Pub Date : 2025-11-03DOI: 10.1109/TITS.2025.3624395
Jiankai Zuo;Yuxiang Yao;Yaying Zhang
The contemporary urban intelligent transportation system (ITS) generates an enormous amount of trajectory data daily, serving as an essential reflection of traffic dynamics. Accurate estimation of arrival time by mining spatio-temporal features and semantic relationships from historical trajectories has become increasingly vital. However, most existing works overlook the joint features between links (i.e., road segments) and crossroads in trajectories. Additionally, they often treat all links uniformly without considering the semantics of critical links, leading to deficiencies in captured representation. To address these issues, this study proposes a novel deep encoder learning framework called the Triple Feature Encoder-based Dual-Granularity Graph Learning Network (TriDGNet) for enhanced travel time estimation. Specifically, we design a triple feature learning encoder to explore the spatio-temporal correlations of trajectories from three perspectives: Depth, Ensemble, and Sequence. Meanwhile, we introduce a consistent modeling method to integrate both links and crossroads. Furthermore, we construct two graph learning modules at different scales. One is an edge-enhanced graph attention network (E-GAT) to capture global spatial dependencies across the entire road network. The other is a backtracking-based subgraph representation network (BackNet) to learn local contextual information from bustling links. Our proposed TriDGNet model has been evaluated on three extensive datasets. The experimental results demonstrate that it outperforms state-of-the-art approaches.
{"title":"TriDGNet: Triple Feature Encoder-Based Dual Granularity Graph Learning Network for Enhanced Travel Time Estimation","authors":"Jiankai Zuo;Yuxiang Yao;Yaying Zhang","doi":"10.1109/TITS.2025.3624395","DOIUrl":"https://doi.org/10.1109/TITS.2025.3624395","url":null,"abstract":"The contemporary urban intelligent transportation system (ITS) generates an enormous amount of trajectory data daily, serving as an essential reflection of traffic dynamics. Accurate estimation of arrival time by mining spatio-temporal features and semantic relationships from historical trajectories has become increasingly vital. However, most existing works overlook the joint features between links (i.e., road segments) and crossroads in trajectories. Additionally, they often treat all links uniformly without considering the semantics of critical links, leading to deficiencies in captured representation. To address these issues, this study proposes a novel deep encoder learning framework called the Triple Feature Encoder-based Dual-Granularity Graph Learning Network (TriDGNet) for enhanced travel time estimation. Specifically, we design a triple feature learning encoder to explore the spatio-temporal correlations of trajectories from three perspectives: Depth, Ensemble, and Sequence. Meanwhile, we introduce a consistent modeling method to integrate both links and crossroads. Furthermore, we construct two graph learning modules at different scales. One is an edge-enhanced graph attention network (E-GAT) to capture global spatial dependencies across the entire road network. The other is a backtracking-based subgraph representation network (BackNet) to learn local contextual information from bustling links. Our proposed TriDGNet model has been evaluated on three extensive datasets. The experimental results demonstrate that it outperforms state-of-the-art approaches.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1606-1620"},"PeriodicalIF":8.4,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877105","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 : 2025-11-03DOI: 10.1109/TITS.2025.3619092
Zihan Wang;Mengran Li;Ronghui Zhang;Jing Zhao;Chuan Hu;Xiaolei Ma;Tony Z. Qiu
With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to maintain stability when driver reaction time fluctuates, as these variations require different levels of system intervention. To address this issue, the proposed human-machine shared vehicle following assistance system (HM-VFAS) integrates driver outputs under various states with the assistance system. The system employs an intelligent driver model that accounts for reaction time delays, simulating time-varying driver outputs. A control authority allocation strategy is designed to dynamically adjust the level of intervention based on real-time driver state assessment. To handle instability from driver authority switching, the proposed solution includes a two-layer adaptive finite time sliding mode controller (A-FTSMC). The first layer is an integral sliding mode adaptive controller that ensures robustness by compensating for uncertainties in the driver output. The second layer is a fast non-singular terminal sliding mode controller designed to accelerate convergence for rapid stabilization. Based on the driver-in-the-loop experimental results using the intelligent cockpit system, the performance of the HM-VFAS was evaluated. Results show that the proposed control strategy maintains a safe distance under time-varying driver states, with the actual acceleration error relative to the target acceleration maintained within $pm 0.6! text {m/s}^{2}$ and the maximum acceleration error reduced by $1.3! text {m/s}^{2}$ . Compared to traditional controllers, the A-FTSMC controller offers faster convergence and less vibration, reducing the stabilization time by 26.8%.
