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IMTRAC: Image-Based Modeling for Transformative Traffic control via Foundation Models

IFAC-PapersOnLine 2024年第10期58卷 225-230页

作者： Xingyuan Dai Yiqing Tang Chao Guo Yuanyuan Chen Yisheng Lv Fei-Yue Wang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 Shanghai Artificial Intelligence Laboratory Shanghai 200232

This paper introduces the Image-based Modeling for Transformative Traffic control (IMTRAC) framework, which revolutionizes urban traffic management by integrating image-based representations, foundation models, and dynamic signaling. It transitions from textual to visual data analysis, transforming complex traffic scenes into quantifiable insights. The image-based representation is then analyzed by foundation models to infer refined and effective traffic control policies. Our experimental results show marked improvements in traffic flow and reductions in waiting times, highlighting IMTRAC’s ability to outperform traditional control methods. The proposed IMTRAC promises a significant leap towards smarter, efficient urban traffic systems, leveraging the synergy of advanced imaging techniques and multi-modal foundation models to enhance control decisions.

关键词： Foundation models Intelligent transportation systems Traffic control

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DAG-BLOCK: A Novel Architecture for Scaling Blockchain-Enabled Cryptocurrencies

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第1期11卷 378-388页

作者： Qi, Naina Yuan, Yong Wang, Fei-Yue Renmin Univ China Sch Math Beijing Peoples R China Minist Educ Engn Res Ctr Finance Computat & Digital Engn Beijing 100872 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China

With the rapid development of blockchain technology and industries, scalability has been widely realized as one of the primary and urgent concerns for the large-scale adoption of blockchain, especially for cryptocurrencies. In this respect, directed acyclic graph (DAG) proves to be an elegant solution to scaling blockchain but suffers from weak consistency and security issues. In this article, we designed a novel DAG-BLOCK architecture for blockchain-enabled cryptocurrency markets in order to improve the scalability. In our work, DAG is used to replace the Merkel-tree-based transaction structure within the block, and a novel design of open blocks is proposed to enable user nodes to participate in verifying the transactions in blockchain systems. On this basis, we designed a new segmented market structure, in which each miner serves only a group of users instead of all users, so as to reduce miners' workload and thus scale transaction processing capabilities. Our work can help improve the scalability of cryptocurrencies via evolving the underlying blockchain systems to graph-based distributed ledgers and is expected to shed new light on designing blockchain-based decentralized markets.

关键词： Blockchain consensus directed acyclic graph (DAG) scalability

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Detecting and Tracking 6-DoF Motion of Unknown Dynamic Objects in Industrial Environments Using Stereo Visual Sensing

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第12期54卷 7558-7570页

作者： Cao, Hao Cheng, Li Shen, Zhipeng Huang, Chao Huang, Hailong Wang, Fei-Yue Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau Peoples R China

Despite recent advancements in robotic exploration, estimating the three-dimensional (3-D) motion of unknown dynamic objects, which are prevalent in chaotic construction sites, remains an unresolved challenge. In this work, we studied the problem of detecting and tracking the six-degree-of-freedom (6-DoF) motion of unknown moving objects using only a stereo camera. The fundamental idea of our approach is to estimate the positions of map points at each timestamp and model their uncertainty in position, such that the correlation between map points can be found by segmenting point clouds that are parts of moving objects into different groups. By analyzing the correlation between map points, we can detect the dynamic objects in the scene without making any assumptions about the type of objects. Thus, this approach enables tracking of both known and unknown moving objects, such as a robot carrier with stacked luggage. It surpasses the performance of existing appearance-based methods, which often face difficulties when dealing with unknown objects. Through extensive real-world experiments, we demonstrate the effectiveness of our approach in accurately tracking moving objects, highlighting its potential for various applications. In addition, we successfully deploy our object motion estimation algorithm in an unmanned ground vehicle (UGV) for the purpose of avoiding unknown moving objects in real-world scenarios. This practical implementation underscores the applicability of our approach in real-world settings.

关键词： Dynamics Motion estimation Cameras Robots Vehicle dynamics Object detection Accuracy Mapping motion estimation stereo tracking

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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

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An Immersive Learning System with Multimodal Cognitive Processes Inference

An Immersive Learning System with Multimodal Cognitive Proce...

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2022 Chinese Automation Congress, CAC 2022

作者： Bian, Siyu Liu, Xiwei Zhao, Hongxia Gong, Xiaoyan Jing, Sifeng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China

ISBN: (纸本)9781665465335

Using multimodal technology to infer learners' cognitive processes is an important means to support personalized learning. Previous cognitive process analysis is more about subjective reasoning, and less about combining physiological signals. With the rapid development of virtual reality technology, new requirements and more immersive schemes for learner's cognitive process inferring have been proposed. Based on this, this paper proposes an immersive learning system with multimodal cognitive processes inference, which is composed of macro, mesoscopic and micro environments. The system analyzes learner's actions, emotions and attention to infer learner's cognitive process through more comprehensive and objective multimodal data. The system also combines eye movement, brainwaves and other data, and proposes a multimodal data acquisition and analysis scheme using HMD-based VR. This paper also provides an immersive learning cognitive processes inference scheme for individual online learning, offline classroom learning scenarios. © 2022 IEEE.

