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An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles

Autonomous Intelligent systems 2025年第1期5卷 1-20页

作者： Liu, Zhengqin Lei, Jinlong Yi, Peng Hong, Yiguang Department of Control Science and Engineering Tongji University Shanghai201804 China The Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China

Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constr

关键词： Autonomous vehicles

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Cascaded Voltage and Power control for Full-Bridge-Fed CLLC Resonant Converter to Expand Output Voltage Range

Cascaded Voltage and Power Control for Full-Bridge-Fed CLLC ...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Chen, Hung-Chi Chang, Ya-Chun Lin, Jia-Liang Wu, Chih-Chiang Department of Electronics and Electrical Engineering Hsinchu Taiwan Institute of Electrical and Control Engineering Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9798350376067

In this paper, the cascaded voltage and power control is proposed to expand the output voltage range for full-bridge-fed CLLC resonant converter. In first, the CLLC resonant circuit is analyzed based on pulse frequency modulation (PFM) and pulse width modulation (PWM). The output voltage is sensed and the power is calculated to tune the switching period to achieve the wide output range. A 3.0kW prototype circuit is implemented to demonstrate the proposed control. © 2024 IEEE.

关键词： Pulse width modulation

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A Multi-party Asymmetric Self-play Algorithm and Its Application in Multi-USV Adversarial Game Simulations 24

A Multi-party Asymmetric Self-play Algorithm and Its Applica...

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2024 International Conference on Machine Learning, Pattern Recognition and automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Enhancing Data Labeling Through Integration of SS-DBSCAN Clustering and Human-in-the-Loop 24

Enhancing Data Labeling Through Integration of SS-DBSCAN Clu...

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6th Asia Conference on Machine Learning and Computing, ACMLC 2024

作者： Monko, Gloriana J. Kimura, Masaomi Department of Functional Control Systems Shibaura Institute of Technology Tokyo Japan School of Engineering Shibaura Institute of Technology Koto City Tokyo Japan

ISBN: (纸本)9798400710018

This study introduces a novel method for integrating Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise (SS-DBSCAN) clustering with the human-in-the-loop approach to semi-supervised data labeling. We enhance the feature representation of text data using attention mechanisms, leading to the formation of more contextually relevant clusters. The clusters are then processed using SS-DBSCAN, where a custom similarity function aligns them closely with expert-labeled examples, facilitating efficient label propagation. Subsequently, human experts reevaluate the clustering outputs, refining the process by reassessing the feature similarities to the expert-labeled classes. This iterative process ensures high fidelity in label assignment and enhances the overall reliability of the semi-supervised learning approach. Applied to MIMIC III and Cancer Doc Classification datasets, this method not only aligns the data labels more closely with expert evaluations but also leverages human judgment to confirm the accuracy and reliability of the re-clustering process. Based on the BERT transformers model, the fine-tuned prediction model achieved 100% accuracy in predicting the correct label and evaluated the re-clustering technique's effectiveness, demonstrating 90.3% and 97.8% correctness in the re-clustered labels in MIMIC III and Cancer Doc Classification datasets, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1239-1249页

作者： Ke Li Shunyi Zhao Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuxi 214122China Department of Chemical and Materials Engineering University of AlbertaEdmonton AB T6G 2G6Canada

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally *** paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear *** high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense *** that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state ***,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing *** results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

关键词： filtering estimation error

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System Class Extensions of the Modulating Function based State Reconstruction for the Advection-Diffusion-Reaction Equation

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IEEE Transactions on Automatic control 2025年

作者： Friedrich, Folke Fokken, Nils Noack, Matti Reger, Johann Institute for Automation and Systems Engineering Technische Universität Ilmenau Control Engineering Group Ilmenau98693 Germany

In this paper, the state estimation of distributed parameter systems (PDEs) using the Modulating Function Method is extended to systems of two or higher spatial dimensions with spatially varying coefficients and general boundary conditions. At the center of the PDE observer approach lies the construction and solvability analysis of systematically obtained kernel equations determining the modulating functions. Additionally, a method for the state estimation of a class of parabolic systems with time-varying and nonlinear parameters based on Volterra integral transformations is presented. Furthermore, a linearization based strategy to deal with nonlinear reaction terms is presented. The stability of the resulting iterative feedback algorithm is proven. Numerical simulations show the validity and robustness of the methodology in two simulated examples. © 1963-2012 IEEE.

关键词： Iterative methods

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A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial Processes Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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Transformer-Based Macroscopic Regulation for High-Speed Railway Timetable Rescheduling

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1822-1833页

作者： Wei Xu Chen Zhao Jie Cheng Yin Wang Yiqing Tang Tao Zhang Zhiming Yuan Yisheng Lv Fei-Yue Wang Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Signal&Communication Research Institute China Academy Railway SciencesBeijing 100081China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.

关键词： High-speed railway reinforcement learning train timetable rescheduling Transformer

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Fuel Characterization of Waste Plastic Diesel from Mixed Waste Plastic Catalytic Pyrolysis 9

Fuel Characterization of Waste Plastic Diesel from Mixed Was...

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9th International Conference on engineering, Applied Sciences, and Technology, ICEAST 2023

作者： Aung, Zin Thu Charoenphonphanich, Chinda Kosaka, Hidenori School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Mechanical Engineering Bangkok Thailand School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo Japan

ISBN: (纸本)9798350322040

Plastics are an essential part of the human life and the global economy. However, the use of plastics has been associated with significant environmental problems due to their accumulation in landfills, as plastic waste does not degrade or degrades at very low pace. Nowadays, fast pyrolysis of waste plastic into valuable fuels is main platform method in minimizing not only the waste disposal but also could be used as an alternative fuel for internal combustion engines. The purpose of this study was to identify, quantify and compare the composition of waste plastic diesel (WPD) with the commercial diesel (CD) of Thailand. Simulated distillation (GC-FID) and n-d-M method were used to find the composition of both fuels. Results indicated that the content of naphtha, kerosene, diesel, and long residue were determined quantitatively and also identified the paraffin, naphthenes, and aromatic contents for both fuels. Naphtha and heavy oil contents of WPD were 9.2 and 8.9wt% higher than that of CD but kerosene and diesel contents were 0.7and 17.4wt% less than that of commercial diesel. After that, paraffin, naphthenes and aromatic contents of WPD from PNA analysis were 80.42, 14.54 and 5.04wt% and these hydrocarbon contents of CD were 60.61, 25.91 and 13.48wt% respectively. By knowing them, the appropriate method can be determined for fuel upgrading and interpret correctly of combustion and emissions results. © 2023 IEEE.

关键词： Paraffins

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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