Pub Date : 2026-01-30DOI: 10.1109/OJITS.2026.3655715
{"title":"2025 Index IEEE Open Journal of Intelligent Transportation Systems","authors":"","doi":"10.1109/OJITS.2026.3655715","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3655715","url":null,"abstract":"","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1685-1709"},"PeriodicalIF":5.3,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11369235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amidst global efforts to mitigate climate change, managing the disruptive impact of highway work zones requires intelligent solutions. Traditional assessment methods often lack the capability to model the complex, dynamic interactions between traffic flow and temporary control policies, while struggling to balance computational efficiency with system-level dynamic assessment in large-scale networks. To address this gap, this paper designs, implements, and validates a Multiagent System based decision support framework. In this framework, individual vehicles and infrastructure elements are modeled as autonomous agents that interact to simulate emergent, system-level traffic dynamics. By modeling the system from the bottom up, this mesoscopic simulation approach enables a system-level quantification of the relative effectiveness of various control policies in reducing CO2 emissions across different work zone scenarios. Using the Hangzhou Ring Expressway as a case study, the study demonstrates the utility of the framework as a virtual testbed. The results of the framework not only reveal the non-linear sensitivity of emissions to policy parameters but, more critically, the multi-objective tradeoff analysis uncovers non-intuitive, Pareto-optimal strategies. For instance, the analysis identifies scenarios where a well-configured two-lane closure can outperform a suboptimal one-lane closure in both traffic efficiency and environmental impact. The findings confirm that the proposed framework is a powerful and extensible tool for transportation authorities to design, test, and deploy more efficient and sustainable work zone management strategies in the era of Intelligent Transportation Systems.
{"title":"An Agent-Based Framework for Performance Evaluation and Tradeoff Analysis of Highway Work Zone Policies","authors":"Qiugang Tao;Jinrui Gong;Xujie Zhang;Zhenyu Mei;Xiaoyong Xu;Zhihua Zhang","doi":"10.1109/OJITS.2026.3655473","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3655473","url":null,"abstract":"Amidst global efforts to mitigate climate change, managing the disruptive impact of highway work zones requires intelligent solutions. Traditional assessment methods often lack the capability to model the complex, dynamic interactions between traffic flow and temporary control policies, while struggling to balance computational efficiency with system-level dynamic assessment in large-scale networks. To address this gap, this paper designs, implements, and validates a Multiagent System based decision support framework. In this framework, individual vehicles and infrastructure elements are modeled as autonomous agents that interact to simulate emergent, system-level traffic dynamics. By modeling the system from the bottom up, this mesoscopic simulation approach enables a system-level quantification of the relative effectiveness of various control policies in reducing CO2 emissions across different work zone scenarios. Using the Hangzhou Ring Expressway as a case study, the study demonstrates the utility of the framework as a virtual testbed. The results of the framework not only reveal the non-linear sensitivity of emissions to policy parameters but, more critically, the multi-objective tradeoff analysis uncovers non-intuitive, Pareto-optimal strategies. For instance, the analysis identifies scenarios where a well-configured two-lane closure can outperform a suboptimal one-lane closure in both traffic efficiency and environmental impact. The findings confirm that the proposed framework is a powerful and extensible tool for transportation authorities to design, test, and deploy more efficient and sustainable work zone management strategies in the era of Intelligent Transportation Systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"379-395"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motion planning in uncertain environments like complex urban areas is a key challenge for autonomous vehicles (AVs). The aim of our research is to investigate how AVs can navigate crowded, unpredictable scenarios with multiple pedestrians while maintaining a safe and efficient vehicle behavior. So far, most research has concentrated on static or deterministic traffic participant behavior. This