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A Novel Framework to Generate Synthetic Video for Foreground Detection in Highway Surveillance Scenarios

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第6期24卷 5958-5970页

作者： Li, Xuan Duan, Haibin Liu, Bingzi Wang, Xiao Wang, Fei-Yue Peng Cheng Lab Shenzhen 518000 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Key Lab Virtual Real Technol & Syst Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Foreground detection (FD) plays an important role in the domain of video surveillance for highway. The design of advanced FD algorithms requires large-scale and diverse video dataset. However, collecting and labeling real dataset is still time-consuming, labor-intensive, and highly subjective. To address this issue, we first use computer graphics (CG) to clone real highway scenarios (HS) and generate synthetic multi-challenge video datasets, called "Synthetic-HS (CG)", automatically labeled with accurate pixel-level ground truth. The Synthetic-HS (CG) dataset contains eight imaging condition sequences for computer vision research. Then, we design an image translation (IT) model that translates source domain (Synthetic-HS (CG)) to target domain (real). This model uses skip connections and attention module to generate realistic synthetic images "Synthetic-HS (IT)". We use publicly available Synthetic-HS in combination with the corresponding real video sequence to conduct experiments. The experiment results suggest that: 1) The Synthetic-HS (CG) dataset enables us to provide precise quantitative evaluation of the drawbacks of foreground detection methods 2) The realistic Synthetic-HS (IT) images can be used to promote the visual perception in highway video surveillance.

关键词： Computational modeling Surveillance Road transportation Generative adversarial networks Synthetic data Solid modeling Visualization Foreground detection highway surveillance parallel intelligent synthetic dataset scenarios engineering

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The Prospects of Multi-modal Pre-trained Models in Epidemic Forecasting 9th

The Prospects of Multi-modal Pre-trained Models in Epidemi...

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9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Fei, Jiaqiang Zhao, Pengfei Luo, Tianyi Wang, Jiaojiao Cao, Zhidong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819758029

Accurate epidemic forecasting is imperative in light of the increasing global threat posed by epidemic diseases. Presently, researchers predominantly approach this challenge as a time-series prediction task and have proposed a variety of effective methodologies. However, two critical issues deserve thorough consideration. Firstly, the transmission of infectious diseases is a profoundly intricate process influenced by a multitude of factors, encompassing complex dynamics and propagation mechanisms. In contrast to the multifaceted nature of real-world scenarios, prevailing modeling approaches often exhibit undue simplification, resulting in imprecision and a deficit of interpretability. Secondly, existing methodologies excessively rely on data and require substantial adaptation for new infectious disease scenarios. The endeavor to conduct rapid and precise simulations and predictions during the early stages of transmission is challenging due to limited data availability. Thus, we aim to explore the feasibility and effective strategies of leveraging multi-modal pre-trained technology for epidemic forecasting. This approach offers the potential to comprehensively grasp the transmission process from multi-view perspectives and acquire richer representations by integrating multi-modal data. Moreover, through pre-trained, this methodology captures overarching patterns and principles of infectious disease transmission, facilitating swift and accurate predictions via fine-tuning, particularly in scenarios of sparse early-stage transmission data. We discuss the prospects of multi-modal pre-trained models in epidemic forecasting and provide feasible strategies for constructing the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Advances in Flexible Robotic Manipulator systems - Part II: Planning, control, Applications, and Perspectives

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第3期29卷 1680-1689页

作者： Li, Bai Li, Xinyuan Gao, Hejia Wang, Fei-Yue Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This work offers a review of techniques related to flexible robotic manipulator (FRM) systems. Our review is divided into two parts. The first part presents the background of FRMs, a survey of previous surveys, and FRM dynamic modeling methods. This article is the second part, which reviews the advances in FRM motion planners and controllers, along with the emerging applications and perspectives. Compared with previous FRM surveys, our work presents a more detailed categorization and extensive review of emerging research trends since 2020. The ongoing development of FRMs is marking advancements in the field of robotics.

