Pub Date : 2025-07-18DOI: 10.1109/TITS.2025.3587612
Daoming Wan;Jimmy Xiangji Huang
Misbehavior detection systems (MDS) play a crucial role in vehicular ad hoc networks (VANETs) to guarantee their secure operation. Most recent studies focus on applying machine learning methods to detect misbehavior messages. However, these studies are mainly proposed in the complete VANETs datasets. The MDS with incomplete messages (IMDS) is an inevitable issue that was rarely discussed in previous studies. This survey paper aims to explore the performance of missing value treatments in IMDS. It comprehensively introduces the current missing value treatments as well as the data amputation method from previous studies. Furthermore, this survey simulates the incomplete environments for VANETs datasets and conducts experiments over simulated incomplete datasets with various performance metrics. The experimental results are analyzed and discussed to indicate the best match of missing value treatments for IMDS. Finally, the potential challenges and promising future research directions are also highlighted in this paper.
{"title":"Missing Value Treatments for Machine Learning-Based Misbehavior Detection Systems: Survey, Evaluation, and Challenges","authors":"Daoming Wan;Jimmy Xiangji Huang","doi":"10.1109/TITS.2025.3587612","DOIUrl":"https://doi.org/10.1109/TITS.2025.3587612","url":null,"abstract":"Misbehavior detection systems (MDS) play a crucial role in vehicular ad hoc networks (VANETs) to guarantee their secure operation. Most recent studies focus on applying machine learning methods to detect misbehavior messages. However, these studies are mainly proposed in the complete VANETs datasets. The MDS with incomplete messages (IMDS) is an inevitable issue that was rarely discussed in previous studies. This survey paper aims to explore the performance of missing value treatments in IMDS. It comprehensively introduces the current missing value treatments as well as the data amputation method from previous studies. Furthermore, this survey simulates the incomplete environments for VANETs datasets and conducts experiments over simulated incomplete datasets with various performance metrics. The experimental results are analyzed and discussed to indicate the best match of missing value treatments for IMDS. Finally, the potential challenges and promising future research directions are also highlighted in this paper.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"12798-12818"},"PeriodicalIF":8.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315486","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 : 2025-07-17DOI: 10.1109/TITS.2025.3584216
Mu Chen;Yong Li;Zaojian Dai;Tao Zhang;Yu Zhou;Hui Wang
Intelligent Transportation Systems (ITS) hold a central position in urban traffic strategies. Reliable and timely communication is crucial for the effective operation of ITS, because it requires uninterrupted real-time data to ensure safe and efficient traffic flow. As an indispensable component of ITS, Uncrewed Aerial Vehicles (UAVs) offer the agility, rapid deployment, and wide area vantage required for city-scale monitoring and prompt incident response. However, the crowded urban spectrum—characterized by co-channel interference, malicious jamming, and stringent spectrum and energy constraints—compromises the reliability and timeliness of UAV communications. This study investigates the anti-jamming communication problem for a UAV swarm applied to urban traffic monitoring and models this task as a decentralized, partially observable Markov decision process (Dec-POMDP). Based on this model, we develop a multi-domain adaptive scheme based on the multi-agent deep deterministic policy gradient (MADDPG) framework. The combination of centralized training and decentralized execution enables each UAV to optimize channel selection and power control policies based on local observations, while a shared global reward encourages swarm-level cooperation. Extensive simulations show that, compared with baseline methods, the proposed method significantly improves link reliability, reduces power consumption, and lowers the overhead associated with frequent channel switching. Simulation results show that the proposed robust, energy-efficient communication strategy effectively improves the overall performance of the ITS urban traffic monitoring UAV swarm system.
