior performance over baseline models, with error reductions ranging from 1.3% to 7.3% across various metrics (A3DS, A2DS, AGS, ALS, APS, AW). This approach presents a practical solution for monocular 3D object detection while eliminating dependency on prior information and complex datasets.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"21172-21185"},"PeriodicalIF":8.4,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486503","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}
Pub Date : 2025-08-11DOI: 10.1109/TITS.2025.3591961
Noor Aboueleneen;Yahuza Bello;Abdullatif Albaseer;Ahmed Refaey Hussein;Mohamed Abdallah;Ekram Hossain
Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs’ systems still get overwhelmed when they encounter a complex dynamic change in the environment resulting from an accident or a roadblock for maintenance. The advanced features of Sixth Generation (6G) technology are set to offer strong support to AVs, enabling real-time data exchange and management of complex driving maneuvers. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework to improve AVs’ decision-making in dynamic and complex Intelligent Transportation Systems (ITS) utilizing 6G-V2X communication. The primary objective is to enable AVs to avoid roadblocks efficiently by changing lanes while maintaining optimal traffic flow and maximizing the mean harmonic speed. To ensure realistic operations, key constraints such as minimum vehicle speed, roadblock count, and lane change frequency are integrated. We train and test the proposed MARL model with two traffic simulation scenarios using the SUMO and TraCI interface. Through extensive simulations, we demonstrate that the proposed model adapts to various traffic conditions and achieves efficient and robust traffic flow management. Specifically, the proposed approach results in a harmonic mean speed increase of up to 15% and a reduction in lane-change frequency by 10%. The trained model effectively navigates dynamic roadblocks, promoting improved traffic efficiency in AV operations with more than 70% efficiency over other benchmark solutions.
{"title":"Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep Reinforcement Learning Approach","authors":"Noor Aboueleneen;Yahuza Bello;Abdullatif Albaseer;Ahmed Refaey Hussein;Mohamed Abdallah;Ekram Hossain","doi":"10.1109/TITS.2025.3591961","DOIUrl":"https://doi.org/10.1109/TITS.2025.3591961","url":null,"abstract":"Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs’ systems still get overwhelmed when they encounter a complex dynamic change in the environment resulting from an accident or a roadblock for maintenance. The advanced features of Sixth Generation (6G) technology are set to offer strong support to AVs, enabling real-time data exchange and management of complex driving maneuvers. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework to improve AVs’ decision-making in dynamic and complex Intelligent Transportation Systems (ITS) utilizing 6G-V2X communication. The primary objective is to enable AVs to avoid roadblocks efficiently by changing lanes while maintaining optimal traffic flow and maximizing the mean harmonic speed. To ensure realistic operations, key constraints such as minimum vehicle speed, roadblock count, and lane change frequency are integrated. We train and test the proposed MARL model with two traffic simulation scenarios using the SUMO and TraCI interface. Through extensive simulations, we demonstrate that the proposed model adapts to various traffic conditions and achieves efficient and robust traffic flow management. Specifically, the proposed approach results in a harmonic mean speed increase of up to 15% and a reduction in lane-change frequency by 10%. The trained model effectively navigates dynamic roadblocks, promoting improved traffic efficiency in AV operations with more than 70% efficiency over other benchmark solutions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18180-18193"},"PeriodicalIF":8.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384579","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}
Bird’s-Eye View (BEV) semantic segmentation is a key technology for constructing high-precision maps in low-cost visual navigation systems. The main challenge lies in effectively transforming image features into BEV features while preserving rich BEV visual information. Recent works have shown that generative models hold great promise in advancing BEV segmentation. However, these methods primarily focus on producing BEV features using prior knowledge, often overlooking key challenges such as feature shift, confusion, and forgetting during the BEV feature generation process. In this paper, we propose GenBEV, a generative model with semantic compensation that formally addresses inaccuracies and confusion in BEV feature generation. GenBEV leverages the synergistic benefits of data fusion consistency and noise-reduction training to enhance the diversity and reliability of the generated information. This improvement boosts the robustness and generalization of BEV segmentation across diverse scenarios, including those involving complex objects and low-quality images. Specifically, we design an adaptive cross-feature encoder to reduce diffusion variability. During decoding, we integrate the context of BEV features with noisy features to construct semantic embeddings. We show the effectiveness of GenBEV on the nuScenes, KITTI Raw, and KITTI 3D Object datasets. GenBEV achieves segmentation scores of 29.5%, 68.8%, and 39.7%, respectively, surpassing current methods by up to 3.6%, 2.4%, and 2.7%. To the best of our knowledge, GenBEV is the first to address the problem of BEV feature falsification in generative architectures.