paper introduces a novel algorithm for motion planning in crowded spaces by combining social force principles for simulating realistic pedestrian behavior with a risk-aware motion planner. We evaluate this new algorithm in a 2D simulation environment to rigorously assess AV-pedestrian interactions, demonstrating that our algorithm enables safe, efficient, and adaptive motion planning, particularly in highly crowded urban environments, a first in achieving this level of performance. This study has not taken into consideration real-time constraints and has been shown only in simulation so far. Further studies are needed to investigate the novel algorithm in a complete software stack for AVs on real cars to investigate the entire perception, planning and control pipeline in crowded scenarios. We release the code developed in this research as an open-source resource for further studies and development. It can be accessed at the following link: https://github.com/TUM-AVS/PedestrianAwareMotionPlanning
{"title":"Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios","authors":"Korbinian Moller;Truls Nyberg;Jana Tumova;Johannes Betz","doi":"10.1109/OJITS.2026.3655468","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3655468","url":null,"abstract":"Motion planning in uncertain environments like complex urban areas is a key challenge for autonomous vehicles (AVs). The aim of our research is to investigate how AVs can navigate crowded, unpredictable scenarios with multiple pedestrians while maintaining a safe and efficient vehicle behavior. So far, most research has concentrated on static or deterministic traffic participant behavior. This paper introduces a novel algorithm for motion planning in crowded spaces by combining social force principles for simulating realistic pedestrian behavior with a risk-aware motion planner. We evaluate this new algorithm in a 2D simulation environment to rigorously assess AV-pedestrian interactions, demonstrating that our algorithm enables safe, efficient, and adaptive motion planning, particularly in highly crowded urban environments, a first in achieving this level of performance. This study has not taken into consideration real-time constraints and has been shown only in simulation so far. Further studies are needed to investigate the novel algorithm in a complete software stack for AVs on real cars to investigate the entire perception, planning and control pipeline in crowded scenarios. We release the code developed in this research as an open-source resource for further studies and development. It can be accessed at the following link: <uri>https://github.com/TUM-AVS/PedestrianAwareMotionPlanning</uri>","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"365-378"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1109/OJITS.2026.3655581
Muhammad Shahbaz;Shaurya Agarwal
This article presents UrbanTwin datasets–high-fidelity, realistic replicas of three public roadside lidar datasets: LUMPI, V2X-Real-IC, and TUMTraf-I. Each UrbanTwin dataset contains $10K$ annotated frames corresponding to one of the public datasets. Annotations include 3D bounding boxes, instance segmentation labels, and tracking IDs for six object classes, along with semantic segmentation labels for nine classes. These datasets are synthesized using emulated lidar sensors within realistic digital twins, modeled based on surrounding geometry, road alignment at lane level, and the lane topology and vehicle movement patterns at intersections of the actual locations corresponding to each real dataset. Due to the precise digital twin modeling, the synthetic datasets are well aligned with their real counterparts, offering strong standalone and augmentative value for training deep learning models on tasks such as 3D object detection, tracking, and semantic and instance segmentation. We evaluate the alignment of the synthetic replicas through statistical and structural similarity analysis with real data, and further demonstrate their utility by training 3D object detection models solely on synthetic data and testing them on real, unseen data. The high similarity scores and improved detection performance, compared to the models trained on real data, indicate that the UrbanTwin datasets effectively enhance existing benchmark datasets by increasing sample size and scene diversity. In addition, the digital twins can be adapted to test custom scenarios by modifying the design and dynamics of the simulations. To our knowledge, these are the first digitally synthesized datasets that can replace in-domain real-world datasets for lidar perception tasks. UrbanTwin datasets are publicly available at https://dataverse.harvard.edu/dataverse/ucf-ut.