关键词： Learning systems

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Research on intelligent methods in virtual reality games

Research on intelligent methods in virtual reality games

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2022 Chinese Automation Congress, CAC 2022

作者： Wang, JiaWei Liu, Xiwei Gong, Xiaoyan Chinese Academy of Sciences The State Key Laboratory for Management Control of Complex Systems Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

Introduction of virtual reality technology enriches the expression of traditional games and greatly improves the sense of substitution and interactive experience of users. Realization of virtual reality is inseparable from artificial intelligence technology. In order to improve immersion and interactivity of virtual reality games, it is necessary to propose more intelligent solutions to existing technologies. This paper aims to discuss some intelligent methods of virtual reality games. Firstly, it discusses artificial intelligence means to enhance the virtual reality experience, which can improve the tracking and force / tactile interaction system of existing virtual reality games from software and hardware aspects;Then it proposes the concept of human-machine hybrid enhanced intelligence: by introducing human cognitive model to improve perception and decision-making ability of virtual reality games, so as to make interaction of virtual reality games more friendly and more real. © 2022 IEEE.

关键词： Virtual reality

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System Dynamics-Based Analysis on Factors Influencing Comprehensive Competence of Primary and Secondary School Students

System Dynamics-Based Analysis on Factors Influencing Compre...

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2022 Chinese Automation Congress, CAC 2022

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

With rapid progress of artificial intelligence (AI), it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures;2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents. © 2022 IEEE.

关键词： Students

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Discover Trip Purposes From Cellular Network Data With Topic Modeling

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2022年第4期14卷 37-46页

作者： Zhao, Xueliang Li, Zhishuai Zhang, Yu Lv, Yisheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Beijing Municipal Inst City Planning & Design Beijing Peoples R China

The widespread use of mobile phones has generated a large amount of individual trajectory data. Such data can greatly help analyze and understand human daily travel behavior. In this paper, we use the topic modeling technique to infer trip purposes based on pseudonymized users' trajectory data from cellular network and points of interest (POIs) from online map services. The adapted latent Dirichlet allocation method is used to model the trip generation process and then infer trip purposes behind the data. The experiments are performed on a data set of 27,732 trip records in Beijing on weekdays. Ten topics are discovered. This method can easily infer different trip purposes based on three trip attributes, i.e., trip departure time, stay duration, and POI categories for destinations, and most of the topics/trip purposes are explainable.

关键词： Trajectory Cellular networks Nickel Electronic mail Data mining Probabilistic logic Finance

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A Lifetime management Framework for SOTIF-Oriented Vehicle Dynamics control: A Data-Driven Approach

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 48-51页

作者： Chen, Jicheng Liang, Jingguo Teng, Siyu Zhang, Hui Wang, Fei-Yue Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Hong Kong Baptist Univ Kowloon Hong Kong 999077 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

Current safety of the intended functionality (SOTIF) studies are focused on perception limitations, decision uncertainties, and human-machine interaction, while it tends to overlook the impact of the control system on SOTIF. This letter seeks to address this gap by establishing a lifetime management framework for vehicle dynamics control (VDC) systems. At first, we provide an introduction to the fundamental composition and control architecture of a VDC system. Furthermore, we identify representative hazard scenarios, trigger conditions and performance deficiencies in VDC. Building upon data-driven fault-tolerant control theory, a comprehensive framework consisting of adaptive filter, iterative learning, fault diagnosis and identification, and fault-tolerant control modules is presented. The proposed framework is specifically designed to address trigger conditions such as driving environment variations, integration of multiple control systems, performance degradation due to components aging and maintenance. The framework features online driving state supervision, driving risk measurement, and intervention for mitigation of the SOTIF issues. As a summary of a part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL), the letter reports the development of an intelligent vehicle lifetime management framework with a strong focus on SOTIF issues.

关键词： Safety control systems Vehicle dynamics Fault diagnosis Hazards Autonomous vehicles Uncertainty Sustainability safety of the intended functionality (SOTIF) intelligent vehicles adaptive observer data-driven FDI fault tolerant control

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Sora for Social Vision With Parallel Intelligence: Social Interaction in Intelligent Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4240-4243页

作者： Yu, Hui Liang, Wei Fan, Lili Wang, Yutong Wang, Fei-Yue Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ England Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100811 Peoples R China

Artificial technologies have made rapid progress and achieved various superior tasks in the past few years, including but not limited to classification, detection, image generation and data processing. Particularly, the very recent emerging Sora has demonstrated the exceptional ability of text-to-video generation lasting for 1 minute long with impressive quality. It provides a huge potential for many new applications across industries, especially social interaction in intelligent vehicles. The emergence of innovative intelligence vehicle applications has given rise to novel requirements for social and human-vehicle interaction within the associated contexts, where Sora and social vision could play an important role. In this perspective, we present a new Social Interaction framework based on Sora and parallel intelligence in intelligent vehicles and provide a novel perspective for conducting new social and human-vehicle interaction in the context of intelligent vehicles.

关键词： Intelligent vehicles Computational modeling Transformers Computer vision Visualization Human-vehicle systems Human computer interaction Sora parallel intelligence social vision social interaction intelligent Vehicles diffusion model human-machine interaction

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