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MetaSocieties in Metaverse: MetaEconomics and Metamanagement for MetaEnterprises and MetaCities

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第1期9卷 2-7页

作者： Wang, Fei-Yue Qin, Rui Wang, Xiao Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China

Welcome to the first issue of the IEEE Transactions on Computational Social systems (TCSS) of 2022. We would like to take this opportunity to express our sincere thanks to our associate editors, reviewers, authors, and readers for your great support and effort devoted to IEEE TCSS. Happy New Year to you all, and cheers to health, happiness, and high-producing in 2022!

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Metaverses-Based Parallel Oil Fields in CPSS: A Framework and Methodology

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2138-2147页

作者： Wang, Xingxia Cheng, Xiang Lu, Jingwei Kwan, Oliver Li, Shixing Ping, Zhixing Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Motion G Inc Singapore City 068898 Singapore North Automatic Control Technol Inst Taiyuan 030006 Peoples R China

Aiming to provide a novel paradigm of oil fields, metaverses-based parallel oil fields are proposed in this article. Compared with the existing smart/intelligent oil fields in cyber-physical systems (CPS), parallel oil fields can take human factors into full consideration and expand the operation space to cyber-physical-social systems (CPSS), which can be regarded as the abstract and scientific explanation of metaverses. In the proposed parallel oil fields, there are three kinds of workers (human workers, digital workers, and robotic workers) coordinating to construct a more reliable and intelligent oil field. Furthermore, the framework and methodology of parallel oil fields are illustrated by parallel systems and the artificial systems, computational experiments, and parallel executions (ACP) approach. Based on the proposed framework, parallel oil fields are capable of generating a more trustworthy artificial system and guaranteeing the realization of the 6S (safety, security, sustainability, sensitivity, service, and smartness) goal. Finally, based on dynamometer cards, fault diagnosis of sucker rod pumping systems (SRPS) is investigated in parallel oil fields.

关键词： Artificial systems computational experiments and parallel executions (ACP) approach Cyber-physical-social systems (CPSS) digital workers fault diagnosis metaverses-based parallel oil fields parallel systems

Cascade Learning for Driver Facial Monitoring

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 404-412页

作者： Gou, Chao Zhou, Yuchen Xiao, Yao Wang, Xiao Yu, Hui Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England

As a non-invasive method, vision-based driver monitoring aims to identify risky maneuvers for intelligent vehicles and it has gained an increasing interest over recent years. However, most existing methods tend to design models for specific tasks, such as head pose or gaze estimation, which results in redundant models hampering real time applications. Besides, most driver facial monitoring methods ignore the correlation of different tasks. In this work, we propose a unified framework based on cascade learning for simultaneous facial landmark detection and head pose estimation, as well as simultaneous eye center detection and gaze estimation. In particular, built upon the key idea that facial landmark locations and 3D face model parameters are implicitly correlated, we introduce a cascade regression framework to achieve these two tasks simultaneously. After coarsely extracting the driver's eye region from the detected facial landmarks, we perform cascade regression for simultaneous eye center detection and gaze estimation. Leveraging the power of cascade learning allows our method to alternatively optimize facial landmark detection, head pose estimation, eye center localization, and gaze prediction. The comparison experiments conducted on benchmark datasets of 300-W, GI4E, BU, MPIIGaze, and driving dataset of SHRP2 demonstrate that our proposed method can achieve state-of-the-art performance with robust effectiveness on the real driver monitoring applications.

关键词： Task analysis Intelligent vehicles Three-dimensional displays Predictive models Pose estimation Monitoring Faces Driver monitoring cascade learning head pose estimation gaze estimation facial landmark detection

Integrated Inspection on PCB Manufacturing in Cyber-Physical-Social systems

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2098-2106页

作者： Wang, Yutong Wang, Jiangong Cao, Yansong Li, Shixing Kwan, Oliver Chinese Acad Sci Inst Automat State Key Lab Management & Controlof Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China North Automat Control Technol Inst Taiyuan Peoples R China Mot G Inc Singapore 068898 Singapore

The printed circuit boards (PCBs) industry is one of the fastest-growing industries in recent decades. The PCB manufacturing process is highly complicated and severely affected by social factors, which makes it very important to conduct integrated inspection, assuring and improving the production quality. In this article, we propose an artificial systems, computational experiments, and parallel execution-based integrated inspection method in cyber-physical-social systems (CPSS) to realize smart manufacturing. In this inspection system, rather than simply performing modeling, analysis, and control, we perform descriptive intelligence to construct production processes with limited multimodal information, perform predictive intelligence to conduct defect detection and defect prediction, and perform prescriptive intelligence to achieve defect diagnosis and defect management. In this way, our inspection system could offer a learning and training platform for workers to master professional inspection skills, provide an experimentation and evaluation platform for product defect monitoring and early warnings, and supply guidance about defect management and control to improve manufacturing processes. For technical implementation, we leverage a Transformer-based foundation model to achieve knowledge reasoning and human-computer interaction. As a result, we provide an innovative solution to cope with the challenges of quality inspection in current smart manufacturing, and expect its further applications in the PCB industry.

