IEEE Open Journal of Intelligent Transportation Systems最新文献_第5页

Editorial Special Section on Machine Learning and Deep Learning for Transportation 交通领域的机器学习和深度学习》编辑特辑

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-09-25 DOI: 10.1109/OJITS.2024.3458288

Abel C. H. Chen

引用次数: 0

Evaluation of Teleoperation Concepts to Solve Automated Vehicle Disengagements 评估解决自动驾驶车辆脱离问题的远程操作概念

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-09-25 DOI: 10.1109/OJITS.2024.3468021

David Brecht;Nils Gehrke;Tobias Kerbl;Niklas Krauss;Domagoj Majstorović;Florian Pfab;Maria-Magdalena Wolf;Frank Diermeyer

{"title":"Evaluation of Teleoperation Concepts to Solve Automated Vehicle Disengagements","authors":"David Brecht;Nils Gehrke;Tobias Kerbl;Niklas Krauss;Domagoj Majstorović;Florian Pfab;Maria-Magdalena Wolf;Frank Diermeyer","doi":"10.1109/OJITS.2024.3468021","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3468021","url":null,"abstract":"Teleoperation is a popular solution to remotely support highly automated vehicles through a human remote operator whenever a disengagement of the automated driving system is present. The remote operator wirelessly connects to the vehicle and solves the disengagement through support or substitution of automated driving functions and therefore enables the vehicle to resume automation. There are different approaches to support automated driving functions on various levels, commonly known as teleoperation concepts. A variety of teleoperation concepts is described in the literature, yet there has been no comprehensive and structured comparison of these concepts, and it is not clear what subset of teleoperation concepts is suitable to enable safe and efficient remote support of highly automated vehicles in a broad spectrum of disengagements. The following work establishes a basis for comparing teleoperation concepts through a literature overview on automated vehicle disengagements and on already conducted studies on the comparison of teleoperation concepts and metrics used to evaluate teleoperation performance. An evaluation of the teleoperation concepts is carried out in an expert workshop, comparing different teleoperation concepts using a selection of automated vehicle disengagement scenarios and metrics. Based on the workshop results, a set of three teleoperation concepts is derived that can be used to address a wide variety of automated vehicle disengagements in a safe and efficient way. This set includes the Remote Driving concept Shared Control as well as Collaborative Planning and Perception Modification from the Remote Assistance category.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"629-641"},"PeriodicalIF":4.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693593","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517894","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

Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations 基于机器学习的 Celeration 建模用于预测闯红灯行为

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-09-25 DOI: 10.1109/OJITS.2024.3467222

Mahmoud Masoud

{"title":"Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations","authors":"Mahmoud Masoud","doi":"10.1109/OJITS.2024.3467222","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3467222","url":null,"abstract":"This research examines the intricate correlation between speed variation (celeration), a metric of driver behavior associated with vehicle control, and occurrences of running red lights. The study is based on a thorough analysis of a large dataset that includes a variety of parameters, such as exceeding speed limits, driver age, passenger count, weather, road condition, and temporal factors. Using cutting-edge machine learning methods like AdaBoost and Bagging, predictive models for red-light violations are painstakingly built, achieving remarkable validation accuracies of 90.4% and 90.1%, respectively. The study acknowledges the dataset’s limitations in capturing real-world traffic complexities while focusing on the effectiveness and trade-offs inherent in these methodologies. This emphasizes how important it is to have synchronized and thorough data sources to guarantee accurate representation. The research field is enhancing predictive modeling techniques and improving transportation safety by connecting celebration, speed variation patterns over time, with instances of red-light violations.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"608-616"},"PeriodicalIF":4.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376625","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

Design of Unidirectional Vehicular Traffic With a Two-Dimensional Topological Structure 二维拓扑结构的单向车流设计

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-09-12 DOI: 10.1109/OJITS.2024.3458987

Hiroya Tanaka;Keita Funayama

{"title":"Design of Unidirectional Vehicular Traffic With a Two-Dimensional Topological Structure","authors":"Hiroya Tanaka;Keita Funayama","doi":"10.1109/OJITS.2024.3458987","DOIUrl":"10.1109/OJITS.2024.3458987","url":null,"abstract":"Vehicular transportation design and control are critical topics of research. In two-dimensional topological systems, edge states exist at the boundary and support transport along the system edges. In this study, we demonstrate the directional flow of vehicular traffic in a hexagonal street network with a two-dimensional topological structure. We model the network structure by the topology of vertices and edges, and vehicular transport as a symmetric random walk between the vertices. We show that the proposed structure provides a macroscopically unidirectional traffic flow to a group of vehicles. Furthermore, we note that such unidirectional properties deteriorate because of the decay of the topological edge mode. To overcome the disappearance of traffic unidirectionarity, we propose a strategy for controlling the entry–exit timing of vehicles in a street network and confirm its effectiveness through simulations. The proposed vehicle management strategy sustainably allows unidirectional traffic in street networks. Our work thus provides a critical building block for designing and controlling transportation networks.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"581-588"},"PeriodicalIF":4.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10679227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184247","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

Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic 利用地理围栏和模糊逻辑验证车联网中的位置，提高网络复原力

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-09-02 DOI: 10.1109/OJITS.2024.3453666

Maria Drolence Mwanje;Omprakash Kaiwartya;Abdallah Naser

{"title":"Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic","authors":"Maria Drolence Mwanje;Omprakash Kaiwartya;Abdallah Naser","doi":"10.1109/OJITS.2024.3453666","DOIUrl":"10.1109/OJITS.2024.3453666","url":null,"abstract":"Position verification is essential in connected and autonomous vehicle technology to enable secure vehicle-to-everything communication. Previous attempts to verify location information have used specific hardware, traffic parameters, and statistical model-based techniques dependent on neighbouring vehicles and roadside infrastructure and whose judgements can be influenced by untrustworthy entities. Considering the back-and-forth communications during verification, these techniques are also unsuitable in the dynamic vehicular networking environment. In this context, this paper proposes a self-reliant trustbased position verification technique using dynamic geofencing, neural network, and Mamdani fuzzy logic controller. The method uses vehicular dynamics, such as distance between the sender and receiver vehicles, magnitude of the speed difference, and direction, to verify the trustworthiness of vehicle positions. An experimental analysis of a dataset of simulated driving scenarios in MATLAB demonstrates that the feedforward neural network records the highest direction classification performance at 99.8% in conjunction with the centroid defuzzification method. Subsequently, further quantitative analysis, including the Receiver Operating Characteristic curve with Area Under Curve and trust level distribution histograms, indicates that the suggested classification model outperforms a random classifier and effectively identifies false position data from the actual during trust computation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"540-554"},"PeriodicalIF":4.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184248","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

Enhancing Intelligent Road Target Monitoring: A Novel BGS-YOLO Approach Based on the YOLOv8 Algorithm 加强智能道路目标监测：基于 YOLOv8 算法的新型 BGS-YOLO 方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-08-26 DOI: 10.1109/OJITS.2024.3449698

Xingyu Liu;Yuanfeng Chu;Yiheng Hu;Nan Zhao

{"title":"Enhancing Intelligent Road Target Monitoring: A Novel BGS-YOLO Approach Based on the YOLOv8 Algorithm","authors":"Xingyu Liu;Yuanfeng Chu;Yiheng Hu;Nan Zhao","doi":"10.1109/OJITS.2024.3449698","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3449698","url":null,"abstract":"Road target detection is essential for enhancing vehicle safety, increasing operational efficiency, and optimizing user experience. It also forms a crucial part of autonomous driving and intelligent monitoring systems. However, current technologies face significant limitations in multi-level feature fusion and the accurate identification of key targets in complex data environments. To address these challenges, this paper proposes an innovative algorithmic model called BiFPN GAM SimC2f-YOLO (BGS-YOLO), aimed at improving detection performance. Initially, this paper employs the Bidirectional Feature Pyramid Network (BiFPN) to effectively integrate multi-level features. This integration overcomes the limitations in feature extraction and recognition found in existing target detection algorithms. Following this, this paper introduces the Global Attention Module (GAM), which markedly improves the efficiency and accuracy of extracting key target information in complex data environments. Additionally, this paper innovatively designs the SimAM-C2f (SimC2f) network, further advancing feature expressiveness and fusion efficiency. Experiments on the public COCO dataset demonstrate that the BGS-YOLO model significantly outperforms the existing YOLOv8n model. Notably, it shows a 7.3% increase in mean average precision (mAP) and a 2.4% improvement in accuracy. These results highlight the model’s high precision and swift response in detecting road targets in complex traffic scenarios. Consequently, the BGS-YOLO model has the potential to significantly enhance road safety and contribute to a considerable reduction in traffic accident rates.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"509-519"},"PeriodicalIF":4.6,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10646366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143833","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

Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities 无人机着陆与强化学习：技术现状、挑战和机遇

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-08-15 DOI: 10.1109/OJITS.2024.3444487

José Amendola;Linga Reddy Cenkeramaddi;Ajit Jha

{"title":"Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities","authors":"José Amendola;Linga Reddy Cenkeramaddi;Ajit Jha","doi":"10.1109/OJITS.2024.3444487","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3444487","url":null,"abstract":"Unmanned aerial vehicles, and special multirotor drones, have shown great relevance in a plethora of missions that require high affordance, field of view, and precision. Their limited payload capacity and autonomy make its landing a crucial task. Despite many attempts in the literature to address drone landing, challenges and open gaps still exist. Reinforcement Learning has gained notoriety in a variety of control problems, with recent proposals for drone landing applications. This work aims to present a systematic literature review on works employing Deep Reinforcement Learning for multirotor drone landing in both static and dynamic platforms. It also revisits Reinforcement Learning Algorithms, the main frameworks and simulators adopted for specific landing operations. The comprehensive analysis performed on reviewed works revealed that there are important untackled challenges when it comes to wind disturbances, unpredictability of moving landing targets, sensor latency, and sim-to-real gap. Finally, we present our critical analysis of how recent state-of-the-art deep learning concepts can be combined with reinforcement learning to leverage the latter in addressing the open gaps in future works.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"520-539"},"PeriodicalIF":4.6,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174012","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