{"title":"UrbanTwin: Synthetic Roadside LiDAR Datasets","authors":"Muhammad Shahbaz;Shaurya Agarwal","doi":"10.1109/OJITS.2026.3655581","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3655581","url":null,"abstract":"This article presents <monospace>UrbanTwin</monospace> datasets–high-fidelity, realistic replicas of three public roadside lidar datasets: <monospace>LUMPI</monospace>, <monospace>V2X-Real-IC</monospace>, and <monospace>TUMTraf-I</monospace>. Each <monospace>UrbanTwin</monospace> dataset contains <inline-formula> <tex-math>$10K$ </tex-math></inline-formula> annotated frames corresponding to one of the public datasets. Annotations include 3D bounding boxes, instance segmentation labels, and tracking IDs for six object classes, along with semantic segmentation labels for nine classes. These datasets are synthesized using emulated lidar sensors within realistic digital twins, modeled based on surrounding geometry, road alignment at lane level, and the lane topology and vehicle movement patterns at intersections of the actual locations corresponding to each real dataset. Due to the precise digital twin modeling, the synthetic datasets are well aligned with their real counterparts, offering strong standalone and augmentative value for training deep learning models on tasks such as 3D object detection, tracking, and semantic and instance segmentation. We evaluate the alignment of the synthetic replicas through statistical and structural similarity analysis with real data, and further demonstrate their utility by training 3D object detection models solely on synthetic data and testing them on real, unseen data. The high similarity scores and improved detection performance, compared to the models trained on real data, indicate that the <monospace>UrbanTwin</monospace> datasets effectively enhance existing benchmark datasets by increasing sample size and scene diversity. In addition, the digital twins can be adapted to test custom scenarios by modifying the design and dynamics of the simulations. To our knowledge, these are the first digitally synthesized datasets that can replace in-domain real-world datasets for lidar perception tasks. <monospace>UrbanTwin</monospace> datasets are publicly available at <uri>https://dataverse.harvard.edu/dataverse/ucf-ut</uri>.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"353-364"},"PeriodicalIF":5.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-15DOI: 10.1109/OJITS.2026.3654451
Yihong Tang;Wei Ma
Accurate trajectory prediction of road agents (e.g., pedestrians, vehicles) is an essential prerequisite for various intelligent systems applications, such as autonomous driving and robotic navigation. Recent research highlights the importance of environmental contexts (e.g., maps) and the “multi-modality” of trajectories, leading to increasingly complex model structures. However, real-world deployments require lightweight models that can quickly migrate and adapt to new environments. Additionally, the core motivations of road agents, referred to as their intentions, deserves further exploration. In this study, we advocate that understanding and reasoning road agents’ intention plays a key role in trajectory prediction tasks, and the main challenge is that the concept of intention is fuzzy and abstract. To this end, we present Intent, an efficient intention-guided trajectory prediction model that relies solely on information contained in the road agent’s trajectory. Our model distinguishes itself from existing models in several key aspects: (i) We explicitly model road agents’ intentions through contrastive clustering, accommodating the fuzziness and abstraction of human intention in their trajectories. (ii) The proposed Intent is based solely on multi-layer perceptrons (Mlps), resulting in reduced training and inference time, making it very efficient and more suitable for real-world deployment. (iii) By leveraging estimated intentions and an innovative algorithm for transforming trajectory observations, we obtain more robust trajectory representations that lead to superior prediction accuracy. Extensive experiments on real-world trajectory datasets for pedestrians and autonomous vehicles demonstrate the effectiveness and efficiency of Intent.
{"title":"INTENT: Trajectory Prediction Framework With Intention-Guided Contrastive Clustering","authors":"Yihong Tang;Wei Ma","doi":"10.1109/OJITS.2026.3654451","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3654451","url":null,"abstract":"Accurate trajectory prediction of road agents (e.g., pedestrians, vehicles) is an essential prerequisite for various intelligent systems applications, such as autonomous driving and robotic navigation. Recent research highlights the importance of environmental contexts (e.g., maps) and the “multi-modality” of trajectories, leading to increasingly complex model structures. However, real-world deployments require lightweight models that can quickly migrate and adapt to new environments. Additionally, the core motivations of road agents, referred to as their intentions, deserves further exploration. In this study, we advocate that understanding and reasoning road agents’ intention plays a key role in trajectory prediction tasks, and the main challenge is that the concept of intention is fuzzy and abstract. To this end, we present <sc>Intent</small>, an efficient intention-guided trajectory prediction model that relies solely on information contained in the road agent’s trajectory. Our model distinguishes itself from existing models in several key aspects: (i) We explicitly model road agents’ intentions through contrastive clustering, accommodating the fuzziness and abstraction of human intention in their trajectories. (ii) The proposed <sc>Intent</small> is based solely on multi-layer perceptrons (<sc>Mlp</small>s), resulting in reduced training and inference time, making it very efficient and more suitable for real-world deployment. (iii) By leveraging estimated intentions and an innovative algorithm for transforming trajectory observations, we obtain more robust trajectory representations that lead to superior prediction accuracy. Extensive experiments on real-world trajectory datasets for pedestrians and autonomous vehicles demonstrate the effectiveness and efficiency of <sc>Intent</small>.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"337-352"},"PeriodicalIF":5.3,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11352503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1109/OJITS.2026.3654547