随着智能网联汽车技术的发展，人机共享控制因其在驾驶辅助方面的有效性而在汽车跟随领域得到了广泛的应用。然而，当驾驶员的反应时间波动时，传统的车辆跟随系统难以保持稳定性，因为这些变化需要不同程度的系统干预。为了解决这一问题，提出了人机共享车辆跟随辅助系统（HM-VFAS），该系统将驾驶员在不同状态下的输出与辅助系统相结合。该系统采用了考虑反应时间延迟的智能驱动模型，模拟了随时间变化的驱动输出。设计了一种基于实时驾驶员状态评估动态调整干预水平的控制权限分配策略。为了处理由驱动权限切换引起的不稳定性，提出的方案包括一个两层自适应有限时间滑模控制器（a - ftsmc）。第一层是一个积分滑模自适应控制器，通过补偿驱动器输出中的不确定性来确保鲁棒性。第二层是快速非奇异终端滑模控制器，旨在加速收敛以实现快速稳定。基于智能座舱系统驾驶员在环试验结果，对该系统的性能进行了评价。结果表明，该控制策略在驾驶员时变状态下保持安全距离，相对于目标加速度的实际加速度误差保持在$pm 0.6！ text {m/s}^{2}$和最大加速度误差减少$1.3！ text {m/s}^{2}$。与传统控制器相比，A-FTSMC控制器收敛速度更快，振动更小，稳定时间缩短26.8%。
{"title":"Effective Finite Time Stability Control for Human–Machine Shared Vehicle Following System","authors":"Zihan Wang;Mengran Li;Ronghui Zhang;Jing Zhao;Chuan Hu;Xiaolei Ma;Tony Z. Qiu","doi":"10.1109/TITS.2025.3619092","DOIUrl":"https://doi.org/10.1109/TITS.2025.3619092","url":null,"abstract":"With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to maintain stability when driver reaction time fluctuates, as these variations require different levels of system intervention. To address this issue, the proposed human-machine shared vehicle following assistance system (HM-VFAS) integrates driver outputs under various states with the assistance system. The system employs an intelligent driver model that accounts for reaction time delays, simulating time-varying driver outputs. A control authority allocation strategy is designed to dynamically adjust the level of intervention based on real-time driver state assessment. To handle instability from driver authority switching, the proposed solution includes a two-layer adaptive finite time sliding mode controller (A-FTSMC). The first layer is an integral sliding mode adaptive controller that ensures robustness by compensating for uncertainties in the driver output. The second layer is a fast non-singular terminal sliding mode controller designed to accelerate convergence for rapid stabilization. Based on the driver-in-the-loop experimental results using the intelligent cockpit system, the performance of the HM-VFAS was evaluated. Results show that the proposed control strategy maintains a safe distance under time-varying driver states, with the actual acceleration error relative to the target acceleration maintained within <inline-formula> <tex-math>$pm 0.6! text {m/s}^{2}$ </tex-math></inline-formula> and the maximum acceleration error reduced by <inline-formula> <tex-math>$1.3! text {m/s}^{2}$ </tex-math></inline-formula>. Compared to traditional controllers, the A-FTSMC controller offers faster convergence and less vibration, reducing the stabilization time by 26.8%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23282-23297"},"PeriodicalIF":8.4,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665720","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 : 2025-10-29DOI: 10.1109/TITS.2025.3617384
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2025.3617384","DOIUrl":"https://doi.org/10.1109/TITS.2025.3617384","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"C3-C3"},"PeriodicalIF":8.4,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11220919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384613","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 : 2025-10-29DOI: 10.1109/TITS.2025.3618880
Li-Ying Hao;Yuxing Zhou;Run-Zhi Wang;Xudong Zhao
