IEEE Open Journal of Intelligent Transportation Systems最新文献

Security Enhancement in AAV Swarms: A Case Study Using Federated Learning and SHAP Analysis

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-03-12 DOI: 10.1109/OJITS.2025.3550792

Sushmitha Halli Sudhakara;Lida Haghnegahdar

{"title":"Security Enhancement in AAV Swarms: A Case Study Using Federated Learning and SHAP Analysis","authors":"Sushmitha Halli Sudhakara;Lida Haghnegahdar","doi":"10.1109/OJITS.2025.3550792","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3550792","url":null,"abstract":"As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging federated learning and FedAvg for weight averaging, combined with SHAP analysis to identify key contributing features. This AI-based system requires less human intervention and is more effective in detecting novel attacks than traditional intrusion detection systems (IDS). Using the IEEE DataPort AAV Attack Dataset, this study aims to develop a robust distributed ML security solution for AAV swarms, significantly advancing the cybersecurity landscape for CPSs.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"335-345"},"PeriodicalIF":4.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769405","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}

引用次数: 0

Integrating Multimodality and Partial Observability Solutions Into Decentralized Multiagent Reinforcement Learning Adaptive Traffic Signal Control

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-03-11 DOI: 10.1109/OJITS.2025.3550312

Kareem Othman;Xiaoyu Wang;Amer Shalaby;Baher Abdulhai

{"title":"Integrating Multimodality and Partial Observability Solutions Into Decentralized Multiagent Reinforcement Learning Adaptive Traffic Signal Control","authors":"Kareem Othman;Xiaoyu Wang;Amer Shalaby;Baher Abdulhai","doi":"10.1109/OJITS.2025.3550312","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3550312","url":null,"abstract":"Adaptive Traffic Signal Control (ATSC) systems leverage sensor data to dynamically adjust signal timings based on real-time traffic conditions but they often suffer from partial observability (PO) due to sensor limitations and restricted detection ranges. This study addresses PO in fully decentralized ATSC systems by introducing eMARLIN-T, a controller designed to enhance performance by incorporating historical information in the decision-making process. Additionally, ATSC systems are commonly optimized to improve the performance of the general traffic, ignoring the impact on transit. On the other hand, traditional transit signal priority (TSP) strategies, which overlay preferential strategies for transit vehicles onto general traffic fixed signal plans, often lead to negative impacts on the general traffic. Thus, this paper tackles the challenge of optimizing traffic signals to benefit both public transit and general vehicular traffic. To address this, a novel decentralized multimodal multiagent reinforcement learning (RL) signal controller, eMARLIN-T-MM, is developed. This controller integrates a transformer-based encoder for transforming the state observations into a latent space and an executor Q-network for decision-making. Tested on a simulation of five intersections in North York, Toronto, eMARLIN-T-MM significantly reduces the total person delays by 58% to 74% across various bus occupancy levels compared to pre-timed signals, outperforming the other decentralized RL-based ATSCs. In addition, eMARLIN-T-MM can automatically adapt to changes in the levels of occupancy, allowing it to optimize the intersection performance in response to varying transit and traffic demands.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"322-334"},"PeriodicalIF":4.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10922202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716453","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}

引用次数: 0

May the ODD Be With You: A Stakeholder Analysis on Operational Design Domains of Automated Driving Systems

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-03-05 DOI: 10.1109/OJITS.2025.3548161

Marcel Aguirre Mehlhorn;Hauke Dierend;Andreas Richter;Yuri A. W. Shardt

{"title":"May the ODD Be With You: A Stakeholder Analysis on Operational Design Domains of Automated Driving Systems","authors":"Marcel Aguirre Mehlhorn;Hauke Dierend;Andreas Richter;Yuri A. W. Shardt","doi":"10.1109/OJITS.2025.3548161","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3548161","url":null,"abstract":"Developing an automated driving system (ADS) is a complex task requiring collaboration between various stakeholders. To support this process, the concept of operational design domain (ODD) has emerged to define the conditions under which an ADS is designed to operate safely. Nonetheless, stakeholders require variable levels of information from an ODD to effectively integrate it into their work processes. Considering the ODD as a crucial asset in developing an ADS, a general overview of how stakeholders use and update the ODD is missing. Thus, this paper focuses on the identification and thorough investigation of eight main stakeholder categories, including self-driving system developers, regulators, and ADS customers. Each of the stakeholder categories will be further subdivided to provide a comprehensive understanding of all the ODD stakeholders involved. Furthermore, a stakeholder analysis is used to assess the ODD stakeholders’ expectations, interests, and influence through qualified interviews with experts of each domain. The analysis identifies the inputs received and the outputs supplied based on the corresponding levels of detail. These findings are summarised in a comprehensive overview to map all necessary ODD engineering requirements and deliverables for all the stakeholders that have been identified. Furthermore, this analysis explains the importance and implications of an ODD for the associated academic and business contexts.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"310-321"},"PeriodicalIF":4.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716543","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}