Xingshuai Huang;Di Wu;Benoit Boulet
Efficient traffic signal control is of critical importance for minimizing traffic congestion and enhancing transportation efficiency. Researchers have turned to Reinforcement Learning (RL) for traffic signal control (TSC) due to the dynamic nature of traffic flow. Despite its potential, the real-world application of RL-based controllers is constrained by low sample efficiency and high computational demands. To address these challenges, we propose DTLight, a simple yet powerful lightweight Decision Transformer (DT)-based offline-to-online TSC method that can learn policy from pre-collected offline datasets while maintaining the capability to refine policy with minimal online interactions. Specifically, we propose three novel adaptive knowledge distillation methods to learn a lightweight offline controller from a well-trained larger teacher model to reduce implementation computation. Additionally, we integrate adapter modules to mitigate the expenses associated with fine-tuning, which makes DTLight practical for online enhancement with minimal computation and only a few fine-tuning steps during real deployment. Extensive experiments have been implemented on different traffic scenarios. The results show that DTLight pre-trained purely on offline datasets can outperform state-of-the-art methods in most scenarios. Additionally, online fine-tuning further improves the performance by up to 40.7% over the best online RL baseline methods. Moreover, we introduce $D$ atasets specifically designed for $T$ SC with offline RL (referred to as DTRL). Our datasets and code are publicly available: https://github.com/xingshuaihuang/dtlight.
{"title":"Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach","authors":"Xingshuai Huang;Di Wu;Benoit Boulet","doi":"10.1109/OJITS.2026.3654547","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3654547","url":null,"abstract":"Efficient traffic signal control is of critical importance for minimizing traffic congestion and enhancing transportation efficiency. Researchers have turned to Reinforcement Learning (RL) for traffic signal control (TSC) due to the dynamic nature of traffic flow. Despite its potential, the real-world application of RL-based controllers is constrained by low sample efficiency and high computational demands. To address these challenges, we propose DTLight, a simple yet powerful lightweight Decision Transformer (DT)-based offline-to-online TSC method that can learn policy from pre-collected offline datasets while maintaining the capability to refine policy with minimal online interactions. Specifically, we propose three novel adaptive knowledge distillation methods to learn a lightweight offline controller from a well-trained larger teacher model to reduce implementation computation. Additionally, we integrate adapter modules to mitigate the expenses associated with fine-tuning, which makes DTLight practical for online enhancement with minimal computation and only a few fine-tuning steps during real deployment. Extensive experiments have been implemented on different traffic scenarios. The results show that DTLight pre-trained purely on offline datasets can outperform state-of-the-art methods in most scenarios. Additionally, online fine-tuning further improves the performance by up to 40.7% over the best online RL baseline methods. Moreover, we introduce <inline-formula> <tex-math>$D$ </tex-math></inline-formula>atasets specifically designed for <inline-formula> <tex-math>$T$ </tex-math></inline-formula>SC with offline RL (referred to as DTRL). Our datasets and code are publicly available: <uri>https://github.com/xingshuaihuang/dtlight</uri>.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"396-411"},"PeriodicalIF":5.3,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11352509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-12DOI: 10.1109/OJITS.2026.3652748
R. Frolow;L. Zhang;V. Schwieger
This contribution is embedded into the challenge of track fault localization with low-cost hardware. For precise localization on the track, with an accuracy of a few decimeters for separating overlapping errors, a high resolution trajectory is needed and therefore sensor fusion is used. The commonly used combination of sensors consists of Global Navigation Satellite Systems and Inertial Measurement Units. The steps of the Kalman filter for sensor fusion are covered and afterwards the Unscented transform is described. This transform is applied to the prediction step of the Kalman filter. The implemented filters are extended by an adaptive stochastic model that applies to the observations used in the update steps. The Error-state Kalman filter and the Unscented Kalman filter are compared with and without the adaptive stochastic model with respect to their resulting root-mean-square (RMS) values. It is observed that the applied adaptive stochastic model improves performance for both filters by a small margin of 2 to 3 cm down to an RMS of 0.26 m. Meanwhile the roll angle estimation achieves deviations down to 0.1°. Both implemented filters achieve comparable results.