The network security of autonomous surface vehicles (ASVs) is critical in intelligent maritime transportation. However, denial-of-service (DoS) attacks can disrupt information transmission in remote communications and compromise the stability of ASVs. To address this challenge, the article proposes a robust and resilient finite-time Lyapunov-based model predictive control (FTLMPC) strategy to resist the malicious impact of DoS attacks and extend the permissible duration of attacks. Concretely, a novel contraction constraint, derived from a finite-time auxiliary control system, is integrated into the FTLMPC framework, leveraging virtual control signals to identify and manage input saturation to enlarge the attraction domain. Additionally, a adjustment mechanism based on saturation factor is introduced to cope with DoS attacks, enabling flexible adaptation of the convergence rate and attraction domain based on the permissible duration of DoS attacks. The proposed strategy ensures finite-time stability under attack conditions while expanding the attraction domain. Simulation results demonstrate the effectiveness and benefits of the proposed algorithm.
{"title":"Finite-Time Lyapunov-Based Model Predictive Control of ASVs: An Enlarging Attraction Domain Strategy Against DoS Attacks","authors":"Li-Ying Hao;Yuxing Zhou;Run-Zhi Wang;Xudong Zhao","doi":"10.1109/TITS.2025.3618880","DOIUrl":"https://doi.org/10.1109/TITS.2025.3618880","url":null,"abstract":"The network security of autonomous surface vehicles (ASVs) is critical in intelligent maritime transportation. However, denial-of-service (DoS) attacks can disrupt information transmission in remote communications and compromise the stability of ASVs. To address this challenge, the article proposes a robust and resilient finite-time Lyapunov-based model predictive control (FTLMPC) strategy to resist the malicious impact of DoS attacks and extend the permissible duration of attacks. Concretely, a novel contraction constraint, derived from a finite-time auxiliary control system, is integrated into the FTLMPC framework, leveraging virtual control signals to identify and manage input saturation to enlarge the attraction domain. Additionally, a adjustment mechanism based on saturation factor is introduced to cope with DoS attacks, enabling flexible adaptation of the convergence rate and attraction domain based on the permissible duration of DoS attacks. The proposed strategy ensures finite-time stability under attack conditions while expanding the attraction domain. Simulation results demonstrate the effectiveness and benefits of the proposed algorithm.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23257-23268"},"PeriodicalIF":8.4,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665786","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}
Conditional anonymous authentication can provide anonymity and traceability to Vehicular Ad-Hoc Networks (VANETs), which protects users’ privacy while resisting malicious users and false messages. However, existing schemes suffer from various disadvantages, such as unavailable batch verification, unrenewable user public keys/certificates, and untimely revocation. In this paper, we propose an efficient conditional anonymous authentication scheme with on-chain key management (ECAKM) in VANETs. To achieve lightweight authentication, we design an efficient Signature of Knowledge (SoK) and a batch verification algorithm. We also employ a Bloom filter on the chain to manage the information about revoked anonymous public keys to further improve the efficiency of our scheme. Moreover, we adopt hash chain technology to update users’ anonymous public keys and protect vehicles against linkage attacks. In addition, based on the blockchain and smart contract (SC), we can manage anonymous public keys of users efficiently and transparently. Security analysis and experimental results demonstrate that our scheme ensures conditional privacy with a reduced authentication overhead.