鸟瞰语义分割是低成本视觉导航系统中构建高精度地图的关键技术。主要挑战在于如何有效地将图像特征转化为BEV特征，同时保留丰富的BEV视觉信息。最近的研究表明，生成模型在推进纯电动汽车分割方面具有很大的前景。然而，这些方法主要侧重于使用先验知识生成BEV特征，往往忽略了在BEV特征生成过程中的关键挑战，如特征转移、混淆和遗忘。在本文中，我们提出了一个具有语义补偿的生成模型GenBEV，它正式解决了BEV特征生成中的不准确和混淆。GenBEV利用数据融合一致性和降噪训练的协同优势，增强生成信息的多样性和可靠性。这种改进提高了BEV分割在不同场景下的鲁棒性和泛化性，包括涉及复杂物体和低质量图像的场景。具体来说，我们设计了一个自适应的交叉特征编码器来减少扩散变异性。在解码过程中，我们将BEV特征的上下文与噪声特征相结合来构建语义嵌入。我们展示了GenBEV在nuScenes， KITTI Raw和KITTI 3D Object数据集上的有效性。GenBEV分别实现了29.5%、68.8%和39.7%的分割分数，比现有方法分别高出3.6%、2.4%和2.7%。据我们所知，GenBEV是第一个解决生成架构中BEV特征证伪问题的算法。
{"title":"GenBEV: Generative Model With Semantic Compensation for Bird’s Eye View Segmentation","authors":"Jiahui Yu;Weiming Fan;Yuping Guo;Hong Lyu;Hongwei Gao;Changting Lin;Meng Han;Xu Cheng","doi":"10.1109/TITS.2025.3589213","DOIUrl":"https://doi.org/10.1109/TITS.2025.3589213","url":null,"abstract":"Bird’s-Eye View (BEV) semantic segmentation is a key technology for constructing high-precision maps in low-cost visual navigation systems. The main challenge lies in effectively transforming image features into BEV features while preserving rich BEV visual information. Recent works have shown that generative models hold great promise in advancing BEV segmentation. However, these methods primarily focus on producing BEV features using prior knowledge, often overlooking key challenges such as feature shift, confusion, and forgetting during the BEV feature generation process. In this paper, we propose GenBEV, a generative model with semantic compensation that formally addresses inaccuracies and confusion in BEV feature generation. GenBEV leverages the synergistic benefits of data fusion consistency and noise-reduction training to enhance the diversity and reliability of the generated information. This improvement boosts the robustness and generalization of BEV segmentation across diverse scenarios, including those involving complex objects and low-quality images. Specifically, we design an adaptive cross-feature encoder to reduce diffusion variability. During decoding, we integrate the context of BEV features with noisy features to construct semantic embeddings. We show the effectiveness of GenBEV on the nuScenes, KITTI Raw, and KITTI 3D Object datasets. GenBEV achieves segmentation scores of 29.5%, 68.8%, and 39.7%, respectively, surpassing current methods by up to 3.6%, 2.4%, and 2.7%. To the best of our knowledge, GenBEV is the first to address the problem of BEV feature falsification in generative architectures.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"21186-21198"},"PeriodicalIF":8.4,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486507","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}
Pub Date : 2025-08-05DOI: 10.1109/TITS.2025.3591911
Hui Sun;Ruoge Xu;Jian Zhang;Xingguo Huang;Li Han
Seismic exploration is an important tool for the detection of road diseases. However, since engineering seismic exploration usually deals with near-surface problems, its detection is complex and difficult. Time-frequency analysis is an important seismic attribute extraction method, which can provide hidden information that is difficult to obtain from seismic profiles, which can effectively help to identify subsurface structures and various types of disease. The S-transform is an important linear time-frequency analysis method, but the window function is fixed during its time-frequency feature extraction, resulting in a shift of the spectrum to higher frequencies, which reduces the accuracy of the time-frequency analysis. The unscaled S-transform, which removes the linear frequency term in the window function, overcomes the above problem to some extent, but affects the temporal resolution of the spectrum in the low-frequency region. To this end, we propose a modified frequency-domain unscaled S-transform method (MFUST) to perform the time-frequency decomposition of seismic signals, and the proposed method adds additional parameters to its window function, which ensures the time-frequency accuracy while realizing the improvement of the spectrum in terms of temporal resolution through the adjustment of the parameters. The effectiveness of the proposed method is verified using synthetic numerical experiments and a real data test.