关键词： Planning Vibrations Trajectory Uncertainty Neural networks End effectors Splines (mathematics) Artificial intelligence flexible material flexible robotic manipulator vibration suppression

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Coordinated Rescheduling of Train Timetable and Crew Scheme for Passenger-Freight Collinear Railway

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第5期11卷 5828-5838页

作者： Wang, Rui Zhou, Min Wang, Hongwei Yang, Bo Dong, Hairong Wang, Fei-Yue Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China China Railway Beijing Grp Co Ltd Dept Dispatching Beijing 100038 Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

On a passenger-freight collinear railway, the freight train operation level is comparatively low, frequently resulting in significant deviations from the original timetable and crew plan in the presence of various interferences. This article focuses on the problem of coordinated rescheduling of train timetable and crew scheme in the presence of disruptions on a double-track passenger-freight collinear railway. We develop a mixed-integer linear program (MILP) model considering the distinct priorities of passenger and freight trains, as well as crew operations, thereby surpassing the current practice of independently adjusting train timetable and crew plan to achieve a collaborative solution. The objective is to minimize delays for passenger trains and deviations in crew schedule, while maximizing the delivery rate of freight trains at railway Bureau boundary stations prior to the settlement time. Furthermore, for large-scale delays, we design a solution algorithm based on the rolling horizon approach to enhance computational efficiency. To validate the effectiveness of the proposed model, simulation experiments are conducted using actual running data from the Beijing-Shanghai railway. The experimental results illustrate that our coordinated model enhances the feasibility of adjustment outcomes during emergencies, in contrast to the model that neglects crew connections. Additionally, our proposed algorithm guarantees a solving error of under 5% and reduces solving time by over 60% compared with the results obtained by CPLEX. Moreover, three additional comparison experiments are conducted to further demonstrate the impact of crew activities on train operation adjustments, which also indicate that our approach can provide dispatchers with more feasible train operation adjustment schemes in terms of crew utilization.

关键词： Rail transportation Delays Mathematical models Schedules Interference Dispatching Optimization Crew operation interference passenger-freight collinear railway rolling horizon algorithm train operation adjustment

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A New Integral Critic Learning for Optimal Tracking control with Applications to Boiler-Turbine systems

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OPTIMAL control APPLICATIONS & METHODS 2023年第2期44卷 830-845页

作者： Wei, Qinglai Liu, Yujia Lu, Jingwei Ling, Jun Luan, Zhenhua Chen, Mingliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China China Nucl Power Engn CO LTD State Key Lab Nucl Power Safety Monitoring Techno Shenzhen Guangdong Peoples R China

Optimal control theory and reinforcement learning are gradually being used in the field of industrial control. In this article, a new optimal tracking control scheme for 160 MW boiler-turbine systems is proposed based on an online policy iteration integral reinforcement learning (IRL) method. Firstly, a self-learning state tracking control with a cost function is developed to deal with the optimal tracking control problems for the boiler-turbine nonlinear system. Then with a modified cost function, a policy iteration-based IRL method is introduced to obtain the optimal control law. Finally, the monotonicity and the convergence of the cost function is analyzed and the detailed implementation of the policy iteration-based IRL method is provided via neural networks. The simulation results show that the control of the boiler-turbine system can converge in a short time by using this online iterative method. Through a theoretical simulation case, it can be concluded that the proposed method is more advanced compared with the MPC method.

关键词： adaptive dynamic programming boiler-turbine system integral reinforcement learning neural network policy iteration

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Parallel Vision for Intelligent Transportation systems in Metaverse: Challenges, Solutions, and Potential Applications

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第6期53卷 3400-3413页

作者： Zhang, Hui Luo, Guiyang Li, Yidong Wang, Fei-Yue Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Metaverse and intelligent transportation system (ITS) are disruptive technologies that have the potential to transform the current transportation system by decreasing traffic accidents and improving driving safety. The integration of Metaverse and transportation technology, called metaverse transportation system (MTS), can greatly improve the intelligence of real transportation system. The digital models built in MTS help to simulate the full life cycle of physical entities, which equip the virtual space with controllability and flexibility. In this article, we concentrate on the field of environment perception, which is the basic function of intelligent vehicles in MTS. To overcome the poor scalability of traditional environment perception methods, we develop the framework of parallel vision for ITS in metaverse (PVITS), consisting of construction of virtual transportation space, model learning based on computational experiments, and feedback optimization based on parallel execution. This article highlights opportunities brought by PVITS in terms of model precision and generalization improvement. Then, the challenges of PVITS are discussed, i.e., distribution difference between virtual and real transportation space, structure design and theoretical interpretation of vision models, and data security and privacy in virtual transportation space. After that, we present several solutions to tackle the application challenges and fully exploit the superior characteristics of PVITS while attenuating their negative side effects. Some potential applications are also given to represent the effectiveness and reliability of PVITS.