{"title":"Precise Train Positioning With Unscented Kalman Filter and Low-Cost Sensors","authors":"R. Frolow;L. Zhang;V. Schwieger","doi":"10.1109/OJITS.2026.3652748","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3652748","url":null,"abstract":"This contribution is embedded into the challenge of track fault localization with low-cost hardware. For precise localization on the track, with an accuracy of a few decimeters for separating overlapping errors, a high resolution trajectory is needed and therefore sensor fusion is used. The commonly used combination of sensors consists of Global Navigation Satellite Systems and Inertial Measurement Units. The steps of the Kalman filter for sensor fusion are covered and afterwards the Unscented transform is described. This transform is applied to the prediction step of the Kalman filter. The implemented filters are extended by an adaptive stochastic model that applies to the observations used in the update steps. The Error-state Kalman filter and the Unscented Kalman filter are compared with and without the adaptive stochastic model with respect to their resulting root-mean-square (RMS) values. It is observed that the applied adaptive stochastic model improves performance for both filters by a small margin of 2 to 3 cm down to an RMS of 0.26 m. Meanwhile the roll angle estimation achieves deviations down to 0.1°. Both implemented filters achieve comparable results.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"304-312"},"PeriodicalIF":5.3,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11345483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-06DOI: 10.1109/OJITS.2026.3651438
Hafiz Muhammad Waseem;Noor Munir;Seong Oun Hwang
Intelligent transportation initiatives increasingly employ extensive networks of Internet-of-Things (IoT) sensors in combination with fog-computing platforms that locate computational resources near data sources in both maritime and urban environments. Although such connectivity enhances traffic monitoring and control, it simultaneously broadens the attack surface, placing sensitive operational data at heightened risk. Identity-Based Encryption (IBE) simplifies cryptographic key management in these contexts; however, it remains constrained by key-escrow exposure and the practical complexity of securely distributing private keys. This study analyzes these limitations and evaluates the extent to which two quantum techniques, Blind Quantum Computation (BQC) and Quantum Annealing (QA), can provide effective solutions. In particular, BQC enables encrypted computation without disclosing the user’s identity to the processing server, thereby substantially mitigating the key-escrow vulnerability inherent in conventional IBE deployments. Meanwhile, QA is recommended for its ability to dynamically optimize network performance and security configurations. By synthesizing recent developments, discussing challenges, and recommending quantum-enhanced solutions, this study marks a significant step towards securing and optimizing smart transportation systems through advanced cryptographic techniques and quantum computing.