关键词： Artificial systems computational experiments and parallel execution (ACP) method cyber-physical-social systems (CPSS) foundation models (FMs) integrated inspection

TASF: Terrestrial-Aerial Synergistic Framework for a New Generation of Intelligent Transportation 27

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TASF: Terrestrial-Aerial Synergistic Framework for a New Gen...

27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Wang, Cong Nie, Dujun Zhang, Ruijun Teng, Siyu Chen, Long Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Hong Kong Baptist University Kowloon Hong Kong Waytous Inc. Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798331505929

Terrestrial-Aerial Synergistic Framework (TASF) represents a novel approach to intelligent transportation, integrating terrestrial and aerial vehicles to enhance urban transportation efficiency and capacity. In this paper, we offer an in-depth analysis of TASF, emphasizing its innovative applications in urban transportation, parcel delivery, and emergency response services. TASF incorporates a five-layer architecture comprising perception, communication, planning, control, and interaction. With the five-layer architecture, TASF can achieve effective collaboration among terrestrial and aerial vehicles, encompassing two principal features: terrestrial-aerial synergistic parallel perception and multi-agent parallel management. Parallel perception results from fusing Bird's Eye View (BEV) representations from Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), whereas decentralized multi-agent management employs foundational models to enhance decision-making capabilities. Additionally, we consider challenges and potential solutions related to TASF, including safety, reliability, energy efficiency, management and integration. TASF marks a paradigm-shifting leap in the realm of intelligent transportation systems, providing a scalable and efficacious solution tailored to the dynamic requirements of urban mobility and emergency services. © 2024 IEEE.

关键词： Decision making

Cognitive-Based Crack Detection for Road Maintenance: An Integrated System in Cyber-Physical-Social systems

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第6期53卷 3485-3500页

作者： Fan, Lili Cao, Dongpu Zeng, Changxian Li, Bai Li, Yunjie Wang, Fei-Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 116600 Peoples R China Dalian Nationalities Univ Sch Sci Dalian 116600 Peoples R China Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Effective road maintenance can not only achieve a balance between limited resources and long-term high-efficiency performance of road but also reduce the loss of life and property caused by road damage to vehicles and pedestrians. Due to the lack of a multidimensional dynamic monitoring system and enough extremely special data, the existing road maintenance system cannot accurately assess the road surface condition and provide timely early warning of sudden road damage. In this article, the M-RM system is proposed, that is, a metaverse-enabled road maintenance system based on cyber-physical-social systems (CPSSs), which fully utilizes the social and artificial system information of CPSS, as well as the simulation, monitoring, diagnosis and prediction functions of road systems in the virtual world of the metaverse. Then, in the road damage detection of system model in the virtual world, for the virtual data of the core assets of the metaverse, we propose an adaptive and information-preserving data augmentation (AIDA) algorithm-based nonclassical receptive field suppression and enhancement, an algorithm developed from human visual cognition. This algorithm enables the generation of a large amount of scarce fidelity data and avoids the introduced noise from impairing the performance of nonaugmented data. Finally, a crack detection algorithm named pay attention twice (PAT) is proposed, which uses the generated virtual data for training, and achieves secondary attention to high-frequency targets by fusing frequency-division convolution and mixed-domain attention mechanism. The detection performance of small targets in uncertain environments is enhanced. The metaverse system built in the current research can not only be used for road maintenance but also empower the traffic metaverse by using the traffic flow prediction module embedded in the algorithm. Experimental results demonstrate that the proposed algorithm can be applied to the road damage detection task under diff

关键词： Roads Maintenance engineering Metaverse Visualization Real-time systems Monitoring Safety Brain inspired crack detection data augmentation metaverse road maintenance visual cognition

