Pub Date : 2025-11-07DOI: 10.1109/TITS.2025.3618307
Zhuolin He;Xinrun Li;Jiacheng Tang;Shoumeng Qiu;Wenfu Wang;Xiangyang Xue;Jian Pu
Conventional camera-based 3D object detectors in autonomous driving are limited to recognizing a predefined set of objects, which poses a safety risk when encountering novel or unseen objects in real-world scenarios. To address this limitation, we present OS-Det3D, a two-stage training framework designed for camera-based open-set 3D object detection. In the first stage, our proposed 3D object discovery network (ODN3D) uses geometric cues from LiDAR point clouds to generate class-agnostic 3D object proposals, each of which are assigned a 3D objectness score. This approach allows the network to discover objects beyond known categories, allowing for the detection of unfamiliar objects. However, due to the absence of class constraints, ODN3D-generated proposals may include noisy data, particularly in cluttered or dynamic scenes. To mitigate this issue, we introduce a joint selection (JS) module in the second stage. The JS module uses both camera bird’s eye view (BEV) feature responses and 3D objectness scores to filter out low-quality proposals, yielding high-quality pseudo ground truth for unknown objects. OS-Det3D significantly enhances the ability of camera 3D detectors to discover and identify unknown objects while also improving the performance on known objects, as demonstrated through extensive experiments on the nuScenes and KITTI datasets.
{"title":"Toward Camera Open-Set 3D Object Detection for Autonomous Driving Scenarios","authors":"Zhuolin He;Xinrun Li;Jiacheng Tang;Shoumeng Qiu;Wenfu Wang;Xiangyang Xue;Jian Pu","doi":"10.1109/TITS.2025.3618307","DOIUrl":"https://doi.org/10.1109/TITS.2025.3618307","url":null,"abstract":"Conventional camera-based 3D object detectors in autonomous driving are limited to recognizing a predefined set of objects, which poses a safety risk when encountering novel or unseen objects in real-world scenarios. To address this limitation, we present OS-Det3D, a two-stage training framework designed for camera-based open-set 3D object detection. In the first stage, our proposed 3D object discovery network (ODN3D) uses geometric cues from LiDAR point clouds to generate class-agnostic 3D object proposals, each of which are assigned a 3D objectness score. This approach allows the network to discover objects beyond known categories, allowing for the detection of unfamiliar objects. However, due to the absence of class constraints, ODN3D-generated proposals may include noisy data, particularly in cluttered or dynamic scenes. To mitigate this issue, we introduce a joint selection (JS) module in the second stage. The JS module uses both camera bird’s eye view (BEV) feature responses and 3D objectness scores to filter out low-quality proposals, yielding high-quality pseudo ground truth for unknown objects. OS-Det3D significantly enhances the ability of camera 3D detectors to discover and identify unknown objects while also improving the performance on known objects, as demonstrated through extensive experiments on the nuScenes and KITTI datasets.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23190-23201"},"PeriodicalIF":8.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665756","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 : 2025-11-07DOI: 10.1109/TITS.2025.3623119
Chanin Eom;Minhae Kwon
With the increasing focus on research on autonomous driving, road environments have evolved into mixed-autonomy traffic networks. In this context, developing an autonomous strategy that can reduce societal costs is important because autonomous vehicles have a direct impact on the entire traffic network. Deep reinforcement learning (RL), a promising autonomous decision-making process, typically leads to an egocentric strategy characterized by a static target and disregards rapidly changing traffic conditions. However, this approach can incur significant societal costs in complex traffic scenarios. In this study, we propose a fast-follower strategy that effectively reduces societal costs in a mixed-autonomy traffic network by dynamically adjusting the reward standards to accommodate varying traffic conditions. To assess the impact of autonomous strategies on transportation networks, we introduce a novel metric, the price of autonomous strategy (PoAS), which is designed to quantify the societal costs associated with autonomous decision-making. Additionally, we provide a traffic-aware analysis using PoAS to identify the driving conditions under which the fast-follower strategy results in a lower societal cost than the egocentric strategy. This theoretical analysis is validated using PoAS comparisons across various societal metrics and traffic conditions. The simulation results confirm that the fast-follower strategy outperforms other autonomous strategies in mixed and fully autonomous traffic networks.
