Article
Engineering, Civil
Lei Liu, Ming Zhao, Miao Yu, Mian Ahmad Jan, Dapeng Lan, Amirhosein Taherkordi
Summary: This paper proposes a task offloading scheme in Vehicular Edge Computing (VEC) that utilizes multi-hop vehicle computation resources to improve response delay and enhance user experience.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Xingwang Li, Yike Zheng, Mohammad Dahman Alshehri, Linpeng Hai, Venki Balasubramanian, Ming Zeng, Gaofeng Nie
Summary: This study focuses on the reliable and secure performance of Internet-of-Vehicle enabled Maritime Transportation Systems communication. Analytical expressions for outage probability and intercept probability are obtained. The results show specific characteristics of system performance in high signal-to-noise ratio and high main-to-eavesdropper ratio regimes, and a trade-off between reliability and security can be achieved by carefully selecting system parameters.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Jinchao Chen, Ying Zhang, Lianwei Wu, Tao You, Xin Ning
Summary: This study focuses on the automatic path planning of autonomous unmanned aerial vehicles (UAVs) with different capabilities using linear programming and clustering algorithms to minimize the time consumption of search tasks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Feng Ding, Keping Yu, Zonghua Gu, Xiangjun Li, Yunqing Shi
Summary: This study introduces a generative adversarial network to improve various degraded images, with a novel architecture to handle additional attributes between image styles, enhancing the accuracy and training efficiency of restoration. Compared to other methods, it shows better performance in restoration and is reliable for assisting context prediction in autonomous vehicles.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, Baocai Yin
Summary: Traffic prediction is crucial for intelligent transportation systems, and deep learning methods have greatly improved the accuracy of traffic prediction. This study provides a comprehensive survey on deep learning-based approaches in traffic prediction, summarizing the latest methods and discussing open challenges in the field.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhihan Lv, Dongliang Chen, Hailin Feng, Hu Zhu, Haibin Lv
Summary: This study explores the impact of Digital Twins in Unmanned Aerial Vehicles on providing medical resources during COVID-19 prevention and control, introducing deep learning algorithms and proposing a UAV DTs information forecasting model. The model shows better performance in terms of transmission delays, energy consumption, task completion time, and resource utilization rate compared to other state-of-art models as end-users and task proportion increase.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Su Liu, Jiong Yu, Xiaoheng Deng, Shaohua Wan
Summary: In this study, an efficient communication approach called FedCPF is proposed to achieve fast convergence and improve testing accuracy in vehicular edge computing. By customizing local training strategies, introducing partial client participation rules, and implementing flexible aggregation policies, FedCPF outperforms the traditional FedAVG algorithm and performs well in various FL settings.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dongpu Cao, Xiao Wang, Lingxi Li, Chen Lv, Xiaoxiang Na, Yang Xing, Xuan Li, Ying Li, Yuanyuan Chen, Fei-Yue Wang
Summary: This is the brief report of the first IEEE Distributed/Decentralized Hybrid Workshop on Future Directions of Intelligent Vehicles. The workshop addressed various issues related to intelligent vehicles and potential topics for future research and development.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Computer Science, Interdisciplinary Applications
Zhong Zhou, Junjie Zhang, Chenjie Gong
Summary: This research proposes a deep learning-based model, YOLOv4-ED, to solve the challenges in traditional tunnel lining defect detection methods. By using EfficientNet as the backbone and introducing DSC, YOLOv4-ED achieves higher detection accuracy and efficiency. A tunnel lining defect detection platform (TLDDP) is built based on the robust and cost-effective YOLOv4-ED, enabling automated detection of various lining defects.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Xiaoying Liu, Bin Xu, Xiong Wang, Kechen Zheng, Kaikai Chi, Xianzhong Tian
Summary: This paper investigates the impacts of sensing energy and data availability on the secondary throughput of energy harvesting cognitive radio networks. It considers two extreme cases of data arrival processes and studies the effects on secondary throughput. The paper also compares non-cooperative spectrum sensing and cooperative spectrum sensing scenarios. It utilizes an energy threshold approach to balance energy harvesting and data transmission. Simulation results show the relationship between sensing energy, data availability, and secondary throughput.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Civil