{"title":"A Modified Unscaled S-Transform for Seismic Time-Frequency Analysis of Road Detection in Intelligent Transportation Systems","authors":"Hui Sun;Ruoge Xu;Jian Zhang;Xingguo Huang;Li Han","doi":"10.1109/TITS.2025.3591911","DOIUrl":"https://doi.org/10.1109/TITS.2025.3591911","url":null,"abstract":"Seismic exploration is an important tool for the detection of road diseases. However, since engineering seismic exploration usually deals with near-surface problems, its detection is complex and difficult. Time-frequency analysis is an important seismic attribute extraction method, which can provide hidden information that is difficult to obtain from seismic profiles, which can effectively help to identify subsurface structures and various types of disease. The S-transform is an important linear time-frequency analysis method, but the window function is fixed during its time-frequency feature extraction, resulting in a shift of the spectrum to higher frequencies, which reduces the accuracy of the time-frequency analysis. The unscaled S-transform, which removes the linear frequency term in the window function, overcomes the above problem to some extent, but affects the temporal resolution of the spectrum in the low-frequency region. To this end, we propose a modified frequency-domain unscaled S-transform method (MFUST) to perform the time-frequency decomposition of seismic signals, and the proposed method adds additional parameters to its window function, which ensures the time-frequency accuracy while realizing the improvement of the spectrum in terms of temporal resolution through the adjustment of the parameters. The effectiveness of the proposed method is verified using synthetic numerical experiments and a real data test.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18194-18203"},"PeriodicalIF":8.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384577","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}
During the process of driving, humans usually rely on multiple senses to gather information and make decisions. Analogously, in order to achieve embodied intelligence in autonomous driving, it is essential to integrate multidimensional sensory information in order to facilitate interaction with the environment. However, the current multi-modal fusion sensing schemes often neglect these additional sensory inputs, hindering the realization of fully autonomous driving. This paper considers multi-sensory information and proposes a multi-modal interactive perception dataset named MIPD, enabling expanding the current autonomous driving algorithm framework, for supporting the research on embodied intelligent driving. In addition to the conventional camera, lidar, and 4D radar data, our dataset incorporates multiple sensor inputs including sound, light intensity, vibration intensity and vehicle speed to enrich the dataset comprehensiveness. Comprising 126 consecutive sequences, many exceeding twenty seconds, MIPD features over 8,500 meticulously synchronized and annotated frames. Moreover, it encompasses many challenging scenarios, covering various road and lighting conditions. The dataset has undergone thorough experimental validation, producing valuable insights for the exploration of next-generation autonomous driving frameworks. Data, development kit and more details will be available at https://github.com/BUCT-IUSRC/Dataset__MIPD
{"title":"MIPD: A Multi-Sensory Interactive Perception Dataset for Embodied Intelligent Driving","authors":"Zhiwei Li;Tingzhen Zhang;Meihua Zhou;Dandan Tang;Pengwei Zhang;Wenzhuo Liu;Qiaoning Yang;Tianyu Shen;Kunfeng Wang;Huaping Liu","doi":"10.1109/TITS.2025.3593298","DOIUrl":"https://doi.org/10.1109/TITS.2025.3593298","url":null,"abstract":"During the process of driving, humans usually rely on multiple senses to gather information and make decisions. Analogously, in order to achieve embodied intelligence in autonomous driving, it is essential to integrate multidimensional sensory information in order to facilitate interaction with the environment. However, the current multi-modal fusion sensing schemes often neglect these additional sensory inputs, hindering the realization of fully autonomous driving. This paper considers multi-sensory information and proposes a multi-modal interactive perception dataset named MIPD, enabling expanding the current autonomous driving algorithm framework, for supporting the research on embodied intelligent driving. In addition to the conventional camera, lidar, and 4D radar data, our dataset incorporates multiple sensor inputs including sound, light intensity, vibration intensity and vehicle speed to enrich the dataset comprehensiveness. Comprising 126 consecutive sequences, many exceeding twenty seconds, MIPD features over 8,500 meticulously synchronized and annotated frames. Moreover, it encompasses many challenging scenarios, covering various road and lighting conditions. The dataset has undergone thorough experimental validation, producing valuable insights for the exploration of next-generation autonomous driving frameworks. Data, development kit and more details will be available at <uri>https://github.com/BUCT-IUSRC/Dataset__MIPD</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"21320-21334"},"PeriodicalIF":8.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486522","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}
Pub Date : 2025-08-04DOI: 10.1109/TITS.2025.3591361
Bin Cao;Rui Du;Zhaokun Wang
Deep neural networks can provide environment sensing and decision support for vehicles in 6G intelligent autonomous transportation systems; however, the high computational cost associated with complex tasks limits the deployment of models on edge devices. To address this issue, this paper introduces a large-scale multiobjective filter pruning method, which stratifies the population by Angle Penalty Distance (APD) and selects the individuals to be updated. Meanwhile, the global search capability of the algorithm is enhanced by combining sampling update strategies and quantum behavioral update, in different situations. Moreover, a dynamic optimization tuning strategy is proposed to regulate the balance between exploration and exploitation within the algorithm. In the depth estimation task, the model is pruned using three objectives: root mean square error (RMSE), number of parameters, and FLOPs. Experimental results indicate that the pruned model obtains the minimum RMSE and the maximum compression ratio of the number of parameters and FLOPs, and can be effectively deployed in sensing-computing integrated chip and system for intelligent transportation systems.