{"title":"Price of the Autonomous Strategy With Reinforcement Learning in Mixed-Autonomy Traffic Networks","authors":"Chanin Eom;Minhae Kwon","doi":"10.1109/TITS.2025.3623119","DOIUrl":"https://doi.org/10.1109/TITS.2025.3623119","url":null,"abstract":"With the increasing focus on research on autonomous driving, road environments have evolved into mixed-autonomy traffic networks. In this context, developing an autonomous strategy that can reduce societal costs is important because autonomous vehicles have a direct impact on the entire traffic network. Deep reinforcement learning (RL), a promising autonomous decision-making process, typically leads to an <italic>egocentric strategy</i> characterized by a static target and disregards rapidly changing traffic conditions. However, this approach can incur significant societal costs in complex traffic scenarios. In this study, we propose a <italic>fast-follower strategy</i> that effectively reduces societal costs in a mixed-autonomy traffic network by dynamically adjusting the reward standards to accommodate varying traffic conditions. To assess the impact of autonomous strategies on transportation networks, we introduce a novel metric, the <italic>price of autonomous strategy</i> (PoAS), which is designed to quantify the societal costs associated with autonomous decision-making. Additionally, we provide a traffic-aware analysis using PoAS to identify the driving conditions under which the fast-follower strategy results in a lower societal cost than the egocentric strategy. This theoretical analysis is validated using PoAS comparisons across various societal metrics and traffic conditions. The simulation results confirm that the fast-follower strategy outperforms other autonomous strategies in mixed and fully autonomous traffic networks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 2","pages":"2741-2752"},"PeriodicalIF":8.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223596","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}
Cooperative perception has significant potential to enhance perception performance compared to single-agent systems by integrating information from multiple agents through vehicle-to-everything (V2X) communication. However, several challenges hinder the attainment of high performance in cooperative perception, particularly positional errors arising from sensor data collection and time delays during data transmission. Existing research often addresses only one of these issues, making it unsuitable for scenarios where spatial-temporal errors coexist. In this paper, we focus on resolving the spatio-temporal drift issue caused by the interplay of spatial and temporal variations. To address this, we propose a novel end-to-end cooperative perception framework called Multi-frame Grouping Multi-agent Perception (MGMP), which effectively fuses spatio-temporal perception features from multiple agents, including vehicles and road infrastructure. Our approach extracts the effective semantic information of the temporal context of multiple agents, leverage the cross-learning of window information through multi-scale window attention, and group and aggregate multiple agents to simultaneously address the spatio-temporal drift problem caused by positional errors and time delays. We validate the effectiveness of our method on the V2XSet, OPV2V and Dair-V2X datasets. Experimental results indicate that, compared to the state-of-the-art (SOTA) work, our method achieves improvements of 2.7%, 1.7%, and 1.2% on AP@0.7, respectively.
{"title":"Cooperative Perception of Multi-Agents Under the Spatio-Temporal Drift Issue","authors":"Penglin Dai;Hao Zhou;Quanmin Wei;Xiao Wu;Zhanbo Sun;Zhaofei Yu","doi":"10.1109/TITS.2025.3626365","DOIUrl":"https://doi.org/10.1109/TITS.2025.3626365","url":null,"abstract":"Cooperative perception has significant potential to enhance perception performance compared to single-agent systems by integrating information from multiple agents through vehicle-to-everything (V2X) communication. However, several challenges hinder the attainment of high performance in cooperative perception, particularly positional errors arising from sensor data collection and time delays during data transmission. Existing research often addresses only one of these issues, making it unsuitable for scenarios where spatial-temporal errors coexist. In this paper, we focus on resolving the spatio-temporal drift issue caused by the interplay of spatial and temporal variations. To address this, we propose a novel end-to-end cooperative perception framework called Multi-frame Grouping Multi-agent Perception (MGMP), which effectively fuses spatio-temporal perception features from multiple agents, including vehicles and road infrastructure. Our approach extracts the effective semantic information of the temporal context of multiple agents, leverage the cross-learning of window information through multi-scale window attention, and group and aggregate multiple agents to simultaneously address the spatio-temporal drift problem caused by positional errors and time delays. We validate the effectiveness of our method on the V2XSet, OPV2V and Dair-V2X datasets. Experimental results indicate that, compared to the state-of-the-art (SOTA) work, our method achieves improvements of 2.7%, 1.7%, and 1.2% on AP@0.7, respectively.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1485-1498"},"PeriodicalIF":8.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877107","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 : 2025-11-04DOI: 10.1109/TITS.2025.3624568