{"title":"ECAKM: Efficient Conditional Anonymous Authentication Scheme With On-Chain Key Management in VANETs","authors":"Shunrong Jiang;Xiao Zhang;Guohuai Sang;Haotian Chi;Yong Zhou","doi":"10.1109/TITS.2025.3622410","DOIUrl":"https://doi.org/10.1109/TITS.2025.3622410","url":null,"abstract":"Conditional anonymous authentication can provide anonymity and traceability to Vehicular Ad-Hoc Networks (VANETs), which protects users’ privacy while resisting malicious users and false messages. However, existing schemes suffer from various disadvantages, such as unavailable batch verification, unrenewable user public keys/certificates, and untimely revocation. In this paper, we propose an efficient conditional anonymous authentication scheme with on-chain key management (ECAKM) in VANETs. To achieve lightweight authentication, we design an efficient Signature of Knowledge (SoK) and a batch verification algorithm. We also employ a Bloom filter on the chain to manage the information about revoked anonymous public keys to further improve the efficiency of our scheme. Moreover, we adopt hash chain technology to update users’ anonymous public keys and protect vehicles against linkage attacks. In addition, based on the blockchain and smart contract (SC), we can manage anonymous public keys of users efficiently and transparently. Security analysis and experimental results demonstrate that our scheme ensures conditional privacy with a reduced authentication overhead.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23407-23418"},"PeriodicalIF":8.4,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665741","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 development of time-based flow management has significantly enhanced the safety, reliability, and predictability of air traffic control (ATC). Actual flight paths often deviate from these standard terminal arrival routes due to pilots requesting shortcut arrivals or ATC officers implementing holding procedures to alleviate congestion. These deviations exacerbate the dynamic complexity of air traffic management (ATM). To address these challenges, we propose a novel online learning Transformer-bidirectional gated recurrent unit (Transformer-BiGRU) framework for tactical spatiotemporal flight trajectory prediction. BiGRU further obtains bidirectional sequence information to improve the Transformer’s spatiotemporal prediction. The proposed research utilises image processing techniques to produce ATC aeronautical holding instructions from historical automatic dependent surveillance-broadcast data. The framework significantly improves real-time prediction ability and environment adaptability by integrating holding instructions and online learning. Experiment results demonstrate that incorporating holding instructions with the proposed Transformer-BiGRU reduces the mean absolute error by approximately 10% in latitude, 8.9% in longitude, and 13.1% in flight level compared to the best baseline model. Furthermore, the mean deviation error of horizontal distance decreases from 0.49 to 0.42 nautical miles (a 13% improvement). These results confirm that the methodology benefits real-time ATC decision-making in various ATM scenarios and provides valuable insights to assure airspace safety.
{"title":"A Spatiotemporal Flight Trajectory Prediction and Online Learning Framework Based on Integrated Transformer-Bidirectional Gated Recurrent Unit","authors":"Ye Liu;Kam Hung Ng;Cheng-Lung Wu;Nana Chu;Xiaoge Zhang;Kai Kwong Hon;Christy Yan-Yu Leung","doi":"10.1109/TITS.2025.3614658","DOIUrl":"https://doi.org/10.1109/TITS.2025.3614658","url":null,"abstract":"The development of time-based flow management has significantly enhanced the safety, reliability, and predictability of air traffic control (ATC). Actual flight paths often deviate from these standard terminal arrival routes due to pilots requesting shortcut arrivals or ATC officers implementing holding procedures to alleviate congestion. These deviations exacerbate the dynamic complexity of air traffic management (ATM). To address these challenges, we propose a novel online learning Transformer-bidirectional gated recurrent unit (Transformer-BiGRU) framework for tactical spatiotemporal flight trajectory prediction. BiGRU further obtains bidirectional sequence information to improve the Transformer’s spatiotemporal