Yuchuan Du, Bohao Qin, Cong Zhao, Yifan Zhu, Jing Cao, Yuxiong Ji
Summary: A novel spatio-temporal synchronization method is proposed for roadside MMW radar-camera sensor fusion, which effectively reduces temporal deviation and spatial deviation between the camera and radar. The method is validated using measurement data from Donghai Bridge in Shanghai, demonstrating significant improvements in spatial alignment.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Parth Kothari, Sven Kreiss, Alexandre Alahi
Summary: This study explores the development of human trajectory forecasting, comparing handcrafted representations with deep learning methods, and proposing two data-driven approaches to effectively capture social interactions. By establishing the TrajNet++ benchmark and introducing new performance metrics, the superiority of the proposed method on real-world and synthetic datasets is validated.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zehua Ye, Dan Zhang, Zheng-Guang Wu, Huaicheng Yan
Summary: This paper focuses on the intelligent event-triggering-based positioning control of networked unmanned marine vehicle (UMV) systems with hybrid attacks. A stochastic switched Takagi-Sugeno (T-S) fuzzy system model is proposed for the UMV systems subject to DoS and Deception attacks, and an asynchronous advantage actor-critic (A3C) learning-based event-triggering approach is introduced to reduce communication load. The stability of the closed-loop system is analyzed using Lyapunov stability theory and switched system analysis, and the effectiveness of the proposed resilient control strategy is verified through a networked UMV system example.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Qin Zou, Qin Sun, Long Chen, Bu Nie, Qingquan Li
Summary: SLAM is crucial for indoor navigation in autonomous vehicles and robots, with visual SLAM having drawbacks in tracking feature points in environments lacking texture. On the other hand, LiDAR SLAM can offer more robust localization by utilizing 3D spatial information directly captured by LiDAR point clouds.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Transportation Science & Technology
Guofa Li, Yifan Yang, Shen Li, Xingda Qu, Nengchao Lyu, Shengbo Eben Li
Summary: This study proposes a lane change decision-making framework based on deep reinforcement learning to find a risk-aware driving decision strategy with the minimum expected risk for autonomous vehicles. The proposed methods are evaluated in CARLA and show better driving performances than previous methods.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Review
Energy & Fuels
Wenhao Yu, Yi Guo, Zhen Shang, Yingchao Zhang, Shengming Xu
Summary: The high demand for electric vehicles in China has led to an increase in power lithium-ion battery (LIB) production, resulting in a large number of spent power LIBs. Comprehensive recycling, including recovery and reuse, is a promising direction to maximize the utilization of spent power LIBs. This article reviews the current situation of comprehensive recycling of spent LIBs in China and discusses the pretreatment, recovery of materials, and reuse process of spent power LIBs.
Article
Engineering, Civil
Wali Ullah Khan, Muhammad Awais Javed, Tu N. Nguyen, Shafiullah Khan, Basem M. Elhalawany
Summary: This paper introduces an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network, aiming to maximize the total energy efficiency of the network while ensuring the minimum data rate of all IoVs. The proposed framework outperforms a benchmark conventional IoV framework in terms of performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zhenpo Wang, Chunbao Song, Lei Zhang, Yang Zhao, Peng Liu, David G. Dorrell
Summary: In this article, a data-driven method based on massive real-world EV operating data is proposed for diagnosing battery charging capacity abnormalities. By utilizing multiple input parameters and a tree-based prediction model for training, along with a statistical method for abnormality diagnosis, the proposed method demonstrates the highest prediction accuracy.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Engineering, Civil
Yuan Cao, Yongkui Sun, Guo Xie, Peng Li
Summary: This study introduced a sound-based fault diagnosis method for railway point machines, achieving over 99% diagnosis accuracy through feature selection and ensemble classifier optimization.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yeongmin Ko, Younkwan Lee, Shoaib Azam, Farzeen Munir, Moongu Jeon, Witold Pedrycz
Summary: The proposed traffic line detection method, PINet, based on key points estimation and instance segmentation, is adaptive to various environments and computing power. PINet allows for choosing the size of trained models based on the target environment's computing power, achieving competitive accuracy and false positive rates on popular public datasets like CULane and TuSimple.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
