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IEEE International Conference on systems, Man and Cybernetics

作者： Rui Qin Juanjuan Li Xiaolong Liang Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This paper presents management-Oriented Operating systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making.

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Neural-Dynamics-Based Active Steering control for Autonomous Vehicles with Noises

Neural-Dynamics-Based Active Steering Control for Autonomous...

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IEEE Symposium on Intelligent Vehicle

作者： Ying Liufu Long Jin Fei-Yue Wang School of Information Science and Engineering Lanzhou University Lanzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper introduces a neural-dynamics-based active steering control (NDASC) scheme developed under artificial systems, computational experiments, and parallel execution (ACP) framework, aimed at enhancing the stability and reliability of autonomous vehicles in noisy environments. Based on the Taylor expansion theorem, noises can be represented in the form of polynomials for the desired accuracy, and therefore polynomial noises can be viewed as a more generalized representation of noises. Then, the proposed NDASC scheme includes a model predictive active steering control (MPASC) strategy solved by a polynomial noise resilience neural dynamics (PNRND) model. Computational experiments parallelly implemented upon the CarSim-Simulink platform substantiate the effectiveness and robustness of the proposed NDASC scheme, providing significant theoretical and practical insights for control strategies of autonomous vehicles under various noisy environments.

关键词： Noise Predictive models Polynomials Stability analysis Robustness Noise measurement Vehicle dynamics

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CenterNet3D: An Anchor Free Object Detector for Point Cloud

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第8期23卷 12953-12965页

作者： Wang, Guojun Wu, Jian Tian, Bin Teng, Siyu Chen, Long Cao, Dongpu Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510275 Guangdong Peoples R China Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point--the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://***/wangguojun2018/CenterNet3d.

关键词： Three-dimensional displays Feature extraction Detectors Object detection Proposals Laser radar Heating systems Point cloud autonomous vehicles deep learning 3D detection anchor free

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Vehicle Trajectory Prediction Based on Graph Attention Network 6th

Vehicle Trajectory Prediction Based on Graph Attention Netwo...

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Chaochen, Zhuolei Zhang, Qichao Li, Ding Li, Haoran Pang, Zhonghua North China University of Technology Beijing100144 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Hunan University Changsha410082 China

ISBN: (纸本)9789811692468

Trajectory prediction is one of the main challenges to autonomous vehicles. Except for the predicted vehicle historical trajectory information, viable solutions for this task must also consider the static geometric context, such as lane centerlines, and the dynamic interaction information among traffic participants, such as the influence of surrounding vehicles or pedestrians on the predicted vehicle. Recently, mainstream methods are mostly based on recurrent neural networks, which have achieved state-of-the-art (SOTA) performance on distance-based prediction metrics. In order to consider the influence of static geometric context and dynamic interaction information better for the vehicle trajectory prediction, we propose a two-stage prediction framework and a combined VAE-GAT model. Stage I estimates the future destination point of the predicted vehicle using a destination point estimation module based on VAE. In stage II, it produces multiple trajectories using a multiple trajectories prediction module based on GAT which consists of an LSTM-based encoder-decoder and a graph attention network. Finally, one specific trajectory is selected from multiple trajectories conditioned on the estimated destination point. The experimental results and performance comparison based on the Argoverse motion forecasting dataset are given to prove the effectiveness of the proposed method. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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An intelligent maritime scene frame prediction based on digital twins technology 1

An intelligent maritime scene frame prediction based on digi...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Guo, Zhong-Zheng Wang, Kai Qin, Jiang-Yi Li, Xian-Bin Ran, De-Chao Shen, Yu Unmanned Systems Research Center National Innovation Institute of Defense Technology Academy of Military Science Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

This paper proposes a scene frame prediction system based on digital twin technology. The system is mainly composed of an unmanned perception module, a communication link module, a command center module that provides intelligent computing services, and an interaction and visualization module for human operators. These modules together constitute a system for predicting and recovery the time-series matrix composed of scene frames and object frames. At the same time, in view of the current problems of lack of real-time information and long response time in the maritime search and rescue (SAR) field, this paper instantiates the above system to improve the existing SAR system. Satellites and unmanned vehicles perceive part of the scene data of the real cyber-physical system in real-time and divide them into multiple frames according to the timestamps. In addition, this paper also proposes an environmental graph-attention network (Env-GAT) to achieve efficient and reliable frame predicting. Finally, this paper verifies that the performance of the spatiotemporal attention graph network model has reached the optimal level through computational experiments. © 2021 IEEE.