How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 593-612页

作者： Hu, Xuemin Li, Shen Huang, Tingyu Tang, Bo Huai, Rouxing Chen, Long Hubei Univ Sch Artificial Intelligence Wuhan 430062 Peoples R China Hubei Univ Minist Educ Key Lab Intelligent Sensing Syst & Secur Wuhan 430062 Peoples R China Worcester Polytechn Inst Dept Elect & Comp Engn Worcester MA 01609 USA Beijing Huairou Acad Parallel Sensing Beijing 101499 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Waytous Inc Beijing 100083 Peoples R China

Developing autonomous driving technologies necessitates addressing safety and cost concerns. Both academic research and commercial applications of autonomous driving vehicles require extensive simulation and real-world testing. The challenge lies in effectively transferring driving knowledge from the virtual simulation world to the reality world, known as the reality gap (RG). This gap arises due to differences in lighting, textures, vehicle dynamics, and agents' behaviors between the two environments. To address this issue, researchers have explored three main approaches: sim2real, digital twins (DTs), and parallel intelligence (PI) technologies. This article reviews these solutions and examines their applications and innovations in autonomous driving. Furthermore, we delve into the state-of-the-art algorithms, models, and involved simulators, and discuss the developmental process from sim2real to DTs and PI. The presentation also sheds light on the challenges and future perspectives in the development of sim2real, DTs, and PI in the field of autonomous driving.

关键词： Autonomous vehicles Digital twins Task analysis Metalearning Virtual reality Vehicle dynamics Solid modeling Autonomous driving sim2real digital twins parallel intelligence reality gap

ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4422-4431页

作者： Li, Xuan Liu, Enlu Shen, Tianyu Huang, Jun Wang, Fei-Yue Peng Cheng Lab Shenzhen 518000 Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410001 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Macau Univ Sci & Technol Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The latest developments in parallel driving foreshadow the possibility of delivering intelligence across organizations using foundation models. As is well-known, there are limitations in scenario acquisition in the field of intelligent vehicles (IV), such as efficiency, diversity, and complexity, which hinder in-depth research of vehicle intelligence. To address this issue, this manuscript draws inspiration from scenarios engineering, parallel driving and introduces a pioneering framework for scenario generation, leveraging the ChatGPT, denoted as SeGPT. Within this framework, we define a trajectory scenario and design prompts engineering to generate complex and challenging scenarios. Furthermore, SeGPT, in combination with "Three Modes", foundation models, vehicle operating system, and other advanced infrastructure, holds the potential to achieve higher levels of autonomous driving. Experimental outcomes substantiate SeGPT's adeptness in producing a spectrum of varied scenarios, underscoring its potential to augment the development of trajectory prediction algorithms. These findings mark significant progress in the domain of scenario generation, also pointing towards new directions in the research of vehicle intelligence and scenarios engineering.

关键词： Parallel driving scenarios engineering foundation model vehicle operating system generative pre-trained transformer trajectory prediction

Metaverse-Enabled Intelligence for Open-Terrain Field Vehicle Fleets: Leveraging Parallel Intelligence and Edge Computing

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3111-3116页

作者： Shuai, Zhibin Hu, Zheng Gai, Jiangtao Chen, Yijie Chen, Jicheng Zhang, Hui Wang, Fei-Yue China North Vehicle Res Inst Beijing 100072 Peoples R China Chinese Scholar Tree Ridge Lab Beijing 100072 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong 999077 Peoples R China Beihang Univ Sch Transportat Sci Engn Beijing 100091 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Open-terrain field vehicle (OTFV) fleets, including mining trucks, construction machinery, and agricultural machinery, often encounter significantly more intricate scenarios and unique challenges than road vehicles. Enhancing the intelligence level of OTFV fleets can significantly enhance their operational effectiveness and improve energy efficiency. This perspective paper introduces a metaverse-enabled framework to improve the intelligence levels of OTFV fleets. The metaverse-enabled framework consists of the parallel intelligence-based vehicle fleet control and edge computing-based vehicle dynamics control levels. We first delve into the framework's specifics, covering open-terrain field metaverse, parallel intelligence, edge computing, and human-vehicle cooperation. We further discuss critical issues such as artificial general intelligence (AGI) enabled large control models, vehicle teleoperation, communication privacy, and edge scenario engineering. Additionally, we provide a detailed account of edge computing and integrated domain control within the vehicle dynamics control level, illustrating the interactions among human drivers, domain controllers, vehicle systems and open-terrain field metaverse. Ultimately, the proposed framework can potentially empower intelligence to OTFV fleets and other equipment clusters with complicated system compositions and challenging missions in complex environments.

关键词： Edge computing metaverse open-terrain field vehicle parallel intelligence vehicle fleet