Multi-Objective Optimization of Urban Air Transportation Networks Under Social Considerations 社会因素下城市航空运输网络的多目标优化

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-08-13 DOI: 10.1109/OJITS.2024.3443170

Nikolas Hohmann;Sebastian Brulin;Jürgen Adamy;Markus Olhofer

{"title":"Multi-Objective Optimization of Urban Air Transportation Networks Under Social Considerations","authors":"Nikolas Hohmann;Sebastian Brulin;Jürgen Adamy;Markus Olhofer","doi":"10.1109/OJITS.2024.3443170","DOIUrl":"10.1109/OJITS.2024.3443170","url":null,"abstract":"This work proposes and investigates a solution approach to the urban air transportation network optimization problem, considering the perspectives of different stakeholders, including societal interests. Given logistic hub positions and a set of optimized paths connecting them pairwise, we aim for a Pareto-optimal and three-dimensional air corridor network structure. This work demonstrates a way to merge the given paths into a network and provides a framework to optimize the network further regarding multiple objectives. It proposes three objective functions that evaluate the network from the economic perspectives of network providers and users and the city residents’ social point of view. Using geospatial data from Frankfurt, Germany, we conducted different experiments including and excluding the social objective function under a varying input set of pre-optimized paths. Our analysis showed that taking social aspects into account results in traffic networks whose increase in social acceptance far outweighs the extra monetary costs. We conclude that it is beneficial to integrate social criteria into optimization problems when the solutions obtained are the basis for decisions in the area of conflict between the economy and human welfare.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"589-602"},"PeriodicalIF":4.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184249","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

A Reference Architecture for Data-Driven Intelligent Public Transportation Systems 数据驱动型智能公共交通系统参考架构

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-08-09 DOI: 10.1109/OJITS.2024.3441048

Franca Rocco Di Torrepadula;Sergio Di Martino;Nicola Mazzocca;Paolo Sannino

{"title":"A Reference Architecture for Data-Driven Intelligent Public Transportation Systems","authors":"Franca Rocco Di Torrepadula;Sergio Di Martino;Nicola Mazzocca;Paolo Sannino","doi":"10.1109/OJITS.2024.3441048","DOIUrl":"10.1109/OJITS.2024.3441048","url":null,"abstract":"Smart cities include complex ICT ecosystems, whose definition requires the cooperation of several software systems. Among them, Intelligent Public Transportation Systems (IPTS) aim to effectively exploit public transit resources. Still, adopting an IPTS is non-trivial. Off-the-shelf IPTS are often tied to specific technologies and, thus, not easy to integrate within existing software ecosystems. Moreover, despite IPTS introduce several peculiar issues, there is a lack of domain-specific reference architectures, which would significantly ease the work of practitioners. To fill this gap, starting from the experience gained with the Hitachi Rail company in deploying a large-scale IPTS, we identify a set of requirements for IPTS, and propose a domain-specific reference architecture, compliant with these requirements, whose primary objective is facilitating and standardizing the design of IPTS, by providing guidelines to IPTS designers. Consequently, it eases also the interoperability among different IPTSs. As an example of an IPTS obtainable from the architecture, we present a solution currently deployed by Hitachi in a major Italian city. Still, being independent from the specific considered urban scenario, the architecture can be easily instantiated in different cities with similar needs. Finally, we discuss some research challenges which should be further investigated in this domain.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"469-482"},"PeriodicalIF":4.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10632072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944413","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

A Secure Object Detection Technique for Intelligent Transportation Systems 智能交通系统的安全物体检测技术

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-08-08 DOI: 10.1109/OJITS.2024.3440876

Jueal Mia;M. Hadi Amini

{"title":"A Secure Object Detection Technique for Intelligent Transportation Systems","authors":"Jueal Mia;M. Hadi Amini","doi":"10.1109/OJITS.2024.3440876","DOIUrl":"10.1109/OJITS.2024.3440876","url":null,"abstract":"Federated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. However, due to the distributed nature of federated learning, there is a larger attack surface, making cyber-attack detection and defense challenging. Although prior works developed various defense strategies to address security issues in federated learning settings, most approaches fail to mitigate cyber-attacks due to the diverse characteristics of the attack, edge devices, and data distribution. To address this issue, this paper develops a hybrid privacy-preserving algorithm to safeguard federated learning methods against malicious attacks in Intelligent Transportation Systems, considering object detection as a downstream machine learning task. This algorithm involves the edge devices (e.g., autonomous vehicles) and road side units to collaboratively train their model while maintaining the privacy of their respective data. Furthermore, this hybrid algorithm provides robust security against data poisoning-based model replacement and inference attacks throughout the training phase. We evaluated our model using the CIFAR10 and LISA traffic light dataset, demonstrating its ability to mitigate malicious attacks with minimal impact on the performance of main tasks.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"495-508"},"PeriodicalIF":4.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10630660","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944414","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