关键词： Forecasting

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Valuing Equity-Linked Guaranteed Minimum Death Benefits with European-Style Asian Payoffs Under the Regime-Switching Diffusion Process with Jump

SSRN

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SSRN 2022年

作者： Wang, Yayun Liu, Shengda Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

In this paper, we consider a novel and efficient method to price equity-linked guaranteed minimumdeath benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. In thesituation of continuous transition of market economy, we assume that the underlying asset price processfollows regime-switching Ĺevy model and use complex Fourier series (CFS) expansion method to derivethe approximate value of GMDB products. The error analysis shows an exponential convergence rateunder geometric Asian and arithmetic Asian payoffs. Furthermore, the CFS approach is comparedwith other Fourier transform methods and Monte Carlo simulation, the numerical experiment resultsillustrate our method has a distinct advantage over other methods in both efficiency and accuracy. © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2055-2062页

作者： Xiujuan Wang Mengzhen Kang Hequan Sun Philippe de Reffye Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Beijing Engineering Research Center of Intelligent Systems and Technology Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Department of Chromosome Biology Max Planck Institute for Plant Breeding ResearchBeijing 100049China Faculty of Biology LMU MunichGroßhaderner Str.2Planegg-Martinsried D-82152Germany AMAP Univ MontpellierCIRADCNRSINRAEIRDMontpellier F-34398France IEEE Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The demand for food is tremendously increasing with the growth of the world population,which necessitates the development of sustainable agriculture under the impact of various factors,such as climate *** fulfill this challenge,we are developing Metaverses for agriculture,referred to as Agri Verse,under our Decentralized complex Adaptive systems in Agriculture(De CASA)project,which is a digital world of smart villages created alongside the development of Decentralized Sciences(De Sci)and Decentralized Autonomous Organizations(DAO)for Cyber-Physical-Social systems(CPSSs).Additionally,we provide the architectures,operating modes and major applications of De CASA in *** achieving sustainable agriculture,a foundation model based on ACP theory and federated intelligence is ***,we discuss the challenges and opportunities.

关键词： Parallel Agriculture management and control AgriVerse Agriculture CPSS ACP DAO-Based Platform Precision Agriculture

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Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence:mechanisms and applications of cognition,management,and control for complex systems

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Frontiers of Information Technology & Electronic Engineering 2022年第8期23卷 1142-1157页

作者： Fei-Yue WANG Jianbo GUO Guangquan BU Jun Jason ZHANG The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Grid Cooperation of China Beijing 100031China China Electric Power Research Institute Beijing 100192China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

In this paper,we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation(HM-KA)as the technical mechanism of hybrid augmented intelligence(HAI)based complex system cognition,management,and control(CMC).We describe the historical development of complex system science and analyze the limitations of human intelligence and machine *** need for using human-machine HAI in complex systems is then explained in *** concept of“mutually trustworthy HM-KA”mechanism is proposed to tackle the CMC challenge,and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid *** is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

关键词： complex systems Human-machine knowledge automation Parallel systems Bulk power grid dispatch Artificial intelligence Internet of Minds(IoM)

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Towards Integrated Traffic control with Operating Decentralized Autonomous Organization 26

Towards Integrated Traffic Control with Operating Decentrali...

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IEEE 26th International Conference on Intelligent Transportation systems (ITSC)

作者： Yao, Shengyue Yu, Jingru Yu, Yi Xu, Jia Dai, Xingyuan Li, Honghai Wang, Fei-Yue Lin, Yilun Shanghai AI Lab Urban Comp Shanghai Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Res & Dev Ctr Transport Ind Autonomous Driving Te Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China

ISBN: (纸本)9798350399462

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITS.

关键词： Integrated control

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AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction

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NEUROCOMPUTING 2021年 445卷 298-308页

作者： Zhou, Hao Ren, Dongchun Xia, Huaxia Fan, Mingyu Yang, Xu Huang, Hai Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin Peoples R China Meituan Beijing Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Predicting pedestrian trajectories in the future is a basic research topic in many real applications, such as video surveillance, self-driving cars, and robotic systems. There are two major challenges in this task, the complex interaction modeling among pedestrians and the unique motion pattern extraction for each pedestrian. Regarding the two challenges, an attention-based interaction-aware spatio-temporal graph neural network is proposed for predicting pedestrian trajectories. There are two components in the proposed method: spatial graph neural network for interaction modeling, and temporal graph neural network for motion feature extraction. Spatial graph neural network uses an attention mechanism to capture the spatial interactions among all the pedestrians at each time step. Meanwhile, temporal graph neural network uses an attention mechanism to capture the temporal motion pattern of each pedestrian. Finally, a time-extrapolator convolutional neural network is used in the temporal dimension of the aggregated graph features to predict the future trajectories. Experimental results on two benchmark pedestrian trajectory prediction datasets demonstrate the competitive performances of the proposed method in terms of both the final displace error and the average displacement error metrics as compared with state-of-the-art trajectory prediction methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Pedestrian trajectory prediction Graph neural networks Spatio-temporal graph Graph attention

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