{"title":"A Robust Multi-Domain Adaptive Anti-Jamming Communication System for a UAV Swarm in Urban ITS Traffic Monitoring via Multi-Agent Deep Deterministic Policy Gradient","authors":"Mu Chen;Yong Li;Zaojian Dai;Tao Zhang;Yu Zhou;Hui Wang","doi":"10.1109/TITS.2025.3584216","DOIUrl":"https://doi.org/10.1109/TITS.2025.3584216","url":null,"abstract":"Intelligent Transportation Systems (ITS) hold a central position in urban traffic strategies. Reliable and timely communication is crucial for the effective operation of ITS, because it requires uninterrupted real-time data to ensure safe and efficient traffic flow. As an indispensable component of ITS, Uncrewed Aerial Vehicles (UAVs) offer the agility, rapid deployment, and wide area vantage required for city-scale monitoring and prompt incident response. However, the crowded urban spectrum—characterized by co-channel interference, malicious jamming, and stringent spectrum and energy constraints—compromises the reliability and timeliness of UAV communications. This study investigates the anti-jamming communication problem for a UAV swarm applied to urban traffic monitoring and models this task as a decentralized, partially observable Markov decision process (Dec-POMDP). Based on this model, we develop a multi-domain adaptive scheme based on the multi-agent deep deterministic policy gradient (MADDPG) framework. The combination of centralized training and decentralized execution enables each UAV to optimize channel selection and power control policies based on local observations, while a shared global reward encourages swarm-level cooperation. Extensive simulations show that, compared with baseline methods, the proposed method significantly improves link reliability, reduces power consumption, and lowers the overhead associated with frequent channel switching. Simulation results show that the proposed robust, energy-efficient communication strategy effectively improves the overall performance of the ITS urban traffic monitoring UAV swarm system.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 2","pages":"2777-2793"},"PeriodicalIF":8.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223752","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}
Point cloud completion concerns the inference of the completed geometries for real-scanned point clouds that are sparse and incomplete due to occlusion, noise, and viewpoint. Previous methods usually learn a one-shot partial-to-complete mapping, which is incapable of generating fine structure details for the complex point cloud distributions. In this paper, a Hierarchical Geometry Generation point completion Network (HGG-Net) is proposed to hierarchically generate the fine-grained completed point cloud with a skeleton-to-details strategy, which consists of three fundamental modules, namely Transformer-enhanced Feature Encoder (TFE), Multi-level Geometry Representation Decoder (MGRD), and Hierarchical Dynamic Geometry Generator (HDG). Specifically, TFE first extracts geometry features of the incomplete input and obtains a coarse prediction via self-attention mechanism and edge convolution. Second, MGRD obtains the multi-level decoded geometry representations by Geometric Interactive Transformer (GIT) and Channel-Attention-based Geometry Features Fusion (CAGF), where GIT is proposed to decode the complete prompt by capturing the semantic relationship between geometry features of the incomplete and the decoded complete objects, and CAGF aims to fuse them for the high-quality representation. Third, HDG generates the complete points hierarchically from skeleton to details based on the Dynamic Graph Attention mechanism. Qualitative and quantitative experiments demonstrate that the proposed HGG-Net outperforms state-of-the-art methods on several point cloud completion datasets. Our code is available at https://github.com/haalexx/HGGNet.
{"title":"HGG-Net: Hierarchical Geometry Generation Network for Point Cloud Completion","authors":"Hao Liang;Zhaoshui He;Xu Wang;Wenqing Su;Ji Tan;Shengli Xie","doi":"10.1109/TITS.2025.3584474","DOIUrl":"https://doi.org/10.1109/TITS.2025.3584474","url":null,"abstract":"Point cloud completion concerns the inference of the completed geometries for real-scanned point clouds that are sparse and incomplete due to occlusion, noise, and viewpoint. Previous methods usually learn a one-shot partial-to-complete mapping, which is incapable of generating fine structure details for the complex point cloud distributions. In this paper, a Hierarchical Geometry Generation point completion Network (HGG-Net) is proposed to hierarchically generate the fine-grained completed point cloud with a skeleton-to-details strategy, which consists of three fundamental modules, namely Transformer-enhanced Feature Encoder (TFE), Multi-level Geometry Representation Decoder (MGRD), and Hierarchical Dynamic Geometry Generator (HDG). Specifically, TFE first extracts geometry features of the incomplete input and obtains a coarse prediction via self-attention mechanism and edge convolution. Second, MGRD obtains the multi-level decoded geometry representations by Geometric Interactive Transformer (GIT) and Channel-Attention-based Geometry Features Fusion (CAGF), where GIT is proposed to decode the complete prompt by capturing the semantic relationship between geometry features of the incomplete and the decoded complete objects, and CAGF aims to fuse them for the high-quality representation. Third, HDG generates the complete points hierarchically from skeleton to details based on the Dynamic Graph Attention mechanism. Qualitative and quantitative experiments demonstrate that the proposed HGG-Net outperforms state-of-the-art methods on several point cloud completion datasets. Our code is available at <uri>https://github.com/haalexx/HGGNet</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"12999-13010"},"PeriodicalIF":8.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315340","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 : 2025-07-14DOI: 10.1109/TITS.2025.3585801
Selim Reza;Marta Campos Ferreira;J.J.M. Machado;João Manuel R. S. Tavares
Acoustic monitoring of road traffic events is an indispensable element of Intelligent Transport Systems to increase their effectiveness. It aims to detect the temporal activity of sound events in road traffic auditory scenes and classify their occurrences. Current state-of-the-art algorithms have limitations in capturing long-range dependencies between different audio features to achieve robust performance. Additionally, these models suffer from external noise and variation in audio intensities. Therefore, this study proposes a spectrogram-specific transformer model employing a multi-head attention mechanism using the scaled product attention technique based on softmax in combination with Temporal Convolutional Networks to overcome these difficulties with increased accuracy and robustness. It also proposes a unique preprocessing step and a Deep Linear Projection method to reduce the dimensions of the features before passing them to the learnable Positional Encoding layer. Rather than monophonic audio data samples, stereophonic Mel-spectrogram features are fed into the model, improving the model’s robustness to noise. State-of-the-art One-dimensional Convolutional Neural Networks and Long Short-term Memory models were used to compare the proposed model’s performance on two well-known datasets. The results demonstrated its superior performance by achieving an improvement in accuracy of 1.51 to 3.55% compared to the studied baselines.