prediction. The proposed research utilises image processing techniques to produce ATC aeronautical holding instructions from historical automatic dependent surveillance-broadcast data. The framework significantly improves real-time prediction ability and environment adaptability by integrating holding instructions and online learning. Experiment results demonstrate that incorporating holding instructions with the proposed Transformer-BiGRU reduces the mean absolute error by approximately 10% in latitude, 8.9% in longitude, and 13.1% in flight level compared to the best baseline model. Furthermore, the mean deviation error of horizontal distance decreases from 0.49 to 0.42 nautical miles (a 13% improvement). These results confirm that the methodology benefits real-time ATC decision-making in various ATM scenarios and provides valuable insights to assure airspace safety.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23374-23388"},"PeriodicalIF":8.4,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665799","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 : 2025-10-10DOI: 10.1109/TITS.2025.3584813
Ming He;Yunjie Bai;Hanqi Liu;Aimin Yang;Liya Wang
Deep neural networks (DNNs) are critical for obstacle recognition in autonomous driving, commonly used to classify objects like vehicles and animals. However, DNNs are vulnerable to adversarial attacks that can cause misclassifications and compromise system safety. To address this, we propose the Adaptive Multi-Scale Positional Encoding Parallel Attention Network (APANet), a model designed to enhance adversarial robustness. APANet includes four main components: multi-scale feature map generation, Adaptive Multi-Scale Positional Encoding (AMSPE), Parallel Attention (PA), and multi-scale feature fusion. AMSPE embeds adaptive positional information and captures long-range dependencies to boost resistance to adversarial perturbations. PA independently processes multi-scale features, enhancing feature utilization and isolating adversarial noise. These components work synergistically to improve the model’s robustness. Experiments show APANet significantly outperforms several state-of-the-art models in Top-1 accuracy under various adversarial attacks and on clean samples. Specifically, AMSPE contributes a 4.13-point improvement in adversarial accuracy and narrows the clean-adversarial performance gap by 4.73 points, while PA improves recognition accuracy by 6.11 points. To validate real-world robustness, we tested APANet on the German Traffic Sign Recognition Benchmark (GTSRB), where adversarial interference can critically affect autonomous driving. APANet demonstrates high accuracy and robustness under adversarial scenarios on GTSRB, confirming its effectiveness in enhancing the safety and reliability of autonomous driving systems.
{"title":"High Adversarial Robustness Network: Adaptive Positional Encoding and Parallel Attention for Obstacle Recognition in Autonomous Driving","authors":"Ming He;Yunjie Bai;Hanqi Liu;Aimin Yang;Liya Wang","doi":"10.1109/TITS.2025.3584813","DOIUrl":"https://doi.org/10.1109/TITS.2025.3584813","url":null,"abstract":"Deep neural networks (DNNs) are critical for obstacle recognition in autonomous driving, commonly used to classify objects like vehicles and animals. However, DNNs are vulnerable to adversarial attacks that can cause misclassifications and compromise system safety. To address this, we propose the Adaptive Multi-Scale Positional Encoding Parallel Attention Network (APANet), a model designed to enhance adversarial robustness. APANet includes four main components: multi-scale feature map generation, Adaptive Multi-Scale Positional Encoding (AMSPE), Parallel Attention (PA), and multi-scale feature fusion. AMSPE embeds adaptive positional information and captures long-range dependencies to boost resistance to adversarial perturbations. PA independently processes multi-scale features, enhancing feature utilization and isolating adversarial noise. These components work synergistically to improve the model’s robustness. Experiments show APANet significantly outperforms several state-of-the-art models in Top-1 accuracy under various adversarial attacks and on clean samples. Specifically, AMSPE contributes a 4.13-point improvement in adversarial accuracy and narrows the clean-adversarial performance gap by 4.73 points, while PA improves recognition accuracy by 6.11 points. To validate real-world robustness, we tested APANet on the German Traffic Sign Recognition Benchmark (GTSRB), where adversarial interference can critically affect autonomous driving. APANet demonstrates high accuracy and robustness under adversarial scenarios on GTSRB, confirming its effectiveness in enhancing the safety and reliability of autonomous driving systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18145-18156"},"PeriodicalIF":8.