Pub Date : 2025-12-04DOI: 10.1109/TITS.2025.3632039
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2025.3632039","DOIUrl":"https://doi.org/10.1109/TITS.2025.3632039","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"C3-C3"},"PeriodicalIF":8.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665794","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 : 2025-11-11DOI: 10.1109/TITS.2025.3623579
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2025.3623579","DOIUrl":"https://doi.org/10.1109/TITS.2025.3623579","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 11","pages":"C3-C3"},"PeriodicalIF":8.4,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11241053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486509","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 : 2025-11-10DOI: 10.1109/TITS.2025.3616119
Changze Li;Yunxue Lu;Hao Wang
The research on signal coordination has been greatly enriched over the last decade. However, existing contributions face inherent limitations such as weak connection between objectives and common measurements of effectiveness (MOEs) caused by insufficient modeling of traffic dynamics, invariable phase splits, and great demand on hyperparameters. Meanwhile, nearly all related works are concentrated on scenarios with only under-saturated phases. Therefore, an arterial signal coordination model for minimum level of over-saturation and stops is proposed. Unlike most related works, the proposed model focuses on minimizing phase over-saturation and total stops by estimating queue profile for all phases under variable signal plans. The model is initially formulated as a mixed-integer nonlinear programming (MINLP). By applying linearization techniques, it is then transformed into a mixed-integer linear programming (MILP). Simulation experiments are carried out in SUMO, where an artery is built with eight scenarios of different traffic demand. The results indicate that the model is more competent in reducing average delay (AD), average stops (AS) and average total travel time (ATTT) than Yang’s multi-path progression model for all scenarios. It is also verified to best MP-BAND by managing obvious reduction in AS and showing advantage in decreasing AD and ATTT in most scenarios. Additionally, the proposed model is able to alleviate the level of over-saturation for an intersection by re-allocating phase splits properly, resulting in less over-saturated phases. Intuitive illustrations attest to the effectiveness of the queue estimation in the proposed model, highlighting the theoretical importance of modeling queue length as a variable.
{"title":"A Multi-Objective Model for Traffic Signal Coordination Control With Queue Profile Estimation","authors":"Changze Li;Yunxue Lu;Hao Wang","doi":"10.1109/TITS.2025.3616119","DOIUrl":"https://doi.org/10.1109/TITS.2025.3616119","url":null,"abstract":"The research on signal coordination has been greatly enriched over the last decade. However, existing contributions face inherent limitations such as weak connection between objectives and common measurements of effectiveness (MOEs) caused by insufficient modeling of traffic dynamics, invariable phase splits, and great demand on hyperparameters. Meanwhile, nearly all related works are concentrated on scenarios with only under-saturated phases. Therefore, an arterial signal coordination model for minimum level of over-saturation and stops is proposed. Unlike most related works, the proposed model focuses on minimizing phase over-saturation and total stops by estimating queue profile for all phases under variable signal plans. The model is initially formulated as a mixed-integer nonlinear programming (MINLP). By applying linearization techniques, it is then transformed into a mixed-integer linear programming (MILP). Simulation experiments are carried out in SUMO, where an artery is built with eight scenarios of different traffic demand. The results indicate that the model is more competent in reducing average delay (AD), average stops (AS) and average total travel time (ATTT) than Yang’s multi-path progression model for all scenarios. It is also verified to best MP-BAND by managing obvious reduction in AS and showing advantage in decreasing AD and ATTT in most scenarios. Additionally, the proposed model is able to alleviate the level of over-saturation for an intersection by re-allocating phase splits properly, resulting in less over-saturated phases. Intuitive illustrations attest to the effectiveness of the queue estimation in the proposed model, highlighting the theoretical importance of modeling queue length as a variable.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 12","pages":"23389-23406"},"PeriodicalIF":8.4,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665752","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.2025.3618307
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 : 2025-11-03DOI: 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}
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