Yuxin Ding;Chenxi Chen;Tianjia Yang;Xianbiao Hu
The Leader-Follower Cooperative Driving Automation (LF-CDA) system, crucial for applications such as truck platooning and off-road vehicle convoys, relies on automation and communication technologies to virtually link multiple vehicles and has become a core focus in the automated vehicle industry. Accurate relative positioning is critical for LF-CDA operations, yet GNSS can be unreliable in challenging environments. Asymmetric architecture is common in many LF-CDA systems, making direct application of localization models either infeasible or both computationally and communication intensive. This manuscript presents a lightweight LiDAR-based cooperative localization model that leverages the unique characteristics of asymmetric LF-CDA systems, specifically the property of “asynchronous view repetition.” In this context, the follower vehicle, operating in vehicle-following mode, consistently receives similar visual and spatial information as the leader vehicle, though with a time delay. To capitalize on such system characteristics, an asynchronous view repetition-based graph optimization model is formulated to minimize the positional errors of both leader and follower vehicles. To provide input to and solve the graph optimization model, a lightweight cooperative localization framework with multiple submodules is established, allowing the system to function independently of environmental constraints. A comprehensive set of experiments was conducted in the CARLA simulation environment, using CT-ICP and KISS-ICP as benchmarks, given their strong performance in single-vehicle settings. The results indicate that, under the LF-CDA scenario, our proposed model demonstrates greater suitability by achieving higher localization accuracy while maintaining comparable or even superior computational efficiency.
{"title":"Lightweight LiDAR-Based Cooperative Localization Model for Asymmetric Leader-Follower Cooperative Driving Automation System","authors":"Yuxin Ding;Chenxi Chen;Tianjia Yang;Xianbiao Hu","doi":"10.1109/TITS.2025.3624568","DOIUrl":"https://doi.org/10.1109/TITS.2025.3624568","url":null,"abstract":"The Leader-Follower Cooperative Driving Automation (LF-CDA) system, crucial for applications such as truck platooning and off-road vehicle convoys, relies on automation and communication technologies to virtually link multiple vehicles and has become a core focus in the automated vehicle industry. Accurate relative positioning is critical for LF-CDA operations, yet GNSS can be unreliable in challenging environments. Asymmetric architecture is common in many LF-CDA systems, making direct application of localization models either infeasible or both computationally and communication intensive. This manuscript presents a lightweight LiDAR-based cooperative localization model that leverages the unique characteristics of asymmetric LF-CDA systems, specifically the property of “asynchronous view repetition.” In this context, the follower vehicle, operating in vehicle-following mode, consistently receives similar visual and spatial information as the leader vehicle, though with a time delay. To capitalize on such system characteristics, an asynchronous view repetition-based graph optimization model is formulated to minimize the positional errors of both leader and follower vehicles. To provide input to and solve the graph optimization model, a lightweight cooperative localization framework with multiple submodules is established, allowing the system to function independently of environmental constraints. A comprehensive set of experiments was conducted in the CARLA simulation environment, using CT-ICP and KISS-ICP as benchmarks, given their strong performance in single-vehicle settings. The results indicate that, under the LF-CDA scenario, our proposed model demonstrates greater suitability by achieving higher localization accuracy while maintaining comparable or even superior computational efficiency.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 1","pages":"1650-1665"},"PeriodicalIF":8.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877110","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.2025.3624395
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}
Pub Date : 2025-10-27DOI: 10.1109/TITS.2025.3621423
Shixuan Yu;Yu Han
This paper presents a deep reinforcement learning (DRL)-based strategy for coordinated ramp metering. Existing DRL-based strategies often fail to explicitly account for the correlation between on-ramp flows and congestion at different bottlenecks. As a result, RL agents must infer these relationships through extensive interactions with the environment, which can cause the control policy to become stuck in local optima, limiting potential traffic performance improvements. To address this problem, the proposed strategy integrates an attention mechanism into the RL agent’s state function, enabling it to capture the spatial-temporal correlations between the traffic states of on-ramps and mainstream segments. This mechanism allows the agent to evaluate the relative importance of each on-ramp’s contribution to a mainstream bottleneck, resulting in more effective ramp metering actions. The proposed method is validated through microscopic traffic simulation on a real-world road network. Experimental results show that the proposed strategy outperforms state-of-the-art DRL-based approaches in improving traffic performance.
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