IEEE Transactions on Intelligent Transportation Systems最新文献_第7页

FREGNet: Ship Recognition Based on Feature Representation Enhancement and GCN Combiner in Complex Environment FREGNet：复杂环境中基于特征表示增强和 GCN 组合器的船舶识别技术

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-16 DOI: 10.1109/TITS.2024.3454016

Yang Tian;Hao Meng;Fei Yuan

{"title":"FREGNet: Ship Recognition Based on Feature Representation Enhancement and GCN Combiner in Complex Environment","authors":"Yang Tian;Hao Meng;Fei Yuan","doi":"10.1109/TITS.2024.3454016","DOIUrl":"https://doi.org/10.1109/TITS.2024.3454016","url":null,"abstract":"Harsh sea conditions, uneven illumination, and the variable spatial positions of ships result in ship images captured by imaging systems that contain not only the ship targets but also complex environmental information. This environmental complexity, intertwined with the ship targets, significantly undermines the accuracy of ship target recognition. However, existing methods for recognizing ships are mainly designed to identify large targets in clear weather with uniform illumination. They rarely account for the aforementioned complex environmental information, leading to the loss of critical detailed features in the ship’s target area, particularly in adverse weather and uneven illumination scenarios. To address these challenges, we propose a novel method for recognizing ship targets in complex environments: the Feature Representation Enhancement and Graph Convolutional Combiner Network (FREGNet). We introduce a Feature Representation Enhancement (FRE) module to the backbone of the feature extraction network to enhance the capture of detailed features in the ship’s target area, particularly under illumination noise and cloud-covered conditions. Furthermore, we design a GCN Combiner module based on a Graph Convolutional Network to dynamically combine global feature key points and local detailed feature key points output by the FRE, thereby increasing the quantity of detailed feature information in the ship’s target area. This approach facilitates the accurate classification of ship categories. Experiments were conducted using the CIB-ships, MAR-ships, and Game-of-ships datasets. Compared to suboptimal transformer-based ship target recognition methods, FREGNet improves ship target recognition accuracy by 2.8%, 0.99%, and 1.38%, respectively. The FREGNet method achieves high recognition accuracy at a faster rate.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15641-15653"},"PeriodicalIF":7.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587588","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}

引用次数: 0

Provably Secure and Lightweight Authentication and Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks 基于雾的车载 Ad-Hoc 网络的可证明安全的轻量级认证和密钥协议

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-10 DOI: 10.1109/tits.2024.3452928

Syed Muhammad Awais, Wu Yucheng, Khalid Mahmood, Mohammed J. F. Alenazi, Ali Kashif Bashir, Ashok Kumar Das, Pascal Lorenz

引用次数: 0

Interval Type-2 Fuzzy Path Tracking Control for Autonomous Ground Vehicles Under Switched Triggered and Sensor Attacks 开关触发和传感器攻击下自主地面飞行器的间隔型-2 模糊路径跟踪控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-10 DOI: 10.1109/TITS.2024.3450182

Wenfeng Li;Zhengchao Xie;Pak Kin Wong;Xiang Zhang;Jian Zhao;Jing Zhao

引用次数: 0

Distributed Multi-Agent Reinforcement Learning for Cooperative Low-Carbon Control of Traffic Network Flow Using Cloud-Based Parallel Optimization 利用基于云的并行优化实现交通网络流量的分布式多代理强化学习低碳协同控制

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-10 DOI: 10.1109/tits.2024.3452430

Yongnan Zhang, Yonghua Zhou, Hamido Fujita

引用次数: 0

A Novel Voronoi-Based Spatio-Temporal Graph Convolutional Network for Traffic Crash Prediction Considering Geographical Spatial Distributions 基于 Voronoi- 的时空图卷积网络，用于考虑地理空间分布的交通事故预测

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-10 DOI: 10.1109/tits.2024.3452275

Jing Gan, Qiao Yang, Dapeng Zhang, Linheng Li, Xu Qu, Bin Ran

引用次数: 0

Traffic Load Simulation for Long-Span Bridges Using a Transformer Model Incorporating In-Lane Transverse Vehicle Movements 使用包含车道内横向车辆移动的变压器模型模拟大跨度桥梁的交通荷载

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-10 DOI: 10.1109/TITS.2024.3452106

Yiqing Dong;Yue Pan;Dalei Wang;Airong Chen

{"title":"Traffic Load Simulation for Long-Span Bridges Using a Transformer Model Incorporating In-Lane Transverse Vehicle Movements","authors":"Yiqing Dong;Yue Pan;Dalei Wang;Airong Chen","doi":"10.1109/TITS.2024.3452106","DOIUrl":"10.1109/TITS.2024.3452106","url":null,"abstract":"Traffic load simulation (TLS) is critical for the design and assessment of long-span bridges. Traditional methods, such as Monte-Carlo sampling and Cellular Automaton, rely on actual traffic data for load generation and evolution. However, they often overlook in-lane transverse movements, which are vital for precise bridge component assessment. This paper presents a TLS framework that incorporates in-lane transverse movements for long-span bridges. We select eight parameters as input features for a Transformer-based deep learning model, designed to predict both longitudinal and transverse vehicle speeds. The TLS process begins with spatial-temporal traffic load monitoring on the target bridge. Monte-Carlo sampling generates vehicle data, and the trained Transformer model simulates traffic evolution. A case study on a 1490-meter main-span suspension bridge illustrates the proposed method. Traffic trajectories were captured using a multi-vision system and reconstructed to minimize errors. The Transformer model was trained with optimized hyperparameters, enabling the completion of TLS on the entire bridge deck. We also compare the performance of other deep learning models, evaluate the accuracy of transverse distribution in TLS, and discuss its potential applications in future bridge assessments. The proposed TLS method enhances current practices by accurately simulating transverse vehicle positions on bridge decks, thereby improving the fidelity of microscopic traffic simulations and enabling more precise fatigue damage assessments of bridge components.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15600-15613"},"PeriodicalIF":7.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227382","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}