{"title":"Road Traffic Events Monitoring Using a Multi-Head Attention Mechanism-Based Transformer and Temporal Convolutional Networks","authors":"Selim Reza;Marta Campos Ferreira;J.J.M. Machado;João Manuel R. S. Tavares","doi":"10.1109/TITS.2025.3585801","DOIUrl":"https://doi.org/10.1109/TITS.2025.3585801","url":null,"abstract":"Acoustic monitoring of road traffic events is an indispensable element of Intelligent Transport Systems to increase their effectiveness. It aims to detect the temporal activity of sound events in road traffic auditory scenes and classify their occurrences. Current state-of-the-art algorithms have limitations in capturing long-range dependencies between different audio features to achieve robust performance. Additionally, these models suffer from external noise and variation in audio intensities. Therefore, this study proposes a spectrogram-specific transformer model employing a multi-head attention mechanism using the scaled product attention technique based on softmax in combination with Temporal Convolutional Networks to overcome these difficulties with increased accuracy and robustness. It also proposes a unique preprocessing step and a Deep Linear Projection method to reduce the dimensions of the features before passing them to the learnable Positional Encoding layer. Rather than monophonic audio data samples, stereophonic Mel-spectrogram features are fed into the model, improving the model’s robustness to noise. State-of-the-art One-dimensional Convolutional Neural Networks and Long Short-term Memory models were used to compare the proposed model’s performance on two well-known datasets. The results demonstrated its superior performance by achieving an improvement in accuracy of 1.51 to 3.55% compared to the studied baselines.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"13011-13024"},"PeriodicalIF":8.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315456","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 : 2025-07-14DOI: 10.1109/TITS.2025.3585504
Qingyuan Shen;Haobin Jiang;Aoxue Li;Marco Cecotti;Chenhui Yin;You Gong
Planning paths for Frenet-based autonomous vehicles (AVs) at roundabouts is difficult without complete and smooth reference paths. In such situations, the interpolating curve planner is often used to create segmented reference paths from simplified geometric roundabout data. While this method ensures curvature continuity within each curve segment, the continuity at the junctions of these segments is poor. Additionally, the determination of merging and diverging point positions at roundabouts has not been thoroughly explored. This paper introduces a novel approach using 5th-order Bézier curves to plan piecewise reference paths for AVs at roundabouts. The proposed method enhances endpoint curvature continuity of the Bézier curves and improves adaptability to non-standard roundabouts. A well-designed objective function is created to optimize both the geometric continuity parameters of the Bézier curves and the positions of merging and diverging points in the circulatory roadway. This function takes into account key factors, including path length and smoothness. Case studies validate the feasibility of maintaining curvature continuity at the endpoints and the method’s ability to generalize across various scenarios, proving its effectiveness for different roundabout structures. The results also confirm the method’s efficacy in generating paths from original geometric roundabout data. Lastly, the acceptable transverse deviations between real-world trajectories and reference paths demonstrate the rationality and practical applicability of this method.