引用次数: 0

An Adaptive Hierarchical Framework With Contrastive Aggregation for Traffic Sign Classification

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-27 DOI: 10.1109/OJITS.2025.3544262

Rodolfo Valiente;Jiejun Xu;Alireza Esna Ashari

{"title":"An Adaptive Hierarchical Framework With Contrastive Aggregation for Traffic Sign Classification","authors":"Rodolfo Valiente;Jiejun Xu;Alireza Esna Ashari","doi":"10.1109/OJITS.2025.3544262","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3544262","url":null,"abstract":"Autonomous vehicles rely on accurate traffic sign classification, which is typically achieved through supervised learning. However, the diversity and complexity of traffic signs make it impractical to rely solely on large labeled datasets. While abundant data exists for common signs such as stop and yield signs, less common signs often lack sufficient representation in existing datasets. Few-shot learning has been proposed as an alternative solution for such cases in which there is not enough training data, but its effectiveness decreases as the number of classes increases. To address these challenges, our research introduces an innovative adaptive hierarchical framework with contrastive aggregation (HF-CA). This framework strategically reduces class dimensionality and enriches the dataset with more examples per category through contrastive aggregation. We validated our approach using modified versions of the GTSRB and Mapillary datasets, demonstrating that our method consistently outperforms existing baselines. By simplifying the classification process, our solution enhances classification accuracy and provides a scalable approach for scenarios with numerous classes but limited labels.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"230-243"},"PeriodicalIF":4.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611809","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}

引用次数: 0

Vehicle Egomotion Estimation Through IMU-RADAR Tight-Coupling

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-27 DOI: 10.1109/OJITS.2025.3546685

Stijn Harbers;Jens Kalkkuhl;Tom van der Sande

{"title":"Vehicle Egomotion Estimation Through IMU-RADAR Tight-Coupling","authors":"Stijn Harbers;Jens Kalkkuhl;Tom van der Sande","doi":"10.1109/OJITS.2025.3546685","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3546685","url":null,"abstract":"The current state-of-the-art vehicle egomotion state estimation systems are limited in their usage for advanced driver-assistance systems (ADAS), as the estimation accuracy is limited in driving scenarios where large amounts of wheel slip occur. Addressing the fundamental limitations of vehicle egomotion state estimation, this study investigates the usage of RADAR in vehicle egomotion state estimation through a tight-coupling between an Inertial Measurement Unit (IMU) and RADAR. The limitations of the state-of-the-art are caused by the usage of automotive grade sensors, which provide limited accuracy. RADAR is a type of sensor, which is already used extensively in ADAS, however, not yet in egomotion estimation. A reason for not using RADAR in this context is that it requires knowledge of the motion of the detected targets. In literature statistical methods are suggested to reject moving detections, but these are per definition not robust. This research, therefore, answers the question: How can RADAR be used in vehicle egomotion state estimation to improve performance and expand the capabilities of the system in a robust way? A new method is developed, which through IMU-RADAR tight-coupling, is able to reject moving detections. These stationary detections are then integrated into a Kalman filter to obtain the vehicle motion. This new method is compared to the state-of-the-art methods and the results are validated on a real data set of a vehicle driving in urban and highway setting. The findings demonstrate that the newly introduced method enhances the accuracy of vehicle egomotion state estimation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"244-255"},"PeriodicalIF":4.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611808","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}

引用次数: 0

TruckSentry: Context Aware Intrusion Detection and Prevention System for J1939 Networks

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-25 DOI: 10.1109/OJITS.2025.3545474

Subhojeet Mukherjee;Rik Chatterjee;Jeremy Daily

{"title":"TruckSentry: Context Aware Intrusion Detection and Prevention System for J1939 Networks","authors":"Subhojeet Mukherjee;Rik Chatterjee;Jeremy Daily","doi":"10.1109/OJITS.2025.3545474","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3545474","url":null,"abstract":"Medium and heavy-duty vehicles often have third party devices connected to their in-vehicle networks, which raises security concerns. A gateway or firewall is used as an architectural element in the vehicle network to help mitigate threats from direct access to these external connections. However, the logic used in these gateways is often unknown. As such, this paper describes and demonstrates the logic of TruckSentry, a next-generation stateful firewall for in-vehicle SAE-J1939 networks within medium and heavy-duty vehicles. TruckSentry consumes conditional rules, enforces them in a stateful manner, and can be configured to pre-empt seemingly threatening messages from being processed by the target device. By utilizing radix search trees on the bits transmitted on the Controller Area Network, the approach can apply rules during transmission. If a malicious message is detected, the system can induce bit flips in the message stream and prevent the message from reaching the application layer. Experiments with correctly configured rules show TruckSentry can identify known threats to SAE-J1939 networks while being attached to a broadcast in-vehicle network and support hundreds of rules at wire speed, even when deployed on commodity in-vehicle development hardware. TruckSentry also demonstrates the use case of next generation firewalls on serial embedded networks, especially where contextual information can be obtained and used to evaluate threatening messages.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"294-309"},"PeriodicalIF":4.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688149","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}