{"title":"Advancing IoT-Driven Transportation Security: A Comprehensive Review of Privacy-Preserving Identity-Based Encryption With Quantum Enhancements","authors":"Hafiz Muhammad Waseem;Noor Munir;Seong Oun Hwang","doi":"10.1109/OJITS.2026.3651438","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3651438","url":null,"abstract":"Intelligent transportation initiatives increasingly employ extensive networks of Internet-of-Things (IoT) sensors in combination with fog-computing platforms that locate computational resources near data sources in both maritime and urban environments. Although such connectivity enhances traffic monitoring and control, it simultaneously broadens the attack surface, placing sensitive operational data at heightened risk. Identity-Based Encryption (IBE) simplifies cryptographic key management in these contexts; however, it remains constrained by key-escrow exposure and the practical complexity of securely distributing private keys. This study analyzes these limitations and evaluates the extent to which two quantum techniques, Blind Quantum Computation (BQC) and Quantum Annealing (QA), can provide effective solutions. In particular, BQC enables encrypted computation without disclosing the user’s identity to the processing server, thereby substantially mitigating the key-escrow vulnerability inherent in conventional IBE deployments. Meanwhile, QA is recommended for its ability to dynamically optimize network performance and security configurations. By synthesizing recent developments, discussing challenges, and recommending quantum-enhanced solutions, this study marks a significant step towards securing and optimizing smart transportation systems through advanced cryptographic techniques and quantum computing.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"268-284"},"PeriodicalIF":5.3,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11329403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1109/OJITS.2026.3650976
Francesco Vitale;Ramin Niroumand;Claudio Roncoli
We propose a novel control strategy for signal-free intersection management through trajectory optimization for connected and automated vehicles. Such methodology is designed to be employed in a distributed manner, hence with no need for central units or specific tasks for leading vehicles, while only a limited amount of information needs to be exchanged and processed. The approach relies on an iterative distributed allocation and subsequent optimization of the time slots to cross the intersection. The distributed allocation aims to identify the constraints for the optimization problem to be solved, which enables the formulation of uncoupled subproblems that can be solved by each vehicle independently. The iterative algorithm initially allows the allocated time slots to overlap via a violation function that gradually decreases to zero as the algorithm progresses. This provides the optimization problem with enough flexibility to allow vehicles to resize their time slots and make them more suitable to meet their own requirements of trajectory smoothness and error minimization. We include simulation results and sensitivity analyses to demonstrate the effectiveness of our approach.
{"title":"Distributed Signal-Free Intersection Optimization via Iterative Time Slots Adjustment for Connected and Automated Vehicles","authors":"Francesco Vitale;Ramin Niroumand;Claudio Roncoli","doi":"10.1109/OJITS.2026.3650976","DOIUrl":"https://doi.org/10.1109/OJITS.2026.3650976","url":null,"abstract":"We propose a novel control strategy for signal-free intersection management through trajectory optimization for connected and automated vehicles. Such methodology is designed to be employed in a distributed manner, hence with no need for central units or specific tasks for leading vehicles, while only a limited amount of information needs to be exchanged and processed. The approach relies on an iterative distributed allocation and subsequent optimization of the time slots to cross the intersection. The distributed allocation aims to identify the constraints for the optimization problem to be solved, which enables the formulation of uncoupled subproblems that can be solved by each vehicle independently. The iterative algorithm initially allows the allocated time slots to overlap via a violation function that gradually decreases to zero as the algorithm progresses. This provides the optimization problem with enough flexibility to allow vehicles to resize their time slots and make them more suitable to meet their own requirements of trajectory smoothness and error minimization. We include simulation results and sensitivity analyses to demonstrate the effectiveness of our approach.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"7 ","pages":"285-303"},"PeriodicalIF":5.3,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11329051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1109/OJITS.2025.3650561
Muhammad Fawzan Anwari Muhammad Saiful Anuar;Fadhlan Hafizhelmi Kamaru Zaman;Syahrul Afzal Bin Che Abdullah;Kok Mung Ng;Kanendra Naidu Vijyakumar;Shyh Kang Ng
Distracted driving is a leading cause of road accidents, with visual and manual distractions being particularly prevalent. Traditional computer vision methods, particularly Convolutional Neural Networks (CNNs), have been extensively utilized for detecting driver behavior; however, they face challenges in effectively modeling long-range dependencies and complex spatiotemporal patterns. Recent advancements in Vision Transformer (ViT) demonstrate significant potential to address these limitations by leveraging global attention mechanisms and a scalable architecture. This review presents a comprehensive review of ViT-based approaches in distracted driving detection, which covers both image-based and video-based methods. It examines several architectural innovations, such as lightweight ViT variants, pose-aware attention-enhanced models, and hybrid ViT-architecture designs. The review also explores multi-modal and multi-view fusion strategies, which integrate several inputs such as RGB, infrared, depth, and physiological signals to enhance model robustness across diverse driving scenarios. In addition, the paper highlights benchmark datasets and performance comparisons used in distracted driving behavior detection. Finally, this review highlights the current challenges, including computational cost and interpretability, while also proposing directions for future research. Overall, ViT-based models present a promising foundation for developing the next generation of intelligent driver monitoring systems.
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