关键词： Intelligent transportation system (ITS) metaverse parallel vision

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China 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 Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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Event-/Self-Triggered Adaptive Optimal Consensus control for Nonlinear Multiagent System With Unknown Dynamics and Disturbances

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第3期55卷 1476-1485页

作者： Wei, Qinglai Jiang, Hao Macau Univ Sci & Technol Inst Syst Engn Macau 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 100049 Peoples R China

In this article, the optimal consensus tracking control for nonlinear multiagent systems (MASs) with unknown dynamics and disturbances is investigated via adaptive dynamic programming (ADP) technology. Taking into account the disturbance as control inputs, the optimal control problem for the nonlinear MASs is reformulated as a multiplayer zero-sum differential game. In addition, a single network ADP structure is constructed to approach the optimal consensus control policies. Subsequently, an event triggering mechanism is implemented to reduce the workload of the controller and conserve computing and communication resources. Since then, in order to further streamline the intricacies of controller design, this work is extended to self-triggered cases to alleviate the need for hardware devices to continuously monitor signals. By using the Lyapunov method, the stability of the nonlinear MASs and the uniform ultimate boundedness (UUB) of the weight estimation error of the critic neural network (NN) is proved. Finally, the simulation results for an MAS consisting of a single-link robot validate the effectiveness of the proposed control method.

关键词： Consensus control Artificial neural networks Vectors Optimal control Event detection Nonlinear dynamical systems Multi-agent systems Monitoring Hardware Directed graphs Adaptive dynamic programming (ADP) event-triggered control (ETC) nonlinear multiagent systems optimal consensus control self-triggered control (STC)

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Parallel Transportation in TransVerse: From Foundation Models to DeCAST

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第12期24卷 15310-15327页

作者： Zhao, Chen Wang, Xiao Lv, Yisheng Tian, Yonglin Lin, Yilun Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230039 Peoples R China Shanghai AI Lab Shanghai 200232 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China

Rapid development of AI technologies has propelled the seamless integration of physical and cyber worlds with various kinds of online/offline information collected from millions of multimodal sensing systems. The complexity, diversity and uncertainty inherited in such systems, such as Intelligent Transportation systems (ITSs), have gone far beyond human capacity of managing and controlling. Our team is among the first to propose the idea of utilizing the nearly unlimited computational resources in cyberspace to construct a bottom-up and top-down combined artificial ITSs for testing, experimenting, representation, verification, and validation of physical ITSs. Especially, the parallel transportation has been developed for safer, smarter, greener, and more reliable transportation services. After three decades of research and field studies, the DeCAST in Transverse, i.e., Decentralized/Distributed Autonomous Operations/Organizations (DAO) in transportation systems, has been envisioned. In this paper, we introduce its architecture, operational processes, software and hardware platforms, and real world applications. Specifically, a transportation foundation model driven by artificial transportation systems, parallel learning and federated intelligence, named TengYun, is outlined for DeCAST.

关键词： Cyber-physical-social systems (CPSS) artificial systems computational experiments parallel execution (ACP) decentralized/distributed autonomous operations and organizations (DAO) big AI models

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Approximate Markov Perfect Equilibrium of Joint Offloading Policy for Multi-IV Using Reward-Shared Distributed Method

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3658-3671页

作者： Li, Chao Liu, Fagui Wang, Bin Tang, Xuhao Liu, Jie Chen, C. L. Philip South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518066 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

In this article, we investigate the problem of optimizing the joint offloading policy in a distributed manner for multiple intelligent vehicles (IVs). During the journey in vehicular edge computing (VEC) networks, IVs continually optimize their joint offloading policy to minimize the long-term accumulated costs generated by executing computational tasks. The stochastic and repetitive interactions among IVs is modeled as a Markov game process. In this way, the optimization of the joint offloading policy is transformed to approximate a Markov perfect equilibrium in a general-sum Markov game. Moreover, we argue that training in the practical VEC networks using the classical centralized training and decentralized executing (CTDE) framework involves challenges of privacy and computational complexity. Motivated by these, we propose a reward-shared distributed policy optimization (RSDPO) method for the considered VEC networks to optimize the joint offloading policy. The experimental results demonstrate that the set of joint offloading policies using RSDPO approximates a Markov perfect equilibrium, and our RSDPO presents significant advantages in terms of converged latency and energy consumption compared with other methods.

关键词： Vehicular edge computing joint offloading policy distributed policy optimization Markov game

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