引用次数: 0

VI-BEV: Vehicle-Infrastructure Collaborative Perception for 3-D Object Detection on Bird’s-Eye View

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-20 DOI: 10.1109/OJITS.2025.3543831

Jingxiong Meng;Junfeng Zhao

引用次数: 0

Toward Resilient CACC Systems for Automated Vehicles

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-20 DOI: 10.1109/OJITS.2025.3544374

Joseba Gorospe;Shahriar Hasan;Arrate Alonso Gómez;Elisabeth Uhlemann

{"title":"Toward Resilient CACC Systems for Automated Vehicles","authors":"Joseba Gorospe;Shahriar Hasan;Arrate Alonso Gómez;Elisabeth Uhlemann","doi":"10.1109/OJITS.2025.3544374","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3544374","url":null,"abstract":"Cooperative Adaptive Cruise Control (CACC) utilizes Vehicle-to-Vehicle (V2V) communications and onboard sensors to facilitate cooperative maneuvering among a group of automated vehicles called a vehicle string. Such string formation of automated vehicles enables improved safety, fuel efficiency, traffic flow, and road capacity. A vehicle using CACC computes its acceleration through information obtained from its preceding vehicle and/or the Leading Vehicle (LV) of the string through V2V communications. However, wireless communication is susceptible to inevitable transient outages due to irregular packet losses, which has severe consequences on the safety and stability of a vehicle string. To address this problem, this paper proposes an enhancement to an existing CACC algorithm; the idea is that when a vehicle does not receive information from its intended sources, i.e., the LV and the predecessor, for a certain duration, it uses information from the closest available longitudinal neighbors to the intended sources to compute its desired acceleration. Furthermore, we also investigate the possibility of using such information for training Machine Learning (ML) models and making predictions on the desired accelerations of the intended sources. Rigorous simulation studies demonstrate that when information from alternative sources is utilized during transient outages, a significant improvement in terms of safety, string stability, and fuel efficiency can be observed compared to the existing CACC. Moreover, the proposed approach can handle transient outages without requiring changes in the CACC communication topology, increasing the number of transmitted messages, or degrading string performance, as proposed by many works in the literature.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"276-293"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10897819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143654900","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}

引用次数: 0

Beat the Morning Rush: Survival Analysis-Informed DNNs With Collaborative Filtering to Predict Departure Times

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-20 DOI: 10.1109/OJITS.2025.3544301

Ekin Ugurel;Gaoang Wang

引用次数: 0

Representation Learning for Place Recognition Using MIMO Radar

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-02-18 DOI: 10.1109/OJITS.2025.3543286

Prashant Kumar Rai;Nataliya Strokina;Reza Ghabcheloo

{"title":"Representation Learning for Place Recognition Using MIMO Radar","authors":"Prashant Kumar Rai;Nataliya Strokina;Reza Ghabcheloo","doi":"10.1109/OJITS.2025.3543286","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3543286","url":null,"abstract":"Traditional radar perception often rely on point clouds derived from radar heatmap using CFAR filtering, which can result in the loss of valuable information, especially weaker signals crucial for accurate perception. To address this, we present a novel approach for representation learning directly from pre-CFAR heatmaps, specifically for place recognition using a high-resolution MIMO radar sensor. By avoiding CFAR filtering, our method preserves richer contextual data, capturing finer details essential for identifying and matching distinctive features across locations. Pre-CFAR heatmaps, however, contain inherent noise and clutter, complicating their application in radar perception tasks. To overcome this, we propose a self-supervised network that learns robust latent features from noisy heatmaps. The architecture consists of two identical U-Net encoders that extract features from the pair of radar scans, which are then processed by a transformer encoder to estimate ego-motion. Ground truth ego-motion trajectories guide the network training using a weighted mean-square error loss. The latent feature representations from the trained encoders are used to create a database of feature vectors for previously visited locations. During runtime, for place recognition and loop closure detection, cosine similarity is applied to query scan feature representation and the database to find the closest matches. We also introduce data augmentation techniques to handle limited training data, enhancing the model’s generalization capability. Our approach, tested on the publicly available Coloradar dataset and our own, outperforms existing methods, showing significant improvements in place recognition accuracy, particularly in noisy and cluttered environments.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"144-154"},"PeriodicalIF":4.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891700","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535499","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}

引用次数: 0