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384578","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 : 2025-10-09DOI: 10.1109/TITS.2025.3610889
Xin Liu;Wenyi Yang;Li Li;Zechen Liu;Yuemin Liu;Feng Li
Since uncrewed aerial vehicles (UAVs) possess inherent characteristics such as exceptional maneuverability and versatile deployment, they can offer integrated sensing and communication (ISAC) services to vehicles in mobile environment. This paper designs a UAV-assisted ISAC system model, wherein the UAV is employed to provide sensing and communication services to mobile vehicles during its flight. In order to evaluate the radar detection performance of the ISAC system, we introduce radar mutual information (MI) from the information theory perspective. A resource optimization problem for the system model is formulated, which seeks to maximize the system communication rate under the constraints of signal-to-noise ratio (SNR) and MI of the radar detection link by jointly optimizing ISAC task scheduling, UAV transmit power allocation and UAV flight trajectory. The simulation results indicate that the proposed scheme significantly improves both the communication rate and radar MI.
{"title":"UAV Assisted Integrated Sensing and Communication for Mobile Vehicles","authors":"Xin Liu;Wenyi Yang;Li Li;Zechen Liu;Yuemin Liu;Feng Li","doi":"10.1109/TITS.2025.3610889","DOIUrl":"https://doi.org/10.1109/TITS.2025.3610889","url":null,"abstract":"Since uncrewed aerial vehicles (UAVs) possess inherent characteristics such as exceptional maneuverability and versatile deployment, they can offer integrated sensing and communication (ISAC) services to vehicles in mobile environment. This paper designs a UAV-assisted ISAC system model, wherein the UAV is employed to provide sensing and communication services to mobile vehicles during its flight. In order to evaluate the radar detection performance of the ISAC system, we introduce radar mutual information (MI) from the information theory perspective. A resource optimization problem for the system model is formulated, which seeks to maximize the system communication rate under the constraints of signal-to-noise ratio (SNR) and MI of the radar detection link by jointly optimizing ISAC task scheduling, UAV transmit power allocation and UAV flight trajectory. The simulation results indicate that the proposed scheme significantly improves both the communication rate and radar MI.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"21335-21339"},"PeriodicalIF":8.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486520","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}
This paper proposes a finite-time prescribed performance control method to ensure effective two-dimensional (2-D) plane vehicle platoon multi-lane fusion and maintain platoon performance in the presence of unknown dynamic uncertainties and false data injection (FDI) attacks. Firstly, unknown dynamic uncertainties of the vehicle are approximated using radial basis function neural networks (RBFNNs). Building on this neural network approximation under FDI attacks, a novel state observer is developed to estimate the vehicle’s state, restore the communication protocol when the communication link is attacked, and address the complex coupling issues between vehicle states. Furthermore, finite-time prescribed performance control inputs are designed based on the constructed sliding surfaces to ensure practical finite-time stability of the 2-D plane vehicle platoon. This method facilitates vehicle multi-lane fusion within a finite time while guaranteeing platoon performance and preventing collisions. Finally, numerical simulations and comparative analyses are presented to demonstrate the effectiveness and superiority of the proposed control strategy, involving one leader vehicle and six followers.