{"title":"Generating G2 Continuity Reference Paths for Autonomous Vehicles at Roundabouts","authors":"Qingyuan Shen;Haobin Jiang;Aoxue Li;Marco Cecotti;Chenhui Yin;You Gong","doi":"10.1109/TITS.2025.3585504","DOIUrl":"https://doi.org/10.1109/TITS.2025.3585504","url":null,"abstract":"Planning paths for Frenet-based autonomous vehicles (AVs) at roundabouts is difficult without complete and smooth reference paths. In such situations, the interpolating curve planner is often used to create segmented reference paths from simplified geometric roundabout data. While this method ensures curvature continuity within each curve segment, the continuity at the junctions of these segments is poor. Additionally, the determination of merging and diverging point positions at roundabouts has not been thoroughly explored. This paper introduces a novel approach using 5th-order Bézier curves to plan piecewise reference paths for AVs at roundabouts. The proposed method enhances endpoint curvature continuity of the Bézier curves and improves adaptability to non-standard roundabouts. A well-designed objective function is created to optimize both the geometric continuity parameters of the Bézier curves and the positions of merging and diverging points in the circulatory roadway. This function takes into account key factors, including path length and smoothness. Case studies validate the feasibility of maintaining curvature continuity at the endpoints and the method’s ability to generalize across various scenarios, proving its effectiveness for different roundabout structures. The results also confirm the method’s efficacy in generating paths from original geometric roundabout data. Lastly, the acceptable transverse deviations between real-world trajectories and reference paths demonstrate the rationality and practical applicability of this method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"13082-13093"},"PeriodicalIF":8.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315533","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}
In the era of big data, intelligent transportation systems are crucial for the development of smart cities, significantly impacting urban economic growth and planning. The integration of 6G networks and digital twin technology presents unprecedented opportunities to enhance urban traffic management through real-time data synchronization and high-fidelity simulations. Accurate traffic flow prediction is vital for congestion control, intelligent route planning, and effective urban traffic management. However, existing deep learning models often struggle to capture the complex spatio-temporal dependencies and dynamic spatial relationships inherent in urban traffic data, particularly in data-scarce environments. Given the spatial heterogeneity of urban data, where dense and sparse regions coexist, improving prediction accuracy in sparse areas is critical to ensuring overall forecasting performance. To address these challenges, we propose a novel framework called 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction (DT-CTFP), which integrates advanced deep learning models within a 6G-supported digital twin environment. The framework leverages real-time data processing capabilities and ultra-low latency of 6G networks to capture complex traffic features and dynamic spatial dependencies. In data-rich regions, the Dynamic Graph Multi-Attention (DGMA) model is used to learn fine-grained spatio-temporal patterns, while for data-scarce regions, the Cross-Area Transfer Prediction (CATP) model utilizes meta-learning techniques to transfer knowledge from data-rich urban areas, improving prediction accuracy in areas with limited data. Experimental results demonstrate the superiority of the DT-CTFP framework, achieving up to 6% reductions in RMSE and 4% reductions in MAE across multiple datasets, highlighting its enhanced prediction accuracy and efficiency. These results emphasize the framework’s capacity to improve traffic management and vehicle-road cooperation within a digital twin smart city.
{"title":"DT-CTFP: 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction","authors":"Baofu Wu;Jilin Zhang;Junfeng Yuan;Yan Zeng;Peng Zhan;Yuyu Yin;Jian Wan;Honghao Gao","doi":"10.1109/TITS.2025.3582356","DOIUrl":"https://doi.org/10.1109/TITS.2025.3582356","url":null,"abstract":"In the era of big data, intelligent transportation systems are crucial for the development of smart cities, significantly impacting urban economic growth and planning. The integration of 6G networks and digital twin technology presents unprecedented opportunities to enhance urban traffic management through real-time data synchronization and high-fidelity simulations. Accurate traffic flow prediction is vital for congestion control, intelligent route planning, and effective urban traffic management. However, existing deep learning models often struggle to capture the complex spatio-temporal dependencies and dynamic spatial relationships inherent in urban traffic data, particularly in data-scarce environments. Given the spatial heterogeneity of urban data, where dense and sparse regions coexist, improving prediction accuracy in sparse areas is critical to ensuring overall forecasting performance. To address these challenges, we propose a novel framework called 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction (DT-CTFP), which integrates advanced deep learning models within a 6G-supported digital twin environment. The framework leverages real-time data processing capabilities and ultra-low latency of 6G networks to capture complex traffic features and dynamic spatial dependencies. In data-rich regions, the Dynamic Graph Multi-Attention (DGMA) model is used to learn fine-grained spatio-temporal patterns, while for data-scarce regions, the Cross-Area Transfer Prediction (CATP) model utilizes meta-learning techniques to transfer knowledge from data-rich urban areas, improving prediction accuracy in areas with limited data. Experimental results demonstrate the superiority of the DT-CTFP framework, achieving up to 6% reductions in RMSE and 4% reductions in MAE across multiple datasets, highlighting its enhanced prediction accuracy and efficiency. These results emphasize the framework’s capacity to improve traffic management and vehicle-road cooperation within a digital twin smart city.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18129-18144"},"PeriodicalIF":8.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384612","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}