{"title":"Large-Scale Multiobjective Model Pruning for Intelligent Transport Systems","authors":"Bin Cao;Rui Du;Zhaokun Wang","doi":"10.1109/TITS.2025.3591361","DOIUrl":"https://doi.org/10.1109/TITS.2025.3591361","url":null,"abstract":"Deep neural networks can provide environment sensing and decision support for vehicles in 6G intelligent autonomous transportation systems; however, the high computational cost associated with complex tasks limits the deployment of models on edge devices. To address this issue, this paper introduces a large-scale multiobjective filter pruning method, which stratifies the population by Angle Penalty Distance (APD) and selects the individuals to be updated. Meanwhile, the global search capability of the algorithm is enhanced by combining sampling update strategies and quantum behavioral update, in different situations. Moreover, a dynamic optimization tuning strategy is proposed to regulate the balance between exploration and exploitation within the algorithm. In the depth estimation task, the model is pruned using three objectives: root mean square error (RMSE), number of parameters, and FLOPs. Experimental results indicate that the pruned model obtains the minimum RMSE and the maximum compression ratio of the number of parameters and FLOPs, and can be effectively deployed in sensing-computing integrated chip and system for intelligent transportation systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18204-18213"},"PeriodicalIF":8.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384576","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}
Pub Date : 2025-08-01DOI: 10.1109/TITS.2025.3591122
Lei Zhang;Ying Shi;Xuanhua Ke;Jingping Wang
The continuously increasing carbon emission from road traffic has a negative impact on the global climate, mak- ing its forecasting a hotspot in intelligent transportation research. There are complex spatiotemporal characteristics and multidimensional features in road traffic network data, which cannot be effectively processed by previous forecasting models. To address the problem, this paper proposes TSD-Informer as the forecasting model. Considering the distraction of attention on temporal dependency, the input mapping method is changed from feature dimension to time dimension. To capture the spatial characteristics of the road traffic network, the spatial correlation is enhanced through a multi-source information fusion gate. The coupling relationship and contribution of muti-source data is studied by introducing the Gated Residual Network. Eventually, an improved Informer model is developed to forecasting the carbon emission for urban road traffic at topological level. The experimental results show that each strategy is effective and compatible with each other. The improved Informer model outperforms Informer model by an average reduction of 27.47%, and 21.38% in the root mean squared error and the mean absolute error with the best R2 closest to 1. All evaluation indicators are superior to the current mainstream forecasting models, which proves the superiority of the proposed TSD-Informer model.