{"title":"Finite-Time Multi-Lane Fusion Control for 2-D Plane Vehicle Platoon With FDI Attacks","authors":"Man-Fei Lin;Zhan Shu;Cheng-Lin Liu;Ya Zhang;Yang-Yang Chen","doi":"10.1109/TITS.2025.3611976","DOIUrl":"https://doi.org/10.1109/TITS.2025.3611976","url":null,"abstract":"This paper proposes a finite-time prescribed performance control method to ensure effective two-dimensional (2-D) plane vehicle platoon multi-lane fusion and maintain platoon performance in the presence of unknown dynamic uncertainties and false data injection (FDI) attacks. Firstly, unknown dynamic uncertainties of the vehicle are approximated using radial basis function neural networks (RBFNNs). Building on this neural network approximation under FDI attacks, a novel state observer is developed to estimate the vehicle’s state, restore the communication protocol when the communication link is attacked, and address the complex coupling issues between vehicle states. Furthermore, finite-time prescribed performance control inputs are designed based on the constructed sliding surfaces to ensure practical finite-time stability of the 2-D plane vehicle platoon. This method facilitates vehicle multi-lane fusion within a finite time while guaranteeing platoon performance and preventing collisions. Finally, numerical simulations and comparative analyses are presented to demonstrate the effectiveness and superiority of the proposed control strategy, involving one leader vehicle and six followers.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23314-23327"},"PeriodicalIF":8.4,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665788","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 : 2025-10-03DOI: 10.1109/TITS.2025.3615073
Weihang Pan;Binbin Lin;Yafei Wang;Zhengxu Yu;Xinkui Zhao;Xiaofei He;Jieping Ye
The decision-making process for connected and autonomous vehicles (CAVs) at unsignalized intersections is a critical and challenging problem. Previous methods predominantly concentrate on optimizing passage strategies for individual intersections in isolation. However, they often neglect global traffic conditions and task priorities in closed, multi-intersection transportation scenarios, leading to localized congestion. In this work, we propose a method that aims to optimize the passing order of intersections from a global and long-term perspective to enhance overall transportation efficiency. Specifically, we model the coordination of multiple unsignalized intersections as a multi-agent sequential decision problem and solve it through a two-stage method. In the planning stage, we construct fully connected undirected graphs based on vehicle conflict relationships and use the multi-agent proximal policy optimization (MAPPO) algorithm to learn the global priorities. In the scheduling stage, the local vehicle scheduling is formalized as a multi-objective optimization problem. The learned global priorities are soft constraints, while a hybrid filtered beam search determines safe and efficient CAV passing orders. Extensive offline experiments and online tests on real-world and synthetic datasets demonstrate that our proposed method outperforms state-of-the-art approaches in minimizing congestion and enhancing transportation efficiency.
{"title":"Cooperative Driving at Multiple Unsignalized Intersections in Fully Autonomous Driving Scenarios","authors":"Weihang Pan;Binbin Lin;Yafei Wang;Zhengxu Yu;Xinkui Zhao;Xiaofei He;Jieping Ye","doi":"10.1109/TITS.2025.3615073","DOIUrl":"https://doi.org/10.1109/TITS.2025.3615073","url":null,"abstract":"The decision-making process for connected and autonomous vehicles (CAVs) at unsignalized intersections is a critical and challenging problem. Previous methods predominantly concentrate on optimizing passage strategies for individual intersections in isolation. However, they often neglect global traffic conditions and task priorities in closed, multi-intersection transportation scenarios, leading to localized congestion. In this work, we propose a method that aims to optimize the passing order of intersections from a global and long-term perspective to enhance overall transportation efficiency. Specifically, we model the coordination of multiple unsignalized intersections as a multi-agent sequential decision problem and solve it through a two-stage method. In the planning stage, we construct fully connected undirected graphs based on vehicle conflict relationships and use the multi-agent proximal policy optimization (MAPPO) algorithm to learn the global priorities. In the scheduling stage, the local vehicle scheduling is formalized as a multi-objective optimization problem. The learned global priorities are soft constraints, while a hybrid filtered beam search determines safe and efficient CAV passing orders. Extensive offline experiments and online tests on real-world and synthetic datasets demonstrate that our proposed method outperforms state-of-the-art approaches in minimizing congestion and enhancing transportation efficiency.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23298-23313"},"PeriodicalIF":8.4,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665793","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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