引用次数: 0

Optimizing Time-Dependent Network Flows With Environmental Objectives: A Path Controlling Approach Enabled by Automated Vehicles 根据环境目标优化随时间变化的网络流：自动驾驶车辆支持的路径控制方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-09 DOI: 10.1109/TITS.2024.3452068

Fang Zhang;Jian Lu;Xiaojian Hu

{"title":"Optimizing Time-Dependent Network Flows With Environmental Objectives: A Path Controlling Approach Enabled by Automated Vehicles","authors":"Fang Zhang;Jian Lu;Xiaojian Hu","doi":"10.1109/TITS.2024.3452068","DOIUrl":"10.1109/TITS.2024.3452068","url":null,"abstract":"The autonomy and controllability of automated vehicles (AVs) present new opportunities for regulating the traffic toward system-wide goals. This paper focuses on one aspect that has received increasing attentions in recent years, namely a path control scheme. This study contributes to the ongoing research by extending the path control scheme to a within-day dynamic context, with both system efficiency and environmental objectives taken into consideration. Moreover, it also contributes to the domain of operations research by developing a unified optimization framework to determine the best control strategy. Specifically, we assume that AVs on the roads originally follow the dynamic user equilibrium (DUE) principle and can be controlled as either dynamic system optimum (DSO) users to minimize the total system travel time, or dynamic eco-system optimum (DESO) users to minimize the total emission by the central agent when they depart from origins. We formulate the optimal time-dependent control problem as a single-level optimization program, with the dynamic equilibrium conditions integrated as a gap function-based constraint. A method for evaluating the path marginals is also presented, based on which a Lagrangian relaxation-based heuristic algorithm is proposed to solve the problem. A robust optimization technique is also developed to tackle the non-unique equilibrium flow patterns of the multiclass dynamic traffic assignment problem. Based on the numerical results, it is found that some O-D pairs enjoy higher control priority than others, and theoretically a satisfactory system performance can be achieved by controlling a small fraction of the traffic.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15654-15672"},"PeriodicalIF":7.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220410","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}

引用次数: 0

Resource-Efficient Model-Free Adaptive Platooning Control for Vehicles With Encrypted Information 信息加密车辆的资源效率型无模型自适应排队控制

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-09 DOI: 10.1109/tits.2024.3454428

Huarong Zhao, Qiuju Zhang, Li Peng, Hongnian Yu

引用次数: 0

Comments on “A Quantum Cryptographic Protocol for Secure Vehicular Communication” 关于 "用于安全车载通信的量子加密协议 "的评论

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-09 DOI: 10.1109/tits.2024.3448479

Run-Hua Shi

引用次数: 0

A Social Welfare Theory-Inspired Lexicographic Optimal Charging Scheduling Framework for Modular EV Fast Charging Stations 受社会福利理论启发的模块化电动汽车快速充电站词典优化充电调度框架

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-09 DOI: 10.1109/TITS.2024.3451498

Can Berk Saner;Jaydeep Saha;Dipti Srinivasan

{"title":"A Social Welfare Theory-Inspired Lexicographic Optimal Charging Scheduling Framework for Modular EV Fast Charging Stations","authors":"Can Berk Saner;Jaydeep Saha;Dipti Srinivasan","doi":"10.1109/TITS.2024.3451498","DOIUrl":"10.1109/TITS.2024.3451498","url":null,"abstract":"Fast charging technology is crucial for widespread electric vehicle (EV) adoption. To enhance efficiency and scalability, charging equipment manufacturers are shifting towards a modular architecture in fast charging stations (FCSs). This architecture features multiple converter modules and charging ports, allowing flexible power allocation through module-to-port assignment. However, it introduces challenges, particularly when ports operate with fewer modules, necessitating EV charging scheduling schemes to allocate limited FCS capacity while maintaining high quality-of-service (QoS). Traditional scheduling methods are ill-suited for modular FCS settings due to unique characteristics such as discrete module-to-port allocation, state-of-charge-dependent charge curves, and power ramp rate limits. This work proposes a social welfare-inspired EV scheduling framework for modular FCSs, using lexicographic optimization and receding horizon control. The framework includes a computationally efficient charge curve model based on sliding convex hulls and a mathematical model tailored for modular FCSs. The three-stage lexicographic model, derived from Rawlsian and Benthamite social welfare theories, accommodates customer preferences and EV characteristics for high QoS provision. A welfare score metric, adapted from social welfare theories, is also introduced for multi-faceted QoS assessment. Across ceteris paribus experiments, the proposed framework consistently outperforms three benchmark methods, with a margin of up to 34% in welfare scores over the second-best method. In a diverse set of randomized EV arrival scenarios, the framework enables a median welfare around 85%, outperforming the benchmarks by at least 7.8%, with statistical tests confirming its significance. Moreover, ramp rate violations are kept at a minimum, while the computational efficiency and scalability are verified.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"18648-18660"},"PeriodicalIF":7.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227378","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}

引用次数: 0