{"title":"Carbon Emission Forecasting for Urban Road Traffic at Topological Level Based on Improved Informer","authors":"Lei Zhang;Ying Shi;Xuanhua Ke;Jingping Wang","doi":"10.1109/TITS.2025.3591122","DOIUrl":"https://doi.org/10.1109/TITS.2025.3591122","url":null,"abstract":"The continuously increasing carbon emission from road traffic has a negative impact on the global climate, mak- ing its forecasting a hotspot in intelligent transportation research. There are complex spatiotemporal characteristics and multidimensional features in road traffic network data, which cannot be effectively processed by previous forecasting models. To address the problem, this paper proposes TSD-Informer as the forecasting model. Considering the distraction of attention on temporal dependency, the input mapping method is changed from feature dimension to time dimension. To capture the spatial characteristics of the road traffic network, the spatial correlation is enhanced through a multi-source information fusion gate. The coupling relationship and contribution of muti-source data is studied by introducing the Gated Residual Network. Eventually, an improved Informer model is developed to forecasting the carbon emission for urban road traffic at topological level. The experimental results show that each strategy is effective and compatible with each other. The improved Informer model outperforms Informer model by an average reduction of 27.47%, and 21.38% in the root mean squared error and the mean absolute error with the best R2 closest to 1. All evaluation indicators are superior to the current mainstream forecasting models, which proves the superiority of the proposed TSD-Informer model.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"21232-21244"},"PeriodicalIF":8.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486512","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}
Pub Date : 2025-08-01DOI: 10.1109/TITS.2025.3589395
Zixu Hao;Yumei Liu;Ting Hu;Pengcheng Liu;Ming Liu
In this paper, a recurrent neural network (RNN) embedding compensation control scheme for a high-speed train (HST) subject to unknown dynamic, unknown disturbances, actuator faults and asymmetric nonlinear actuator saturation is investigated. The adaptive non-singular fast terminal sliding mode fault-tolerant controller with mix basis function approximator (MBF) and disturbance observer (DOB) is proposed as base controller of RNN-embedding compensation control. The MBF is used to approximate unknown dynamic terms in HST system and eliminate the asymmetric nonlinear actuator saturation. The DOB is used to observe unknown disturbances. Then, RNN-embedding compensation control scheme are proposed to optimize the performance of the base controller. The RNN-embedding compensation controller based on uniformly ultimately bounded Lyapunov stability is embedded to the base controller and the equivalent objective function is given to optimize the RNN. Finally, simulation results based on a real train dynamic model are presented to show the proposed schemes’ effectiveness and feasibility.
{"title":"RNN-Embedding Compensation Fault Tolerant Control for High-Speed Trains With Actuator Saturation","authors":"Zixu Hao;Yumei Liu;Ting Hu;Pengcheng Liu;Ming Liu","doi":"10.1109/TITS.2025.3589395","DOIUrl":"https://doi.org/10.1109/TITS.2025.3589395","url":null,"abstract":"In this paper, a recurrent neural network (RNN) embedding compensation control scheme for a high-speed train (HST) subject to unknown dynamic, unknown disturbances, actuator faults and asymmetric nonlinear actuator saturation is investigated. The adaptive non-singular fast terminal sliding mode fault-tolerant controller with mix basis function approximator (MBF) and disturbance observer (DOB) is proposed as base controller of RNN-embedding compensation control. The MBF is used to approximate unknown dynamic terms in HST system and eliminate the asymmetric nonlinear actuator saturation. The DOB is used to observe unknown disturbances. Then, RNN-embedding compensation control scheme are proposed to optimize the performance of the base controller. The RNN-embedding compensation controller based on uniformly ultimately bounded Lyapunov stability is embedded to the base controller and the equivalent objective function is given to optimize the RNN. Finally, simulation results based on a real train dynamic model are presented to show the proposed schemes’ effectiveness and feasibility.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"21271-21282"},"PeriodicalIF":8.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486506","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}
Pub Date : 2025-07-28DOI: 10.1109/TITS.2025.3585638
Yunzhi Xia;Yunyun Li;Lingzhi Yi;Xianjun Deng;Xiao Tang;Laurence T. Yang;Chenlu Zhu;Jong Hyuk Park
The large-scale applications of wireless sensor networks (WSNs) place higher demands on their reliability. WSNs deployed in the intelligent tunnel are often affected by environmental interference or malicious intrusions, which lead to untrusted paths and affect the reliability of transmission. A trusted path ensures that the collected information can be successfully and reliably transmitted to the sink node. To solve the transmission reliability problem of wireless sensor networks, a trusted multi-source shortest path-based transmission reliability (TMSPR) algorithm is proposed in this paper. A lightweight trust management model with a node relation matrix (RM) is applied to identify and exclude untrusted nodes, thereby establishing secure transmission links. Meanwhile, the shortest transmission path is selected based on the minimum path to save the energy of the nodes. The information transmitted through trusted multi-source shortest paths (TMSPs) can successfully reach the sink node, which significantly improves the transmission reliability of the network. Furthermore, a transmission reliability index TRel is defined as a probabilistic measure to assess reliability. Simulation results demonstrate that the proposed algorithm exponentially reduces both computation time and memory usage, while enhancing transmission reliability by approximately 5%.