The integration among 6G communication networks, power grids, and transportation systems is emerging as a promising paradigm to achieve mutual benefits among autonomous-driving electric vehicle (EV) users, communication operators, and power grids. Task offloading strategies for autonomous driving and the traveling patterns of EVs can induce communication load fluctuation within 6G network, which subsequently influences energy flow in power grid. Conversely, electricity price from the power grid affects EV charging/discharging strategies, impacting traffic flow and autonomous driving task offloading within the 6G network. Based on the interdependencies among the three networks, this paper constructs a communication-power-transportation coupling network with 6G base stations (BSs) and fast charge stations (FCSs) acting as coupling hubs. Besides, a spatio-temporal electricity price model considering spatial traffic distribution and temporal load fluctuation is developed. Moreover, the optimization problem is formulated to jointly coordinate FCS selection, bidirectional charging/discharging power regulation, task offloading decisions, and route selection strategies to maximize demand response quality of experience (QoE), grid stability and balance under the constraint of autonomous driving quality of service (QoS). Then, a knowledge transfer collaboration-based spatio-temporal EV task offloading, energy, and traffic management joint optimization algorithm is proposed, which improves the optimization performance through knowledge transfer collaboration among EV. Finally, simulation results validate the performance improvement of the proposed algorithm in demand response QoE, grid stability and balance, and autonomous driving QoS.
{"title":"Spatio-Temporal EV Task Offloading, Energy, and Traffic Management for 6G Communication-Power-Transportation Coupling Network","authors":"Chao Pan;Ziming Li;Haoyu Ci;Haijun Liao;Zhenyu Zhou;Anwer Al-Dulaimi;Muhammad Tariq","doi":"10.1109/TITS.2025.3574402","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574402","url":null,"abstract":"The integration among 6G communication networks, power grids, and transportation systems is emerging as a promising paradigm to achieve mutual benefits among autonomous-driving electric vehicle (EV) users, communication operators, and power grids. Task offloading strategies for autonomous driving and the traveling patterns of EVs can induce communication load fluctuation within 6G network, which subsequently influences energy flow in power grid. Conversely, electricity price from the power grid affects EV charging/discharging strategies, impacting traffic flow and autonomous driving task offloading within the 6G network. Based on the interdependencies among the three networks, this paper constructs a communication-power-transportation coupling network with 6G base stations (BSs) and fast charge stations (FCSs) acting as coupling hubs. Besides, a spatio-temporal electricity price model considering spatial traffic distribution and temporal load fluctuation is developed. Moreover, the optimization problem is formulated to jointly coordinate FCS selection, bidirectional charging/discharging power regulation, task offloading decisions, and route selection strategies to maximize demand response quality of experience (QoE), grid stability and balance under the constraint of autonomous driving quality of service (QoS). Then, a knowledge transfer collaboration-based spatio-temporal EV task offloading, energy, and traffic management joint optimization algorithm is proposed, which improves the optimization performance through knowledge transfer collaboration among EV. Finally, simulation results validate the performance improvement of the proposed algorithm in demand response QoE, grid stability and balance, and autonomous driving QoS.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18044-18057"},"PeriodicalIF":8.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405261","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 : 2025-07-02DOI: 10.1109/TITS.2025.3579612
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2025.3579612","DOIUrl":"https://doi.org/10.1109/TITS.2025.3579612","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"C3-C3"},"PeriodicalIF":7.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11063252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536496","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}
Pub Date : 2025-07-02DOI: 10.1109/TITS.2025.3580163
Simona Sacone
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供：本刊发表的文章摘要。
{"title":"Scanning the Issue","authors":"Simona Sacone","doi":"10.1109/TITS.2025.3580163","DOIUrl":"https://doi.org/10.1109/TITS.2025.3580163","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9138-9164"},"PeriodicalIF":7.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11063254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536503","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}
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