{"title":"TMSPR: Trusted Multi-Source Shortest Paths-Based Transmission Reliability of Wireless Sensor Network in Intelligent Tunnel","authors":"Yunzhi Xia;Yunyun Li;Lingzhi Yi;Xianjun Deng;Xiao Tang;Laurence T. Yang;Chenlu Zhu;Jong Hyuk Park","doi":"10.1109/TITS.2025.3585638","DOIUrl":"https://doi.org/10.1109/TITS.2025.3585638","url":null,"abstract":"The large-scale applications of wireless sensor networks (WSNs) place higher demands on their reliability. WSNs deployed in the intelligent tunnel are often affected by environmental interference or malicious intrusions, which lead to untrusted paths and affect the reliability of transmission. A trusted path ensures that the collected information can be successfully and reliably transmitted to the sink node. To solve the transmission reliability problem of wireless sensor networks, a trusted multi-source shortest path-based transmission reliability (TMSPR) algorithm is proposed in this paper. A lightweight trust management model with a node relation matrix (RM) is applied to identify and exclude untrusted nodes, thereby establishing secure transmission links. Meanwhile, the shortest transmission path is selected based on the minimum path to save the energy of the nodes. The information transmitted through trusted multi-source shortest paths (TMSPs) can successfully reach the sink node, which significantly improves the transmission reliability of the network. Furthermore, a transmission reliability index TRel is defined as a probabilistic measure to assess reliability. Simulation results demonstrate that the proposed algorithm exponentially reduces both computation time and memory usage, while enhancing transmission reliability by approximately 5%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"13037-13050"},"PeriodicalIF":8.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315458","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}
Pub Date : 2025-07-28DOI: 10.1109/TITS.2025.3590075
Chen Xu;Qiang Wang;Lijun Sun
Travel time estimation is a key task in navigation apps and web mapping services. Existing deterministic and probabilistic methods, based on the assumption of trip independence, predominantly focus on modeling individual trips while overlooking trip correlations. However, real-world conditions frequently introduce strong correlations between trips, influenced by external and internal factors such as weather and the tendencies of drivers. To address this, we propose a deep hierarchical joint probabilistic model, ProbETA, for travel time estimation, capturing both inter-trip and intra-trip correlations. The joint distribution of travel times across multiple trips is modeled as a low-rank multivariate Gaussian, parameterized by learnable link representations estimated using the empirical Bayes approach. We also introduce a data augmentation method based on trip sub-sampling, allowing for fine-grained gradient backpropagation when learning link representations. During inference, our model estimates the probability distribution of travel time for a queried trip, conditional on spatiotemporally adjacent completed trips. Evaluation on two real-world GPS trajectory datasets demonstrates that ProbETA outperforms state-of-the-art deterministic and probabilistic baselines, with Mean Absolute Percentage Error decreasing by over 12.60%. Moreover, the learned link representations align with the physical network geometry, potentially making them applicable for other tasks.
{"title":"Link Representation Learning for Probabilistic Travel Time Estimation","authors":"Chen Xu;Qiang Wang;Lijun Sun","doi":"10.1109/TITS.2025.3590075","DOIUrl":"https://doi.org/10.1109/TITS.2025.3590075","url":null,"abstract":"Travel time estimation is a key task in navigation apps and web mapping services. Existing deterministic and probabilistic methods, based on the assumption of trip independence, predominantly focus on modeling individual trips while overlooking trip correlations. However, real-world conditions frequently introduce strong correlations between trips, influenced by external and internal factors such as weather and the tendencies of drivers. To address this, we propose a deep hierarchical joint probabilistic model, ProbETA, for travel time estimation, capturing both inter-trip and intra-trip correlations. The joint distribution of travel times across multiple trips is modeled as a low-rank multivariate Gaussian, parameterized by learnable link representations estimated using the empirical Bayes approach. We also introduce a data augmentation method based on trip sub-sampling, allowing for fine-grained gradient backpropagation when learning link representations. During inference, our model estimates the probability distribution of travel time for a queried trip, conditional on spatiotemporally adjacent completed trips. Evaluation on two real-world GPS trajectory datasets demonstrates that ProbETA outperforms state-of-the-art deterministic and probabilistic baselines, with Mean Absolute Percentage Error decreasing by over 12.60%. Moreover, the learned link representations align with the physical network geometry, potentially making them applicable for other tasks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"21149-21161"},"PeriodicalIF":8.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486505","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}
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